One day towards the end of 2000 my wife finally lost her financial patience with me. As she threw a pile of bills and receipts at me she screamed "That's it! I'm never doing the accounts or paying the bills again. You never collect receipts, you never write a memo in the checkbook, and your work expenses are impossible to understand!" She then stormed out of the room. I was in no doubt that she meant it and she has remained free of the household accounting burden ever since. I unfortunately have not. It is true that until that day I had never balanced a checkbook in my life and habitually threw bank balances in the trash without even opening them - another contributing factor to my wife's rant. In fact the only time I ever knew my bank balance was when the ATM refused to dispense cash. So it was with great trepidation that I began my fiscally responsible life. I figured that as I had designed and built large financial software systems I ought to be able to use a small one. So after some research I selected Quicken as they had a large share of the market and had a version for Mac.

In bringing our accounts under control I have come to realize that complete and accurate financial information is a powerful thing. We recently bought a house, and were able to calculate exactly what we could afford based on four years worth of data. Without Quicken we would have been guessing. Looking back over the past four years using Quicken I can now see that I have gone through several distinct phases. These phases are parts of an unconscious optimization process aimed at reducing the overall cost and effort involved in managing our finances. I did not plan this process it just became obvious once I had access to complete and accurate data. The result is that the banks get less of my money and I spend less time managing it. From the banks perspective financial management software like Quicken is a "bad thing" because customers who use it are less profitable. The following sections describe the optimization process that emerged and some of the pitfalls I encountered.

Stage 1 Account Setup

It took about three months to get everything setup. First I had to find out what accounts we had - quite a few as it turned out. As I discovered each account I set it up in Quicken. The actual setup was trivial but finding all the required information and getting my account "activated" so I could access it through Quicken took many phone calls to the customer support desks of each financial institution. Getting technical support from a bank is like getting medical advice from a hairstylist - it lacks authority and can be completely misleading. Sometimes, it was not until I had been passed through several customer service representatives that I finally heard "Oh ! we don't support that feature for Quicken on a Mac!" Once the account is setup it takes at least one billing cycle to cross check the paper statement with the Quicken managed data to make sure they reconcile.

Quicken for Mac provides four levels of support for maintaining an account. These levels provide increasing control for the user and decreasing control for the bank. Most of the tradeoffs I made were to do with the cost and level of service I would accept from an institution for the benefits I would get. The four levels of support are described below.

Manual maintenance

This is the worst solution. If your account has more than a few transactions a month entering them by hand will be a major headache. This is the only solution for financial institutions that provide no support at all for Quicken. I avoid these institutions if I possibly can.

Web connect

This is the basic level of support provided by most financial institutions. It requires a visit their website once a month to download a file containing all the transactions which must then be imported into the correct account in Quicken. You can get data more frequently but you have to remember exactly what dates you have already downloaded since you can get duplicate transactions if you are not careful. This approach is ok if you have a limited number of accounts but rapidly becomes a pain if you have more than a few accounts.

Direct connect

This is the best approach if you need to monitor the account on a daily basis. Transactions are downloaded whenever you request and are automatically loaded into Quicken. I have this option for all my bank and credit card accounts. I will not open a new account of this type unless I can get this level of service. I pay $3 a month to my current bank for this service. But my credit cards provide it free. It is interesting that credit card companies will provide this service free but banks will not. I believe the banks fee for this is a deterrent intended to drive customers to use the banks web based service instead.

Direct connect with online bill payment

This provides all the benefits of direct connect but also allows payments to be made through Quicken. This feature means I hardly use checks at all anymore and I can schedule payments to be made at any date in the future. I pay my current Bank $6.95 a month for this service. Again I believe this fee is a deterrent and a way to make up for lost income as I no longer use checks. Paying this fee is a compromise, as well as online banking I want access to a large network of ATMs. I can avoid out of network ATM transactions and do online bill payment through Quicken.

Stage 2 - Categorization

Once my accounts were setup I started to classify transactions. Initially I used Quicken's default categories but over time I started developing my own categories tailored to my needs. This meant I started to monitor the things that mattered to me. After a while I began to see patterns of spending where we could easily make savings. These patterns were only visible because of the customized categorization scheme.

Stage 3 - Cost Reduction

One of the obvious costs that could be reduced was banking fees. Firstly we decided to only get cash from our Bank's ATMs. Those $2:00 fees for using other banks ATMs add up quickly. Then there was the $10 fee for automatic transfers from the savings account to the checking account at the end of the month. With the forward view available in Quicken I can anticipate these events and transfer money to avoid any fees. Cumulatively these savings easily cover the cost of the yearly software upgrade.

Stage 4 - Account Consolidation

The next big realization was that we had too many accounts. By consolidating accounts we could further reduce banking charges and the effort required to manage them. For example one savings account was under the threshold balance for free banking. As a result we were being charged once a quarter for the privilege of leaving our money in the account. So I consolidated our savings in a few accounts. This put the account in question over the threshold and stopped the charges.

Stage 5 - Increasing Ease of Use

I have now reached the stage where the main driver for my financial decisions is the ease with which I can integrate new financial institutions into my system. There is enough choice out there that if an institution will not support my computing needs then I will take my business elsewhere. Even my primary checking account can be moved if necessary. I don't rely on my bank for anything other than acting as the interface between my software and the rest of the financial world. In short they have become a utility provider. The telephone companies provide dial tone and my bank provides the financial equivalent and that's it. I believe this is why banks make it so difficult to integrate their systems with personal financial software and why they try so hard to get you to use their proprietary online banking systems. They want to lock our data into thier systems and prevent their services becoming commoditized.

Quicken is without doubt the most valuable piece of software I use and has returned the small investment I made in it many times over. It is also a valuable weapon for managing the sharks of the personal finace world.

]]> 2004-07-29T07:31:03Z 2004-07-28T23:24:58-08:00 tag:www.virtualtravelog.net,2004://2.63 2004-07-29T07:24:58Z

I just re-wrote the first four sections of the Wikipedia article for the term Computer. The Current page is here. This link to the change history page for the article currently shows one change on line 6. This was a...

John http://www.virtualtravelog.net/ foobar@bigfoot.com Technology I just re-wrote the first four sections of the Wikipedia article for the term Computer. The Current page is here.

This link to the change history page for the article currently shows one change on line 6. This was a trivial typo that I just fixed in my final version. Over time this link should change to show all the modifications to the article made by other people. I'm curious to see how signifcant the changes will be...

]]> 2004-07-16T18:21:43Z 2004-05-11T19:58:29-08:00 tag:www.virtualtravelog.net,2004://2.62 2004-05-12T03:58:29Z

Explaining unprecedented phenomena can lead to a cascade of new findings and theories. However, it is hard to handle unique evidence with scientific rigor. Sadly only a few scientists are prepared to risk damaging their careers by tackling such issues.

John http://www.virtualtravelog.net/ foobar@bigfoot.com Complexity When Stephen Hawking said "The only thing nature abhors more than a vacuum is a naked singularity". He was talking specifically about the laws of physics in relation to black holes. But his observation could equally apply to the body of human knowledge and the existence of unprecedented phenomena. The only thing that drives our desire for knowledge more than a complete absence of information is the presence of a single, undeniable but unprecedented piece of evidence. Such tantalizing evidence demands explanation.

]]> Unique evidence is hard to handle precisely because it is unparalleled. Science relies on repeatable experiments that can be designed to test hypotheses. Part of the challenge with understanding unprecedented phenomena is learning why they are without parallel in the first place. Are they in some way special, or just rare and the first to be discovered?

Unprecedented phenomena do not fit easily into the general framework of science. They create a dilemma: Should existing theory be modified to handle a single piece of evidence? Or can the phenomenon be treated as a special exception? Or is a completely new theory required to explain the evidence?

Some of the greatest problems in science involve the explanation of unprecedented phenomena: The presence of life on earth but nowhere else in the solar system, the lack of a rigorous explanation for the existence of human consciousness and the problem of why the Universe goes to the trouble of existing at all. No one is quite certain if these phenomena are unique and if they are what their uniqueness might mean. One thing is certain - if we ever solve any of these problems the solution will have many consequences.

Understanding unprecedented phenomena like those described above is one of the best ways to rapidly expand the breadth of our knowledge. Such an understanding leads to a cascade of other dependent findings. The problems described above raise big questions that often attract religious explanations and more than their fair share of crackpots. In an effort to avoid theological arguments I was trying to think of a case which does not deal with such big issues. Then I remembered the Oklo Fossil Reactors.

