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Time for the next generation of knowledge automation

In preparing for my workshop at the Business Rules Forum in Las Vegas on November 5th, I have focused on the following needs in reasoning about processes, about events, and about or over time:

  1. Reasoning at a point within a [business] process
  2. Reasoning about events that occur over time.
  3. Reasoning about a [business] process (as in deciding what comes next)
  4. Reasoning about and across different states (as in planning)

Enterprise decision management (EDM) addresses the first.  Complex event processing (CEP) is concerned with the second.  In theory, EDM could address the third but it does not in practice.  This third item includes  the issue of governing and defining workflow or event-driven business processes rather than point decisions within such business processes. 

Business applications of rules have not advanced to include the fourth item.  That is to say, business has yet to significantly leverage reasoning or problem solving techniques that are common in artificial intelligence.  For example, artificially intelligent question and answer systems, which are being developed for  the semantic web,  can do more than retrieve data – they perform inference.  Commercial database and business intelligence queries are typically much less intelligent, which presents a number of opportunities that I don’t want to go into here but would happy to discuss with interested parties.  The point here is that business does not use reasoning much at all, let alone to search across the potential ramifications of alternative decisions or courses of action before making or taking one.  Think of playing chess or a soccer-playing robot planning how to advance the ball on goal.  Why shouldn’t business strategies or tactical business decisions benefit from a little simulated look-ahead along with a lot of inference and evaluation?

Even though I have recently become more interested in the fourth of these areas, I expect the audience at the business rules forum to be most interested in the first two points above.  There will also be some who have enough experience with complex business processes, which are common in larger enterprises.  These folks will be interested in the third item.  Only the most advanced applications, such as in biochemical process planning, will be interested in the fourth.  I don’t expect many of them to attend!

The notion of enterprise decision management (EDM) is focused on point decision making within a business process.  For enterprises that are concerned with governing business processes, a model of the process itself must be available to the business rules that govern its operation.  I’ve written elsewhere about the need for an ontology of events and processes in order to effectively integrate business process management (BPM) with business rules.  Here, and in the workshop, I intend to get a little more specific about the requirements, what is lacking in current standards and offerings, and what we’re trying to do about it. Continue reading “Time for the next generation of knowledge automation”

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Harvesting business rules from the IRS

Does your business have logic that is more or less complicated than filing your taxes?

Most business logic is at least as complicated.  But most business rule metaphors are not up to expressing tax regulations in a simple manner.  Nonetheless, the tax regulations are full of great training material for learning how to analyze and capture business rules.

For example, consider the earned income credit (EIC) for federal income tax purposes in the United States.  This tutorial uses the guide for 2003, which is available here. There is also a cheat sheet that attempts to simplify the matter, available here. (Or click on the pictures.)

eitc-publication-596-fy-2003.jpgeitc-eligibility-checklist-for-tax-year-2003.jpg

What you will see here is typical of what business analysts do to clarify business requirements, policies, and logic.  Nothing here is specific to rule-based programming.  Continue reading “Harvesting business rules from the IRS”

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Ontology of time in progress – amounts needed

Recent posts on money and time have produced some excellent comments and correspondence.  There is even recent OMG effort that is right on the money, at least concerning time.  For details, see the Date-Time Foundational Vocabulary RFP.  I am particularly impressed with SBVR “Foundation” Vocabularies, which I understand Mark Linehan of IBM presented last week at an OMG meeting in DC[1].

Mark’s suggestions include establishing standard upper ontologies for:

  1. Time & dates
  2. Monetary amount
  3. Location
  4. Unit of measure
  5. Quantities, cardinalities, and ratios
  6. Arithmetic operations

I will skip operations for now since they are not taxonomic concepts but functional relationships involving such concepts.  I believe the post on CEP and BPM covered time in adequate detail and the post on Siebel’s handling of foreign exchange covered the currency exchange aspects of money.  It only touched on the more general concept of amounts that I will focus on here.