The Oklo Fossil Reactors

I first heard about The Oklo Fossil Reactors in the late 1980s while I was working at the British Geological Survey. A colleague of mine was developing calibration procedures for devices used to measure radioactivity. He was explaining various methods of calibration and mentioned that measuring the U-238 U-235 ratio was one way to calibrate a device since the ratio was well known and was constant throughout all natural sources in the solar system U-238 (99.27%) U-235 (0.72%). Except, he said, in the case of the Uranium ore from Oklo mine in Gabon, Africa. He then explain how in March 1972 some scientists at the Pierrelatte Uranium enrichment plant in France were conducting mass spectrometry tests and noticed that the U-238, U-235 ratio in their samples was not what it should be. These measurements caused considerable consternation and after dismissing all the obvious causes; miss-calibration, contamination etc, they traced the exceptional samples back to Africa. At first they suspected that someone had exploded a nuclear bomb in Africa, without anyone noticing, as the only way to change the U-238, U-235 ratio is by nuclear reaction. This theory was dismissed when the unusual readings were found to localized to ore from the Oklo mine. It gradually became apparent that a natural nuclear fission reaction had occurred at various sites within the ore body and that these reactions had been responsible for changing the U-238 U-235 ratio. At least that's how I remember the story.

One of the Oklo Fossil Reactors exposed by mining operations

Zone 15 is one of the remaining Oklo Fossil Reactors, it is accessible through a tunnel from the main mine workings

The true Oklo detective story is considerably more elaborate. The main reason it was so difficult to make the leap to assuming a natural nuclear reaction is that today it is no longer possible.

When the earth was formed natural uranium consisted of about 17% U-235 . However, U-238 has a half life of approximately 4.5 billion years and U-235 has a half life of only 700 million years. Hence U-238 now accounts for 99.27% of all Uranium and U-235 for only 0.72 % (there are trace amounts of other isotopes). In today's proportions U-235 can only support a nuclear reaction in the presence of a heavy water moderator. So it can not react naturally since heavy water does not occur naturally. But, and this is the leap the scientists made, 1.7 billion years ago U-235 accounted for nearly 3% of uranium and in that quantity it could have been moderated by ordinary water.

The Uranium ore body at Oklo is several kilometers long and contained pockets of highly enriched (up to 70% pure) uranium as UO2. The ore was buried underground and in places was thick enough to create a critical mass of uranium. Water percolating down from the surface through cracks in the rock was present in sufficient quantity to moderate the reaction, and finally there were no natural poisons like boron that could prevent the reaction. The reaction generated heat, which turned the water to steam, which escaped from the reactor and reduced the amount of water , which reduced the intensity of the reaction, until the rocks cooled enough for the water to return - and so on - until the reactor reached a state of equilibrium. This occurred in 15 separate places within the ore body and continued for hundreds of thousands of years. In all approximately 10 tons of Uranium ore was depleted in this way.

Only one other natural reactor has been found 30 miles from Oklo.

One of the most fascinating consequences of the Oklo Fossil Reactors discovery is the chain of follow-on hypotheses, discoveries, and implications that has spread into different fields of research. Some of these discoveries are hard science and some highly speculative hypotheses, while others are so extreme as to be worthy of note only because of their lunacy. The uniqueness of the Oklo phenomenon has provided a rich set of precedents that have fueled many claims.

Nuclear Waste Disposal

One of the most significant short term consequences of the Oklo fossil reactors are for the disposal and storage of nuclear waste. It is difficult to imagine a more unsuitable place to dispose of nuclear fission by-products than the environment at Oklo - buried with no containment vessel in an underground stream. And yet, remarkably, most of the fission by-products at Oklo have remained exactly where they were produced or have moved only a few meters in 1.7 billion years. This containment has been achieved without any of the elaborate precautions being designed for nuclear waste repositories like Yukka Mountain.

The periodic table showing the elements produced by the Oklo fossil reactors and the degree to which they were retained at the reactor site.

The Office of Civilian Radioactive Waste Management behind the Yukka Mountain Project use the Oklo Fossil Reactors as a precident for long term nuclear waste mobility. Their argument - in the only natural example ever discovered buried nuclear waste was safely contained, without man-made precautions, in a far worse environment than Yukka mountain.

Cosmological Constants

It is generally assumed that the laws of nature are the same everywhere in the universe and have been so ever since the universe began. This fundamental assumption enables us to explain events billions of light years away and make predictions about the behavior of the early universe. But, if it is not true then many of our predictions about the universe could be called into question. Studies of light from distant quasars suggest the fine-structure constant, a number that determines the strength of interactions between charged particles and electromagnetic fields, could have changed over the history of the universe. If the fine-structure constant has changed, then other constants might have changed as well, and this could have major consequences for our understanding of the universe. Peter Moller, a Los Alamos physicist collaborated on the paper Nuclear data in Oklo and Time-Variability of Fundamental Coupling Constants . This paper used evidence of unusually low samarium-149 at Oklo to conclude that the fine-structure constant has not changed significantly between the time the fossil reactors were active and the present day. Only the existence of the Oklo reactor allowed this measurement to be made.

Planetary Magnetic Fields

Since 1990 J. Marvin Herndon, Ph.D Has pursued the idea that some planets in our solar system may have planetary scale nuclear reactors at their cores. In the 1960s astronomers discovered that the planet Jupiter radiates about twice as much energy as it absorbs from the sun. Hendon believed the existing explanations for this phenomenon (gravity) were inadequate and suggested a natural nuclear reactor as the source. He cites Oklo as evidence that his theories are possible and has also suggested that the earth has a similar reactor at its core. By Herndon's reckoning the earth's reactor is 5 miles in diameter and is responsible for the earth's magnetic field. He suggests that the natural shutting down and restarting of the reactor explain the frequent reversals in the Earth's magnetic field and that if the reactor should ever use up all its fuel it would shutdown the earths magnetic field for good, with disastrous consequences for life on earth. [ Deep-Earth Reactor: Nuclear Fission, helium, and the geomagnetic field. D. F. Hollenbach and J. M. Herndon pdf ]

Herndon's theories have not been well received by other geophysicists. But, before he is completely dismissed it should be remembered that the more accepted theories are equally strange and it took geologists 50 years to accept the theory of continental drift.

Current Biography Profile of J. Marvin Herndon

But Herndon found that instead of being the subject of discussion and debate, his work was systematically ignored. His grants were no longer renewed, and without that support, his position at the University of California at San Diego was eliminated.

Current Biography. November 2003

The Age of the Earth

Several people have suggested that the Oklo Fossil Reactors are hard evidence against the Creationist idea that the earth may only be a few thousand years old. Some defenders of creationism have actually tried to argue that scripture can be interpreted to account for the evidence of the Oklo Fossil Reactors. In the quote below C. L. Webster of the Geoscience Research Institute ("Integrating Science and Faith") presents his desperate closing arguments and concludes that in the case of Oklo "We need to seek a better understanding ... through the guidance of the Holy Spirit." It appears that the only way for Christian Holy Scripture to account for the Oklo Fossil Reactors is with the support of a direct appeal to God through prayer! A cyclical argument if ever their was one!

A question that now arises in the face of these strong data supporting long ages for the existence of abiotic matter is, "Can these data be accepted within the Scriptural framework of a literal seven-day creation as described in Genesis?" I personally believe that the answer is "Yes!"

One of the immediate consequences of accepting these long ages for the abiotic material of the earth is the assumption that this matter existed on planet Earth before the creation of life. This assumption is supported by interpreting Genesis 1:1-2 as identifying God as the Creator of the "foundations" of the Earth, regardless of when that creation process took place. The creation of life and living processes, as we know them, begins with verse 3 of Genesis 1. In addition, one can add the fact that there is no specific reference in the scriptural account of Creation week that addresses the creation of water or the mineral components of dry land ("earth" that was created on day three). The only reference made to their creation is "in the beginning." It seems possible then that the elementary abiotic matter is not bound by the limited age associated with living matter.

The implications of this approach would suggest that the radiometric clocks are not reset to zero whenever the minerals are transported by igneous or erosional processes. This approach also strongly suggests that the radiometric age assigned to the inorganic minerals associated with a fossil is more a reflection of the characteristics of the source of this inorganic material than an indication of the age of the fossil.

Conflicts between scientific and biblical interpretations are minimized with these assumptions. However, not all of the questions are answered, and areas that call for the exercise of faith remain.

In seeking to harmonize the revelation of God through Scripture and natural science, we must find a model that is consistent with both sources of revelation. Where such consistency is not found, we need to seek a better understanding of both sources through the guidance of the Holy Spirit.

[The Implications of the Oklo Phenomenon on the Constancy of Radiometric Decay C. L. Webster Geoscience Research Institute]

Ancient Civilizations

In the twilight zone of fringe religions and pseudo science it has even been suggested that the Oklo reactors were created by an ancient, non-human, race and all that remains of this race is their nuclear waste. This claim seems to have been floating around since the 1970's. Below is a quote from a Falun Dafam website.

As a matter of fact, many people today know that the reactor is a relic from a prehistoric civilization. It's probable that two billion years ago there was a fairly advanced civilization living at a place now called Oklo. This civilization was technologically superior to today's civilization. Compared to this huge "natural" nuclear reactor, our current nuclear reactors are far less impressive.

[ Prehistoric Nuclear Reactor In Gabon Republic from Pure Insight ]

It's difficult to know what to say about such wild claims. At least C. L. Webster had the honesty to admit Christian Scripture could not account for the Oklo Fossil Reactors without a good dose of faith. Falun Dafam on the other hand appears not to be concerned with intellectual honesty!

Conclusion

Explaining unprecedented phenomena can lead to a cascade of new findings and theories spreading into many areas of human knowledge. These findings can have significant consequences as they ripple through other areas of research. However, it is hard to handle unique phenomena with scientific rigor precisely because they have no parallel with which they can be compared. As a result they are open to wild interpretation by those with a tenuous grasp on reality. It would be a shame if the wildly irrational were able to dissuade the strictly logical from making significant advances. But one gets the impression that only a few brave , confident, or foolhardy scientists are prepared to tackle such unprecedented phenomena because they fear the damage being associated with the lunatics would do to their careers.

]]> 2004-05-07T18:37:44Z 2004-03-30T23:03:36-08:00 tag:www.virtualtravelog.net,2004://2.61 2004-03-31T07:03:36Z

In 1936, [Howard] Aiken had proposed his idea [to build a giant calculating machine] to the [Harvard University] Physics Department, ... He was told by the chairman, Frederick Saunders, that a lab technician, Carmelo Lanza, had told him about...

John http://www.virtualtravelog.net/ foobar@bigfoot.com Computer History

In 1936, [Howard] Aiken had proposed his idea [to build a giant calculating machine] to the [Harvard University] Physics Department, ... He was told by the chairman, Frederick Saunders, that a lab technician, Carmelo Lanza, had told him about a similar contraption already stored up in the Science Center attic.

Intrigued, Aiken had Lanza lead him to the machine, which turned out to be a set of brass wheels from English mathematician and philosopher Charles Babbage's unfinished "analytical engine" from nearly 100 years earlier.

Aiken immediately recognized that he and Babbage had the same mechanism in mind. Fortunately for Aiken, where lack of money and poor materials had left Babbage's dream incomplete, he would have much more success.

Later, those brass wheels, along with a set of books that had been given to him by the grandson of Babbage, would occupy a prominent spot in Aiken's office. In an interview with I. Bernard Cohen '37, PhD '47, Victor S. Thomas Professor of the History of Science Emeritus, Aiken pointed to Babbage's books and said, "There's my education in computers, right there; this is the whole thing, everything I took out of a book."

[The Harvard University Gazette. Howard Aiken: Makin' a Computer Wonder By Cassie Furguson]

]]> The quote is incorrect. The "brass wheels" were a small demonstration piece for the Difference Engine I not the Analytical Engine. They were one of six such pieces constructed by Babbage's son Henry after his fathers death. These demonstration pieces were distributed among various universities including Harvard. Aiken must have been sufficiently intrigued by the mechanism to investigate Babbage. In the course of this investigation he would have discovered Babbage's Analytical Engine and the similarities it bore to his own machine. It is not clear when Aiken was given Babbage's "books" or indeed what they contained. They did not contain plans of the Analytical Engine since the only plans have always been stored at the Science Museum at Kensington in London. Aiken may have been able to obtain some of the following documents which together comprise the complete published account of the Analytical Engine

• Sketch of The Analytical Engine Invented by Charles Babbage. By L. F. Menabrea of Turin, Officer of the Military Engineers from the Bibliotheque Universelle de Geneve, October, 1842, No. 82. With notes upon the Memoir by the Translator Ada Augusta, Countess of Lovelace.
• Passages from the Life of a Philosopher. Chapter VIII. Of the Analytical Engine by Charles Babbage. 1864
• Report of the Committee, consisting of Professor Cayley, Dr. Farr, Mr. J. W. L. Glaisher, Dr. Pole, Professor Fuller, Professor A. B. W. Kennedy, Professor Clifford, and Mr. C. W. Merrfield, appointed to consider the advisability and to estimate the expense of constructing Mr. Babbage's Analytical Machine, and of printing Tables by its means. Drawn up by Mr. Merrifield. 1878
• The Analytical Engine, paper by Major-General Henry P. Babbage (Charles Babbage's son), read at Bath on September 12th, 1888; published in the Proceedings of the British Association, 1888.
• Babbage's Analytical Engine. By Major-General H. P. Babbage. 1910 April 8. From the Monthly Notices of the Royal Astronomical Society 70, 517-526, 645 [Errata] (1910).

[ via The Foumilab Analytical Engine website the best website concerning the Analytical Engine]

These documents along with Babbage's "books" would have given Aiken a high level description of Babbage's planned machine.

The pictures below show front and rear views of one of six demonstration pieces for the Difference Engine I created by Henry Babbage after his Fathers death. This piece is similar to the one shown to Howard Aiken in 1936.

Aiken may also have seen photographs of the largest test piece of the Difference Engine I held by the Science Museum, Kensington, London, UK.

Two large fragments of the Analytical Engine were constructed by Babbage's son and Aiken may have seen photographs or otherwise become aware of their existence.

In 1991 the Science Museum in London constructed the Difference Engine II, the printer was added in 2001. These pieces are on display in the Museum, which is well worth a visit. The construction of the Difference Engine II is documented by Doron Swade in his book The Difference Engine Charles Babbage and the quest to build the first computer. The Difference Engine II was the last machine Babbage designed and employs lessons he learned from both the Difference Engine I and the Analytical Engine. For example the printer was designed for use by the Analytical Engine and Babbage reused it for the Difference Engine II. The similarity between the Difference Engine II and the machine that Aiken built is striking. Note the drive shaft running along the bottom of both machines and the general arrangement with printers at one end of a long tall frame. This may be the result of convergent evolution rather than direct influence but the similarity is still striking.

In the foreword to the manual for the operation of the Automatic Sequence Controlled Calculator (ASCC) Howard Aiken states that "The appendices were prepared by Lieutenant [Grace] Hopper" with the assistance of others and that "[She] acted as general editor, and more than any other person is responsible for the book." It seems safe to conclude that Howard Aiken and Grace Hopper were not only influenced by Charles Babbage but they and their team held him in high regard and considered themselves guardians of his reputation and inheritors of his quest.

Chapter 1. Historical Introduction

"If, unwarned by my example, any man shall undertake and shall succeed in really constructing an engine embodying in itself the whole of the executive department of mathematical analysis upon different principles or by simpler mechanical means, I have no fear of leaving my reputation in his charge, for he alone will be fully able to appreciate the nature of my efforts and the value of their results."

Charles Babbage The Life of a Philosopher (1864)

[The Manual of Operation for the Automatic Sequence Controlled Calculator. By the staff of the Computation Laboratory with a forward by James Bryant Conant. Cambridge, Massachusetts. Harvard University Press. 1946.]

The staff of the Computation Laboratory at the time the ASCC manual was published in 1946 are listed below. They went on to have a considerable influence on the development of the modern computer. Not least of which was Grace Hooper who developed the first compiler and several popular languages.

• Comdr. Howard H. Aiken USNR. Officer in Charge
• Lt. Comdr. Hubert A. Arnold, USNR
• Lt. Harry E. Goheen, USNR
• Lt. Grace M. Hopper, USNR
• Lt(jg) Richard M. Bloch, USNR
• Lt(jg) Robert V. D. Campbell, USNR

• Lt(jg) Brooks J. Lockhart. USNR
• Ens. Ruth A. Brendel, USNR

• William A. Porter, CEM
• Frank L. Verdonck, YI/c
• Delo A. Calvin, Sp(I)I/c
• Hubert M. Livingston, Sp(I)I/c
• John F. Mahoney, Sp(I)I/c
• Durward R. White, Sp(I)I/c
• Geary W. Huntsberger, MMS2/c
• John M. Hourihan, MMS3/c

• Kenneth C. Hanna
• Joseph O. Harrison, Jr.
• Robert L. Hawkins
• Ruth G. Knowlton
• Eunice H. MacMasters
• Frederick G. Miller
• John W. Roche
• Robert E. Wilkins

The influence of both, Howard Aiken, and the IBM ASCC - Harvard Mk I machine, on the later development of computers should not be over stated. The published notes from The Moore School Lectures (held in 1946) are rather scathing with respect to Aiken and his understanding of the direction in which the new electronic computing machines would lead.

Hartree was very forward looking and was excited by the mathematical potential of the stored program computer. On the other hand, Aiken was absorbed in his own way of doing things and does not appear to have been aware of the significance of the new electronic machines.

[ The Moore School Lectures (Charles Babbage Institute Reprint)]

Unlike Aiken and his machine, Grace Hopper and some of her colleagues went on to have a significant influence in the early development of compilers and language design. One wonders what if any influence Babbage and Ada Lovelace had on Grace Hopper's ideas. Unfortunately I can find no comments by Hopper regarding either Babbage or Lovelace.

[ Pictures via The Science and Society Picture Library]]]> 2004-03-28T20:18:36Z 2004-03-04T19:53:43-08:00 tag:www.virtualtravelog.net,2004://2.60 2004-03-05T03:53:43Z

Ontologies are like Khunian Paradigms; new ontologies are resisted by those who have a vested interest in the old system. Ontologies, like jargon, can form barriers to market entry that effectively exclude potential competitors and protect established players. Establised players are acting out of self interest in resisting change.