The remaining concepts are common to almost every application conceivable.  They are some of the most primitive, domain-independent concepts of a critical and practical upper ontology.  They include: Continue reading “Ontology of time in progress – amounts needed”

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Understanding events and processes takes time

We have been teaching a computer to answer questions like, “How much did IBM’s earnings change last quarter?”  It takes a fair bit of knowledge, including how to understand English, to answer this question.  But teaching it what a “quarter” is brought back memories of debates with some former CMU colleagues about what units are and how to model time.  Since quite a few people ask me for help with knowledge engineering and ontological matters, I thought some might be interested in parts of those debates.As you will see, a strong upper ontology of common knowledge is required to understand common business knowledge.  Leveraging such an ontology is the only way to deliver business rules for under $50.

Sentences like “do something if more than a number of possibly related things have happened within a timeframe of something else happening” or “do something if nothing happens within a timeframe following something happening” are extremely common in business process management (BPM), complex event processing (CEP), and workflow.  With a sense of time, a business rules management system (BRMS) can support BPM, CEP, and workflow applications almost trivially.  Without a sense of time, most BRMS force users to perform computations.

For example, without a sense of time and an infrastructure that supports it, the sentence “call a customer if no response is received within 30 days of notifying the customer of a delinquency” has to be transformed into something like “if a notice is mailed on a date and the notice is a delinquency and the date of notification has a day number then compute the date for checking by adding 30 to the day number and check for a response to the delinquency notice on the date for checking”.  The checking on a date for a response to a notice must also be implemented as a database (or persistent queue) of events to be polled or triggered by application code.  Then a second rule is required to implement the check, as in “if checking whether a response has been received to a notice and the notice was given on a date of notice and the notice was given to a customer and there exists no record of communication with the customer since the date of notice then call the customer”.  (Note that this is actually how most BRMS products would implement this.  The natural language approach I prefer handles the original sentence.)

The discussion here reflects the general structure and content that a usable ontology for business process management requires.  Most users of business rules management tools will find the need to understand and engineer this discussion in their tool of choice.  As my Haley Systems customers know, much of this is reflected in Authority’s built-in ontology and English vocabulary, but quite a few of the points discussed here reflect improvements, especially concerning the confusion between units and amounts.

As you will see the discussion takes careful thinking.  Some readers may find it onerous.  If at any time you have had enough (or if you simply cannot take anymore!), please skip to the end and decide whether to fill in the conclusions by revisiting the body.

Continue reading “Understanding events and processes takes time”

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The $50 Business Rule

Work on acquiring knowledge about science has estimated the cost of encoding knowledge in question answering or problem solving systems at $10,000 per page of relevant textbooks. Regrettably, such estimates are also consistent with the commercial experience of many business rules adopters. The cost of capturing and automating hundreds or thousands of business rules is typically several hundred dollars per rule. The labor costs alone for a implementing several hundred rules too often exceed $100,000.

The fact that most rule adopters face costs exceeding $200 per rule is even more discouraging when this cost does not include the cost of eliciting or harvesting functional requirements or policies but is just the cost of translating such content into the more technical expressions understood by business rules management systems (BRMS) or business rule engines (BRE).

I recommend against adopting any business rule approach that cannot limit the cost of automating elicited or harvested content to less than $100 per rule given a few hundred rules. In fact, Automata provides fixed price services consistent with the following graph using an approach similar to the one I developed at Haley Systems.

Cost per Harvested or Elicited Rule

Continue reading “The $50 Business Rule”

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When Rules Meet Requirements

I am working on some tutorial material for business analysts tasked with eliciting and harvesting rules using some commercial business rules management systems (BRMS). The knowledgeable consumers of this material intuitively agree that capturing business rules should be performed by business analysts who also capture requirements. They understand that the clarity of rules is just as critical to successful application of BRMS as the clarity of requirements is to “whirlpool” development.[1]  But they are frustrated by the distinct training for requirements versus rules. They believe, and I agree, that unification of requirements and rules management is needed.

Consider these words from Forrester:

One might argue that Word documents, email, phone calls, and stakeholder meetings alone are adequate for managing rules. In fact, that is the methodology currently used for most projects in a large number of IT shops. However, this informal, ad hoc approach doesn’t ensure rigorous rules definition that is communicated and understood by all parties. More importantly, it doesn’t lend itself to managing the inevitable rules changes that will occur throughout the life of the project. The goal must be to embrace and manage change, not to prevent it. [2]

But note that Forrester used the word “requirements” everywhere I used “rules” above!

Continue reading “When Rules Meet Requirements”