John http://www.virtualtravelog.net/ foobar@bigfoot.com System Design The International System of Units (SI) [72 page pdf Brochure] is maintained by the Bureau International des Poids et Measures at it's headquarters in Sevres near Paris, France. The Metric System as it is often known has a long history; supposedly invented in 1670 by Gabriel Mouton, a French clergyman, It was adopted by France in 1795 and by the United States in 1866. The system gained international status with the signing of The Convention of the Meter in Paris on 20th May 1875. The U.S. was one of the original seventeen signatory nations and is the only industrialized nation that still does not use the system.

]]>

Note: At this time, only three countries - Burma, Liberia, and the US - have not adopted the International System of Units (SI, or metric system) as their official system of weights and measures. Although use of the metric system has been sanctioned by law in the US since 1866, it has been slow in displacing the American adaptation of the British Imperial System known as the U.S. Customary System. The US is the only industrialized nation that does not mainly use the metric system in its commercial and standards activities, but there is increasing acceptance in science, medicine, government, and many sectors of industry.

CIA World Fact Book. 2000

In 1971 the U.S. Metric Study by the National Bureau of Standards resulted in a Report to the Congress called "A Metric America, A Decision Whose Time Has Come." The report recommended that the U.S. should switch to the metric system deliberately and carefully through a coordinated national program, and establish a target date 10 years ahead. In 1992 NIST the National Institute of Sciences (The Successor to the National Bureau of Standards) published a report titled A Metric America: A Decision Whose Time Has Come - For Real (NISTIR 4858) June 1992. The US has spent over a century trying to switch to the metric system through a process of voluntary adoption.

The SI is without question more rational that the US Customary System and the British Imperial System on which the US system was based. But it has failed to gain acceptance in the US and its adoption in many other countries has been painfully slow. This is a classic example of the influence of network effects resulting in lock-in. But it is also an example of economic protectionism masquerading as defence of cultural heritage and patriotism. The adoption problems of the SI are not unique. When an established ontology is challenged by a newer ontology there will be resistance to the new ontology no matter how good it is. Ontologies are like Khunian Paradigms; new ontologies are resisted by those who have a vested interest in the old system. Ontologies, like jargon, can form barriers to market entry that effectively exclude potential competitors and protect established players. The benefits provided by these barriers can outweigh the benefits of adopting the new ontology. In such cases voluntary adoption will not occur. Establised players are acting out of self interest in resisting change.

Effective conversion from an old established ontology to a new replacement ontology requires more than encouragement it must be accompanied by coercion. In the UK enforcement of the metric system is proceeding through government mandated use in; the education system, the military, all government contracts and for all commerce. Government departments are also aggressively pursuing offenders like Tesco through the courts. This policy has generated a backlash. Groups like the BWMA are mounting popular resistance campaigns aimed at preventing change.

Building a great ontology is only the first step. Getting people to adopt it is far more challenging. Adoption is not driven by the merits of the new ontology alone. Enforcement is often required. The US will not become metric until congress is prepared to enact enforceable laws that mandate the use of the metric system.

]]> 2004-07-26T19:48:32Z 2004-02-11T14:17:28-08:00 tag:www.virtualtravelog.net,2004://2.59 2004-02-11T22:17:28Z

If anyone should ever have been capable of predicting the future it was Vannevar Bush in 1945. Unlike almost anyone else before or since Bush was actually in possession of ALL the facts - as only the head of technology research in a country at war could be...

John http://www.virtualtravelog.net/ foobar@bigfoot.com Computer History Today Vannevar Bush (rhymes with achiever) is often remembered for his July 1945 Atlantic Monthly article As We May Think in which he describes a hypothetical machine called a Memex. This machine contained a large indexed store of information and allowed a user to navigate through the store using a system similar to hypertext links. At the time of writing his essay Bush knew more about the state of technology development in the US than almost any other person. During the war, he was Roosevelt's chief adviser on military research. He was responsible for many war time research projects including Radar, the Atomic Bomb, and the development of early Computers. If anyone should ever have been capable of predicting the future it was Vannevar Bush in 1945. He is an almost unprecedented test case for the art of prediction. Unlike almost anyone else before or since Bush was actually in possession of ALL the facts - as only the head of technology research in a country at war could be.

]]> The Editor of the Atlantic Monthly introduced the article as follows:

As Director of the Office of Scientific Research and Development, Dr. Vannevar Bush has coordinated the activities of some six thousand leading American scientists in the application of science to warfare. In this significant article he holds up an incentive for scientists when the fighting has ceased. He urges that men of science should then turn to the massive task of making more accessible our bewildering store of knowledge. For years inventions have extended man's physical powers rather than the powers of his mind. Trip hammers that multiply the fists, microscopes that sharpen the eye, and engines of destruction and detection are new results, but not the end results, of modern science. Now, says Dr. Bush, instruments are at hand which, if properly developed, will give man access to and command over the inherited knowledge of the ages. The perfection of these pacific instruments should be the first objective of our scientists as they emerge from their war work. Like Emerson's famous address of 1837 on "The American Scholar," this paper by Dr. Bush calls for a new relationship between thinking man and the sum of our knowledge. -THE EDITOR

The essay was prescient in many respects. However, it failed to anticipate several innovations that are fundamental to modern information management and made many predictions that are only partially correct. It is easy to ignore Bush's off-target predictions and focus solely on what he got right, but this would be a waste of an opportunity. By examining the innovations Bush failed to anticipate and the predictions he got half-right, and even wrong, we can develop a better understanding of prediction itself.

Background

Before the War Bush had been involved in the design and construction of analog computers for many years. At MIT He led colleagues and students in the development a series of analog machines that could solve differential equations. In 1927 Bush and others started developing the Integraph - a machine capable of solving first order differential equations. This was followed by the Bush Hazen Differential Analyzer, a general purpose equation solver that could solve 6th order differential equations. The Bush Hazen machine was operational at MIT in 1932 and served as the prototype for many similar machines built elsewhere. Finally in December 1941 the Rockerfeller Differential Analyzer (RDA) became operational at MIT. Financed by the Rockefeller Foundation, this machine used vacuum tubes and relays. It weighed 100 tons and was immediately classified. It spent the war calculating artillery tables. By the Wars end the RDA was redundant having been superceded by totally electronic machines like the ENIAC.

As Director of the Office of Scientific Research and Development Bush was Roosevelt's chief adviser on military research. He was an engineer, an expert administrator, a capable politician, and was not afraid of fight. He allocated funds and managed priorities for many of the major US funded research projects of the Second World War. At the end of the war when he wrote the essay he knew many secrets.

Veiled Secrets not Predictions

Vannear Bush's paper was published at the dawn of the digital age in July 1945. Many of the "predictions" it contained were merely veiled descriptions of secret wartime developments that had yet to be declassified. When Bush wrote his essay the great electronic computers that had been developed to aid the war effort were still secret. The ENIAC was the first of these machines to be publicly announced by the New York Times on February16th, 1946. Bush undoubtedly knew of ENIAC and other machines under development. The following quote from the essay is stated as a prediction but is actually a fairly accurate description of the ENIAC.

Moreover, they [computers] will be far more versatile than present commercial machines [punch card tabulators and hand calculators], so that they may readily be adapted for a wide variety of operations. They will be controlled by a control card or film, they will select their own data and manipulate it in accordance with the instructions thus inserted, they will perform complex arithmetical computations at exceedingly high speeds, and they will record results in such form as to be readily available for distribution or for later further manipulation. Such machines will have enormous appetites. One of them will take instructions and data from a whole roomful of girls armed with simple key board punches, and will deliver sheets of computed results every few minutes. There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.

The first atomic bomb was detonated at the Trinity site in New Mexico on July 16, 1945. a few weeks after the essay was published. Bush is said to have had a nervous collapse after witnessing the test detonation. It's success must have been a tremendous relief for Bush who had persuaded the President to commit the $2 Billion necessary to build the bomb. The following paragraph describing the impact of the war on scientific research, especially physics, seems to refer to the massive Manhattan Project and all the physicists involved.

It is the physicists who have been thrown most violently off stride, who have left academic pursuits for the making of strange destructive gadgets, who have had to devise new methods for their unanticipated assignments. They have done their part on the devices that made it possible to turn back the enemy, have worked in combined effort with the physicists of our allies. They have felt within themselves the stir of achievement. They have been part of a great team. Now, as peace approaches, one asks where they will find objectives worthy of their best.

Predictions

Bush starts his visionary predictions by suggesting that computers could be made to manipulate premises in than same way they manipulate numbers.

It is readily possible to construct a machine which will manipulate premises in accordance with formal logic, simply by the clever use of relay circuits. Put a set of premises into such a device and turn the crank, and it will readily pass out conclusion after conclusion, all in accordance with logical law, and with no more slips than would be expected of a keyboard adding machine.

He then describes the Memex as a personal desktop interactive device. However it is here that his foresight breaks down because the Memex it described as analog not digital. While it contained some computing components information was stored photographically on microfilm and retrieved electro-mechanically. The Memex was nothing like the room sized computers of the late 1940's. In the 1946 New York Times article announcing the ENIAC the new computer was described as "an amazing machine which applies electronic speeds for the first time to mathematical tasks hitherto too difficult and cumbersome for solution." It took a long time before people began to implement Bush's suggestion that computers could manipulate premises as well as numbers. Alan Turing had understood that computers were manipulators of symbols and that those symbols could represent any concept. But this knowledge was tightly bound to his work on code breaking and he in turn was bound by secrecy not to discuss it.

Ultimately Bush's prescience was limited by two factors: Failure to anticipate the emergence of fundamentally new technologies, and failure to predict the exponential improvements in many areas that such inventions would support.

The Relay gave way to the Thermionic Value which in turn gave way to the Transistor which itself was replaced by the Silicon Chip. Each paradigm shift maintained the exponential rate of growth in computing power. Bush could not have predicted this chain of technological advances. But as Moore has shown the exponential growth it has produced is predictable.

In 1945 the ENIAC could not even store its own meager program in what little memory it had and all data was stored externally. The idea of storing vast quantities of data digitally was not considered realistic, it was accepted that there had to be some form of external physical storage. Bush merely replaced the punched card with a microfilm. But memory storage advanced in a similar way to computing power, from mercury delay lines, and magnetic drums to William's tubes, magnetic core memory and tape, to modern chip based RAM, and high speed disc drives. Today a standard home computer is typically shipped with over a 100 Gigabytes of storage and several hundred Megabytes of memory.

Bush's biggest failings were in predicting implementation details and his most accurate predictions concerned the interaction of people and technology. The Memex is eerily similar to a networked PC running a web browser. Even Bushes description of the wearable camera is remarkably close. We don't wear our cameras because they double as portable phones but everything else about them from their size to the number of photos they can take is remarkably accurate.

Conclusions

Reading this essay with hind sight, and knowing that large amounts of information were still secret in July 1945, one is forced to wonder who was the intended audience of the essay. The essay seems to be an inverse call to arms, aimed at the scientists and researchers who would have recognized their own secret war time work between Bush's lines. In effect Bush was suggesting a path for post war research and development based on his uniquely broad knowledge of the state of technology. The Memex was a technological phoenix to be built from the ashes of wartime science. It was an example of how various wartime advances could be combined to create something awesome but benign. A modern library of Alexandria on every desktop.

The ability to see even a decade into the future is impressive. That Vannevar Bush was able to see much further is remarkable and a testament to his brilliance. It would be a shame if he were only remembered as the inventor of hypertext. When in fact he foresaw the information revolution.

]]> 2004-06-16T06:02:09Z 2004-01-26T10:33:40-08:00 tag:www.virtualtravelog.net,2004://2.58 2004-01-26T18:33:40Z

The first of an occasional series of reviews that will illustrate some important general traits of ontologies. This review covers an ontology called the Common Basic Specification (CBS) that was designed in the late 1980s to bring much needed standardization and rationalization to the fragmented information management processes of the British National Health Service (NHS). This is my explanation of why it failed.

John http://www.virtualtravelog.net/ foobar@bigfoot.com System Design This is the first of an occasional series of reviews I intend to write to illustrate some important general traits of ontologies. In each review I will dissect an ontology and examine why it succeeded or failed. In this essay I mention concepts that are defined in my previous essay. Judging the likely success of an ontology. This first review covers an ontology called the Common Basic Specification (CBS) that was designed in the late 1980s to bring much needed standardization and rationalization to the fragmented information management processes of the British National Health Service (NHS). It persisted in various forms until the late 1990's when it was finally abandoned. This is my explanation of why it failed.

]]> I have been living in the US now for 6 years and have come to recognize a common reaction among Americans whenever the NHS is mentioned. They all try to hide it, but a patronizing look comes over them. It doesn't matter how much you tell them that doctors in the NHS wash before they go into surgery, and even use anesthetics these days, Americans just can't help feeling sorry for anyone who has to suffer under a 3 rd world healthcare system. So before I explain the CBS it is probably worth describing the NHS.

Background

The NHS is the largest employer in Europe it is the main provider of healthcare for the entire United Kingdom (pop 60 million). It's annual budget is about 5 to 6 percent of the GDP of the United Kingdom. In 2002 that amounted to about 64 billion GBP which at current exchange rates is approximately 115 billion USD. By contrast the US spends about 15 percent of its GDP on healthcare and Americans live on average one year longer than the British. A more detailed breakdown of staffing and budget allocation for the NHS is available here. Primary healthcare in the UK is delivered through about 10,000 General Practices and about 2000 Hospitals, most with less than 100 beds. There are a few large Hospitals with up to 1000 beds, these larger hospitals are usually teaching Hospitals. All these numbers are approximate but they give a general idea of the scale of the NHS.

In the late 1980s it became apparent that the same information management problems were being encountered over and over again in hospital after hospital throughout the UK. Not only that, but they were being solved poorly time and time again. This was obviously wasteful. Hospitals had no reason to compete in the area of information management and every reason to cooperate. If these common problems could be solved correctly and then reused significant savings might be realized. At the same time the digital patient record became the holy grail of healthcare computing. In this vision of the future any patients complete medical history would be available anywhere it was needed and could be passed from GP practice to Hospital and back. It could even follow a patient as s/he moved around the country. The Common Basic Specification was suggested as the best way to ensure consistency across multiple solutions and thus enable standardization and portability of the digital patient record.

The Common Basic Specification

The Common Basic Specification ( CBS ) is a conceptual generic model of the activity of health care delivery.

[IT Standards Handbook - NHS Data Standards]

Definition of the NHS Data Model was started in 1986 and continued for several years. It was eventually renamed the Common Basic Specification (CBS). By 1992 a CBS Generic Model had been published. This was in effect a top level ontology (similar to the SUMO currently under development). It was developed for healthcare service delivery but it as can be seen from the diagram below it was universally applicable to any kind of service delivery.

Class descriptions for the figure

• Act for subject: An "Activity" that is directed towards a "Subject".
• Activity: Purposeful and intentional "Event"
• Activity class: A kind of "Activity".
• Agent: Role assumed by a "Subject" enabling it to act purposefully
• Authorised to perform: The recording of the fact that an "Agent" may perform certain classes of "Activity".
• Category: Abstraction on the basis of common properties.
• Concept: "Object" which is a unit of thought.
• Event: Something which happens.
• Incident: "Event" occurring without known volition.
• Knowledge concept: A collection of "Concepts", the relationships between them and the reasons for them.
• Located at: A "Location" for a "Subject".
• Location: Point or piece of space.
• Object: Part of the conceivable or perceivable universe
• Percept: Perceived or inferred to exist
• Reason for activity: The identification of a "Subject property" as the reason for performing or planning an "Activity for subject".
• Reference point: Point or piece of time or space.
• Responsible for: The responsibility that an "Agent" has for a "Subject".
• Results in: A means of establishing that an "Activity for subject" has resulted in a "Subject property".
• Subject: "Percept" which is one or more physical objects.
• Subject property: Anything that describes a subject: location, identity, characteristic etc..
• Timepoint: Point or span of time.

The initial publication of the CBS was found to be too high level. The model was hurriedly reworked and republished in 1993 as a series of CBS Application views one of which was later renamed the CBS Clinical View. This view was a slightly lower level ontology. After nearly 5 years and 5 million GBP had been invested in the model it was decided to use it as the foundation for a series of "demonstrator projects". This work began in 1992/3. I was involved in the largest of these projects where the CBS was to be used as the foundation for an entire Hospital Information Support System. This system was to cover every aspect of hospital information management: patient master index, inpatient and outpatient management, orders and results reporting, , maternity, pharmacy, and many other ancillary activities including; clinical laboratories, laundry, and facilities management. By using the CBS as a conceptual model the final system was intended to be reusable across the NHS and be fully "future-proofed". The system took almost 3 years to build and while it was successful in the first hospital it was only reused once.

In 1998, 3 years after the Hospital Information Support System was completed the model was redefined yet again and renamed the NHS Healthcare Model (HcM). But by this point it was too late. No one believed in the blue fairy of future proofing anymore and the model was abandoned. It is no longer even available online from any official NHS website. However, last year I downloaded a copy in anticipation of writing this article. So here is The Common Basic Specification (CBS) a.k.a. The NHS Healthcare Model (HcM). Take a look around and try drilling down on some of the diagrams to get a real flavor of the model. (If you want a complete copy just send me an email and I will send a zipped file. Please don't over stress my poor machine by downloading the whole thing.)

As ontologies go the Common Basic Specification is large and complex, which is not surprising given its scope and the length of time it took to develop. The CBS was reworked and refined until it became a clean conceptual model for generic service delivery. It is; coherent - logical in the relationship of its parts, generic enough to handle any healthcare delivery use case, and it is mature - the corners have been knocked off. So why did it fail?

Reasons for Failure of the Common Basic Specification

Of the Millions of pounds spent on developing the CBS very little was spent on articulating the benefits of implementing a common standard or training people to use the model. Many potential beneficiaries of the model did not see the value of training staff to understand it. Implementing an ontology is a political activity. It requires persuasion, coercion and sometimes direct threats. Small specialized groups can be persuaded to agree on large complex ontologies but large groups find such agreement difficult and often impossible. It is almost as if there was a law governing the adoption of ontologies.

The size and complexity of an ontology is inversely proportional to the size and complexity of the community of agents that can be persuaded to adopt it.

But there is a more fundamental problem with top level ontologies that is not political. At higher levels of abstraction every conceptual model is subjective; there is always another way to model reality. It can be advantageous to deliberately take a contrary view precisely because it will lead to different conclusions that may offer competitive advantage over others. The field of Healthcare delivery is large enough to accommodate many different world views. The CBS failed because it was the fossilization of a single world view.

Finally the Common Basic Specification took a top down reductionist approach to a fundamentally bottom up emergent problem. This same fundamental error is made by all top level ontologies. It is being made today by the developers of SUMO, an ontology doomed to the same fate as the CBS. As a professional body the IEEE ought to know better. A top down reductionist approach is useful for constrained problem domains but it is the wrong strategy for broad areas of knowledge. Robert Graves understood the tradeoff between a top down and a bottom up and explained it better than I ever could in his poem In Broken Images.

In Broken Images

He is quick, thinking in clear images;
I am slow, thinking in broken images.

He becomes dull, trusting to his clear images;
I become sharp, mistrusting my broken images,

Trusting his images, he assumes their relevance;
Mistrusting my images, I question their relevance.

Assuming their relevance, he assumes the fact,
Questioning their relevance, I question the fact.

When the fact fails him, he questions his senses;
When the fact fails me, I approve my senses.

He continues quick and dull in his clear images;
I continue slow and sharp in my broken images.

He in a new confusion of his understanding;
I in a new understanding of my confusion.

Robert Graves

]]> 2004-05-19T04:08:17Z 2004-01-19T18:00:01-08:00 tag:www.virtualtravelog.net,2004://2.57 2004-01-20T02:00:01Z

By defining the anatomy or architecture of an ontology it is possible to judge the likely success of various examples. The anatomy I develop here is based on practical experience of systems I have designed, reviewed, or studied.

John http://www.virtualtravelog.net/ foobar@bigfoot.com System Design The debate about the promised value of the Semantic Web seems to me to be missing a dispassionate examination of the success, or otherwise, of existing ontology based solutions. Clay Shirky is obviously right when he states that a single monolithic ontology will never work. His critics are equally right when they claim the Semantic web will only work if it is a melange of multiple interoperable Ontologies. What is missing from the debate is a more detailed explanation of what ontologies are good at, how they interoperate, and why systems based on ontologies succeed or fail. From my perspective as a systems designer this last point is the most significant. Debates about theory are nice, but examples of real solutions are more instructive. This essay will begin to examine this question by attempting to define the anatomy of an ontology. I will use this structure in later essays to examine the reasons for success and failure of individual ontologies.

]]> Ontologies are nice, in theory, but difficult to extract value from, in practice. They fail for a variety of reasons: The fundamental assumptions on which they are based are often unsound. Sometimes they are inflexible and unable to adapt to new circumstances. Or, conversely, while attempting to make them adaptable their designers make them too abstract for the people who are then burdened with their implementation. Frequently they are inadequately specified, leaving vital areas open to interpretation, thus negating their usefulness. And just as frequently they are designed without consideration for interoperability with other ontologies - ironic considering their basic purpose. Despite all these opportunities for failure successful ontologies can be spectacularly powerful.

To analyze the success or failure of example ontologies it will be necessary to first define the anatomy or architecture of an ontology. By defining the roles and responsibilities of the component parts of an ontology it will be possible to explain the success or failure of various examples in terms of these components. Ontologies can vary widely in complexity. Some contain all the components listed below but many do not. The anatomy I develop below is based on practical experience of what has worked in systems I have designed, reviewed, or studied. This anatomy may not; be consistent with text books, agree with the latest theory, or reflect current best practice. It does however work for me. It may work for you, but I make no promises.

Ontologies in Theory

Tom Gruber of Stanford University defines the word Ontology as :-

A specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents.

An Ontology can be thought of as a contract shared between agents that intend to exchange information. The contract takes the form of a model for structuring and interpreting exchanged data and a vocabulary that constrains these exchanges. Using a relatively small ontology agents can exchange vast quantities of data and consistently interpret it to extract information. Furthermore they can, in principle, infer new information by applying logical rules allowed, and sometimes explicitly specified, by the Ontology.

It is worth remembering that every non-trivial ontology will allow logical inconsistencies. As Godel pointed out in his Incompleteness Theorem - In any axiomatic system it is possible to create propositions that cannot be proved or disproved. This does not negate the usefulness of ontologies just as it does not negate the usefulness of mathematics. However it does mean ontologies, like everything else, have their limitations.

The sense of the word "Ontology" defined above was defined by the Artificial Intelligence research community after they stole the word from the field of philosophy. More recently it has been adopted to describe components of the Semantic Web. In the AI community the ability to infer new information from existing data is of fundamental importance and this is sometimes misinterpreted as a defining feature of an ontology. In fact many ontologies support this capability only weakly, if at all. The word is also sometimes narrowly defined to mean hierarchical taxonomies or constrained vocabularies, this usage is too narrow since an ontology also contains assertions about how data can be structured and interpreted and these assertions are missing from taxonomies and constrained vocabularies.

The following brief summary taken from the essay What are the differences between a vocabulary, a taxonomy, a thesaurus, an ontology, and a meta-model? by Johannes Ernst provides a good description of the various frameworks often classified as ontologies.

Taxonomies and Thesauri may relate terms in a controlled vocabulary via parent-child and associative relationships, but do not contain explicit grammar rules to constrain how to use controlled vocabulary terms to express (model) something meaningful within a domain of interest. A meta-model is an ontology used by modelers. People make commitments to use a specific controlled vocabulary or ontology for a domain of interest

For the purpose of this essay I will use a broad definition for the word ontology

Ontology :- A specification of a conceptualization used by a community of agents to support the exchange and consistent use of information.

Ontologies in Practice

Ontologies, far from being an unproven new concept, are already in practical daily use. They form the foundation of classification systems, databases, and object oriented software applications. In a few notable cases ontologies have persisted and even evolved over many decades. What is new is the realization that all these seemingly different systems can be compared from an ontological point of view. With the rise of the Internet and the more recent global adoption of the Web the desire to discover and exchange information, rather than mere data, has grown. Developing methods to allow the interoperation of existing and new ontologies has become imperative - hence the efforts being expended on the development of the Semantic Web.

Ontologies in Context

The value of an ontology can only be judged by its ability to support the exchange of information between agents. An ontology considered outside its context of use is a meaningless abstraction. It is only when a number of agents agree to use the same ontology to constrain their interactions that it gains any value. Most ontologies, especially successful ones, are less than perfect. Every useful ontology is a compromise between the conflicting needs of different agents. To fully appreciate the ability of an ontology to successfully support the exchange of information it is necessary to examine the instance data that is exchanged. All too often ontologies are presented in isolation as if they were the end of the story when in fact they are only the beginning of the dialog. The exchange is what is important not the ontology. For this reason I also include the instance data in the discussion below even though it is strictly not part of an ontology. It is worth noting that use cases are an essential part of ontology design. An ontology provided without supporting use cases is likely to be a failure.

General Purpose and Single Purpose Ontologies

There are two basic types of ontology :- General purpose and single purpose. A General purpose ontology is analogous to a Universal Turing Machine in that it is capable of defining any other arbitrary ontology just as a Universal Turing Machine is capable of defining any other arbitrary Turing machine. General purpose ontologies are also capable of defining themselves. Self-definition is an significant capability because it provides for auto-discovery by both programmers and programs.

For example, a program or programmer that can read and understand xml schema is theoretically capable of examining the definition of the xml language (itself written in xml) and auto-discovering new features should they be introduced at some point in the future. Thus programmers and programs need only be taught one ontology definition language if that language is auto-defining. Codd understood this need for self-definition when he specified rule 4 of his 12 rules.

Codds Rule 4 - Active online catalog based on the relational model
The system is required to support an online, inline, relational catalog that is accessible to authorized users by means of their regular query language.

And if you don't believe relational databases are ontologies you should read this.

Self-definition is only one of the benefits of general purpose ontologies. The other is the ability to define new arbitrarily complex ontologies. Paul Rendell has used John Conway's game of life (a set of rules defining a simple tile game) to implement a Turing machine. This mind boggling example proves that the game of life is Turing complete and neatly illustrates how massive complexity can arise from a very simple specification. Another similar example is the XSLT Turing machine. In this example XSLT itself defined in XML is used to define a Turing Machine.

Self definition and the ability to define arbitrarily complex ontologies is more than a party-trick. Consider the C programming language. The first C compiler was written in a programming language called B by Dennis Ritchie. The first thing he wrote in C was another C compiler which he compiled using the compiler written in B. He then created another executable compiler from the same C source code this time using the new pure C compiler. Since he now had the source code for a C compiler written in C and an executable version of that same code he was free of the B language forever.

General purpose ontologies capable of self definition have existed for a century at the most and have only had any practical application outside mathematics and philosophy since the widespread adoption of the computer in the 1960's and 70's. Special purpose ontologies are a different matter. There are many successful special purpose ontologies that are not defined by any formal language (yet) but are never-the-less formally defined. I will examine several of these in later entries but for the time being two examples will illustrate their value.

The periodic table is a classic widely used taxonomy. It becomes an ontology when it is combined with the following assertions and constants. (Please forgive me for any errors in these rules I am not a chemist.)

• A molecule is the smallest quantity of a compound composed of chemically bonded elements
• A chemical reaction occurs when reactants (elements and/or molecules) combine to produce products (elements and/or molecules) of different composition
• Mass is conserved in a chemical reaction
• Elements cannot be transmuted in a chemical reaction
• A Mole of any compound is the product of the Avogadro number and the sum of the atomic masses of its constituent elements

This classic chemistry ontology has withstood the attacks of scientist and abuse of school children for 200 years. It allows chemists to test the plausibility of any possible chemical reaction and predict the quantities of reaction products. The system is not perfect it cannot predict if reactions are thermodynamically likely, and it cannot predict some element properties, for example why Mercury is a liquid, but it can rule out many implausible chemical reactions. Most spectacularly it has been used to infer the existence of undiscovered elements and compounds.

The Benesh Movement Notation is an ontology with a very different purpose. It is a system for recording any form of human movement. Expert Choreologists can use the notation to record entire ballets with multiple, interacting performers so that other choreologist can recreate the ballet in high fidelity without the original choreographer being present. The system records human movement symbolically on a musical stave so that the movements can be synchronized with a musical accompaniment. The system is superior to video since it can record the intentions of the original choreographer rather than the individual interpretation created by a particular performer.

Special purpose ontologies are common. Some are widely used and some are very specialized, many are successful. All these ontologies will gradually be defined by general purpose ontology definition languages so they can be made to interoperate with other similar or overlapping ontologies. By studying these successful and in some cases long-lived ontologies we can learn a great deal about how to make the semantic web successful. Even interoperability of ontologies is not a new problem (more on interoperability in a later entry).

Anatomy of an Ontology. A Five Layer Model

It may be claimed that the model presented here is really a three layer model and that layers 0 and 4 are not strictly part of an ontology. This is true but it is necessary to consider all five layers when evaluating an ontology since they all affect the fitness for purpose of the complete solution.

Layer 0. Ontology Definition Language

The days of informally defined special purpose ontologies are over. From now on anyone taking the trouble to define an ontology will use a formal specification language. All the existing special purpose ontologies will gradually be re-defined using one of the available languages. The question is which one? There are legitimate reasons for having more than one language - different language bestow different qualities on the ontologies they define. It is still early days for many of these languages and today there are few, if any, experts who can make truly informed choices. A few general observations can be made.

Ontology specification languages are not a mature technology. They are still evolving. It is likely that there will be several generations of these languages just as there were several generations of programming languages. The languages in use today will be replaced by "better" languages tomorrow. We can already see this happening, SGML has largely given way to XML and XML may in turn give way to RDF or OWL at least for certain uses. A well designed ontology has the potential to retain its usefulness for a long time and so may need to be migrated from one specification language to another. Languages that are better able to support interoperability will be a safer choice since ontologies specified with them will be easier to migrate to new "better" languages.

In choosing an ontology specification language it should be remembered the relational data model and SQL have been the default choice for the past 25 years. This approach has been phenomenally successful. There is no doubt that it works and there is plenty of support for the model in terms of tools and expertise. It is no coincidence however, that new ontology specification languages are emerging just as the web has made the Internet ubiquitous. Relational databases work well on isolated servers where encapsulation of tightly coupled data and functionality are a benefit and applications interacting with the database can be strictly controlled. But smearing a relational database across multiple unreliably networked machines is to not a good idea. It can be done, but it isn't pretty. The new languages (XML,RDF,OWL,etc) are designed to support exactly this kind of distribution. When functionality and data are distributed, loosely coupled, and independently controlled an ontology specification language will be a much better choice.

Layer 1. Data Structures

All ontologies specify data structures. Depending on the specification language selected these could be; tables containing columns, classes with slots, statements of the form: subject, predicate, object or one of several other basic formats. Whatever language is chosen a conceptual model must be developed that defines a set of data structures that is fit for the intended use. This process is the most influential factor in determining the quality of any solution subsequently designed to use the ontology. Support for flexibility, reliability, maintainability and many other qualities is either designed into the ontology or neglected at this point. Desirable qualities such as these are frequently weakened during later stages by poor development practices, but, even good development practices will not put these qualities back in if they were never there in the first place.

In a previous essay on system design reviews I defined a set of axioms for good conceptual modeling. I have reproduced them here in summary form below. They apply equally to ontology design since an otology is merely a specification of a conceptualization.

1. Everything in moderation and nothing to excess
2. A good system design is based on a sound conceptual model (Architecture)
3. A sound conceptual model accounts for all system requirements at a reasonable level of abstraction
   1. A conceptual model is sufficiently generalized when it can account for all significant use cases in a concise way that reduces complexity by consolidating similar features
   2. A conceptual model is sufficiently specific when it is possible to demonstrate how a system design based on the model will achieve measurable targets for required system attributes
4. A good conceptual model is easy to communicate
   1. A conceptual model is easier to understand and communicate if it is coherent - Logical in the relationship of it's parts - Aesthetically consistent.
   2. A conceptual model is easier to understand and communicate if it is analogous to a commonly experienced, tangible, real world system
   3. A conceptual model is easier to understand and communicate if it is anthropomorphized - Made to mimic human behavior, characteristics and modes of interaction

Layer 2. Assertions and Constraints

Data structures only implement part of a conceptual model. An ontology also contains a set of assertions and constraints. These assertions and constraints define rules concerning the relationships between data structures and the way the data they contain can be used.

• Integrity Constraints define what it means for operational data to be well formed or well structured. These constraints define the rules controlling validity of data such as uniqueness of; objects, records, or statements, and the cardinality and optionality of allowed relationships. Simpler constraints defined the data types for individual items like dates, numbers and specially formatted strings.
• Inference Constraints define how operational data can be combined and manipulated to produce new information - inferences. In most software applications these rules are usually static and embedded in the code and not directly accessible to other applications. However one of the features of ontology specification languages designed to support Artificial Intelligence systems is their ability to explicitly specify these rules so that external systems can learn them.

Layer 3. Reference Data

Many ontologies specify reference data in the form of constrained vocabularies, taxonomies, or thesauri. These data are used as components and classifiers of the operational data that is exchanged between agents. Agents agree on the meaning of reference data beforehand and thus can interpret exchanged messages or statements without ambiguity. Implicit in this use of reference data is the assumption that changes to the reference data will be infrequent as they will likely require version changes of the entire ontology. This is a non-trivial happening. Modification to a previously agreed constrained vocabulary may require many agents to change the way they interpret and process data and should be avoided if possible. Significant effort should be expended on considering the consequence of changes to reference data. It is too easy to ignore such issues and assume someone else will deal with problem should they arise.

Layer 4. Operational Data

Operational data, sometimes also called instance data, is supported by it's ontology but is not part of it. The purpose of the ontology is to provide structure for the operational data. As a result it is necessary to consider the operational data when evaluating an ontology. It is my experience that there are two general types of operational data.

• Configuration Data. This data supports the required degree of flexibility in the solution by allowing certain features to be reconfigured. Systems that make a special feature of this type of flexibility are often called data driven, In a banking solution the types of bank account and the data relating to interest rates associated with each account could be considered configuration data. This is not the same as reference data. Configuration data is expected to change and must not require a version change of the ontology. In a hospital solution configuration data may describe the wards in the hospital and the bed compliment on each ward. All agents in such a system expect these things change over time and must be capably of reconfiguring the way they process data accordingly.

• Activity Data. Management of activity data is the main reason for the existence of any ontology. Activity data includes the actual exchanges of messages and statements between agents that have agreed to use the ontology. Without activity data everything that goes before is pointless. In a banking environment activity data could describe the opening or closing of an accounts or the actual deposits and withdrawals. In a health care setting activity data could describe clinical interventions; the x-rays and blood test performed on a patient, or at a slightly higher level the inpatient stays and diagnoses.

Summary

The success or failure of any ontology should be judged primarily by it's ability to support the exchange of operational activity data between agents. This can only be confirmed after the ontology is implemented by assessing how the system performs in the context of use. To reduce the risk of failure in the early stages of specification the various components of the ontology should be assessed individually and collectively in terms of their ability to support required use cases for operational activity data. The design of any ontology should be assessed in terms of the following components

1. Ontology Definition Language
2. Data Structures
3. Assertions and Constraints
   1. Integrity Constraints
   2. Inference Constraints
4. Reference Data
5. Operational Data
   1. Configuration Data
   2. Activity Data

]]> 2004-02-29T22:21:28Z 2003-12-29T23:33:33-08:00 tag:www.virtualtravelog.net,2003://2.56 2003-12-30T07:33:33Z

When the history of early software development is written it will be a travesty. Few historians will have the ability, and even fewer the inclination, to learn long dead programming languages. History will be derived from the documentation not the source code.

John http://www.virtualtravelog.net/ foobar@bigfoot.com Technology When the history of early software development is written it will be a travesty. Few historians will have the ability, and even fewer the inclination, to learn long dead programming languages. History will be derived from the documentation not the source code. Alan Turings perplexed, hand written annotation "How did this happen?" on a cutting of Autocode taped into his note book will remain a mystery.

What kind of bug would stump Alan Turing? Was it merely a typo that took a few hours to find? a simple mistake maybe? Or did the discipline of the machine expose a fundamental misconception and thereby teach him a lesson? The only way to know would be to learn Autocode.

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The first stored program to be successfully executed was written by Tom Kilburn and executed on Monday 21 st June 1948 at Manchester University, England. It is said that this was the first and last program that Kilburn ever wrote. The program found the highest factor of a number and took 1 minute to complete on it's first run. A second run with a different number took 2 minutes and 52 seconds. Unfortunately no one thought to document the program until Geoffrey C. Tootill wrote an amended version in his note book a month later on the 18 th July 1948. The original has been lost. Below is a copy of Tootill's version.

[Via The National Archive for the History of Computing]

]]> 2004-02-29T22:24:31Z 2003-12-11T15:33:53-08:00 tag:www.virtualtravelog.net,2003://2.54 2003-12-11T23:33:53Z

The changing relationships between time, distance, information, and money are at the heart of today's globalization trends. The cost of a 3-minute transatlantic phone call is an interesting metric since it fixes distance and the amount of information. These diagrams literally replace distance with cost and graphically show how the world is "shrinking" from a cost of information exchange perspective.

John http://www.virtualtravelog.net/ foobar@bigfoot.com Globalization A corollary of the statement "Time is Money" is that things that cause time to be consumed are money as well - distance is related to time through velocity and information is related to time in the same way through bandwidth. Velocity is a crucial factor in determining the cost of exchanging physical goods and bandwidth is similarly crucial in determining in the cost of exchanging information. In both cases speed costs. The changing relationships between time, distance, information, and money are at the heart of today's globalization trends.

The cost of a 3-minute transatlantic phone call is an interesting metric since it fixes distance and the amount of information. The graph above comes from a presentation on Globalization by the World Bank. It clearly shows that the cost of a 3-minute call between New York and London has been decaying exponentially over 6 decades. In fact this metric has a half-life of about a decade.

]]> But the graph does not show the impact of these changes. The following before-and-after diagrams come from Telegeography [via Matt Jones]. Both diagrams show the cost of a one minute phone call from the US. These diagrams literally replace distance with cost and graphically show how the world is "shrinking" from a cost of information exchange perspective.

Cost of a 1-Minute Call from the US 1994

Cost of a 1-Minute Call from the US 1998

An interesting fact that is not immediately apparent from these diagrams is that the 10-year half-life of transatlantic call costs, that remained steady for 6 decades, has collapsed in the last decade. Between 1990 and 1996 the cost of a 3-minute call from New York to London fell from about $3.50 to $0.30, which is an order of magnitude fall in half a decade. It seems that cell phones, deregulation, and the rise of the Internet have well-and-truly shattered the old pricing paradigm.

]]> 2004-12-31T01:55:51Z 2003-11-29T15:15:00-08:00 tag:www.virtualtravelog.net,2003://2.51 2003-11-29T23:15:00Z

Judging by the reviews of this product it appears that many people have been unable to fix problems with this device. Below is my description of the problems I encountered and a solution that worked for me. Hopefully this will help others, but as always, your mileage may vary!

John http://www.virtualtravelog.net/ foobar@bigfoot.com Reviews Background

A few months ago I had to setup a home office and decided I would take the opportunity to upgrade my home network. My Linksys BEFSR41 Etherfast Cable / DSL Router had never given me any problems and so I decided to upgrade to the Linksys BEFW11S4 Wireless-B broadband Router. I now have everything working reliably but getting to this happy state and resolving the problems took a lot of luck and in the end the solution was far from obvious. Judging by the bad reviews on Amazon and elsewhere it appears that many people have been unable to fix similar problems with this device. Below is my description of the problem and a solution that worked for me. Hopefully this will help others, but as always, your mileage may vary!

]]> Configuration

I have two Macs running OSX on my home network, both machines have static IP addresses and are wired to the router. I wanted to add a wireless Windows Laptop with a DHCP assigned IP address. I purchased a Linksys BEFW11S4 Wireless-B broadband Router and at the same time I bought a Proxim ORiNOCO Gold 802.11a/b/g ComboCard for the Laptop this came with a Proxim client utility. Installation and setup of the laptop card was very easy. Simply plug in the card, insert the disc and install the driver. The card immediately picked up my neighbors open network and I was on the Internet. Setting up the router was a bit more challenging; I copied the configuration from my previous device and enabled DHCP to start issuing IP numbers out of range of my 2 static machines. Everything worked and I was connected to the Internet via my new router.

Symptoms

The wireless network seemed to work fine for several days but then it "hung". The only fix was to turn off the router and turn it back on again. This hang affected all machines connected to the router. Whenever they attempted any network operations they would time out. However the Proxim client utility and the laptop claimed the wireless network was still running! The network hung once or twice a week - Annoying, but tolerable. Then I upgraded the laptop from windows 2000 to XP and things got worse! The network would hang one or two times a day. At first I did not connect the OS upgrade with the router problems, besides, I had another bigger issue.

I connect to work via a Cisco VPN client which seemed to be working ok but all of a sudden (in fact immediately after the XP upgrade), Microsoft Exchange slowed to glacial speed. It would take hours to sync with the Exchange Server. This was not acceptable. I had to fix things. I called our company technical support and got through to the guy who manages the VPN. He said "What order did you install the wireless card driver and the VPN client? Because they don't play nice together and you must install the wireless card driver first and then the VPN client".

Solution

• Download and install the latest firmware upgrade from Linksys. This is not enough to fix the problem on its own. I tried this first and the network still hung. But it's a good idea and this solution may not work without it.
• Uninstall the Cisco VPN client and the Proxim client utility from the laptop
• Reinstalled the Proxim client utility on the laptop
• Reinstalled the Cisco VPN client on the laptop

Outcome

The network has been running for 10 days without a single hitch.

Cause

I'm still not certain what the cause was but this is what I suspect. The VPN client and Proxim client utility share something in common dlls or configuration or something! When installed in the wrong order things get messed up and in unusual circumstances the laptop sends network traffic that is somehow malformed. This affects the router and causes it to hang. Basically the router appears to be intolerant of glitches in low level network messages and this leads to low reliability. Not a great explanation I know and it may be completely spurious but my network now works reliably so I'm happy!

]]> 2004-07-06T19:55:05Z 2003-11-27T15:40:00-08:00 tag:www.virtualtravelog.net,2003://2.50 2003-11-27T23:40:00Z

Some time between 1934 and 1950 the first modern computer was created. Pinning down exactly when that event occured is not easy. It depends on how you define the term computer and what you think is more important: The concept, the design, the first succesful test, or the first time the machine solved a real problem. This is the first release of an open source graphical representation of the evolution of the modern computer.

John http://www.virtualtravelog.net/ foobar@bigfoot.com Technology Some time between 1934 and 1950 the first modern computer was created. Pinning down exactly when that event occured is not easy. It depends on how you define the term computer and what you think is more important: The concept, the design, the first succesful test, or the first time the machine solved a real problem. In those early days it usually took years for a team to progress from concept through design to working machine. There were many such teams working mainly in the US and UK. These teams competed and cooperated somtimes they shared ideas and designs, and they sent representatives to visit each others laboratories. On one famous occassion in the Summer of 1946 almost all the leaders in the field got together at the Moore School for an 8 week long series of lectures. In short the story of the emergence of the modern computer is a complex one that involves both direct and indirect contributions from many people.

There are many Computer History Timelines in existence. But all of these suffer from the same flaws. They are incomplete and thier linear nature fails to capture the complex web of influence that was the hallmark of computer development.

Downloadable files available here

In an effort to visualize this web of interaction. I have started to develop a graphical representation of the evolution of the modern computer. Fortunately AT&T have kindly released a package called Graphviz which is capable of drawing complex directed graphs. The graph above is produced by Graphviz from a text file.

The text file contains a detailed description of my approach, the classification I have used, and lists all the machines and the references to the data sources I used. I have not duplicated that information here because the whole point of the exercise is to gather all the data in one place.

I have licensed this file with an attribution, share alike creative commons license. So please feel free to download and improve what I have started. If you do make changes please send me a copy and I will share the updates on this page.

For the record. I believe that The Manchester Mk I Prototype was the first Computer in a modern sense. But the text file is not intended to prove this or any other machine was first. It is only intended to record the known dates and influences for computing machines designed between 1934 and 1950. I Believe that the graph is complex enough to support many interpretations.

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