The Learnability of Business Rules

Wang, Olivier; Ke, Changhai; Liberti, Leo; Marie, Christian de Sainte

doi:10.1007/978-3-319-51469-7_22

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…With respect to the work of Wang et al, 20 the Turing-completeness proof presented here, the operational semantics, and the associated proof-of-concept implementation, are new. We remark that, since the BR language itself is Turing-complete, the termination of its programs, which we claim can be computed by our operational semantics, is obviously an undecidable problem.…”

Section: Contributionsmentioning

confidence: 93%

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Universality and prediction in business rules

Wang

Marie

et al. 2018

Computational Intelligence

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Business rules (BR) have the form ⟨if condition then action⟩. A BR program, which can be executed by means of an interpreter, is a sequence of business rules. Motivated by International Business Machines use cases, we look at the problem of setting parameter values in a given BR program so it will achieve a given average goal over all possible instances. We explore the following fundamental question: Is there a general learning algorithm, which addresses this issue? We prove the answer is negative. On the positive side, we derive operational semantics for BR programs. As a proof of concept, we show empirically that these can be used to detect potential nontermination situations.

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Section: Contributionsmentioning

confidence: 93%

“…Algorithms for learning some restricted classes of programs exist for inductive logic programming 32 or nonmonotonic inductive logic programming. 33 Here, similar to the work of Wang et al, 20 we wish to examine the general case of learning in all terminating programs.…”

Section: Basics Of Computational Learning Theorymentioning

confidence: 99%

Universality and prediction in business rules

Wang

Marie

et al. 2018

Computational Intelligence

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“…The work by Karaa et al 39 is interesting in the context of UML since it presented an improved approach to transform business rules from natural language that are specified within a user's requirements document into an UML class diagram, automatically. The work by Wang et al 40 is also appealing in the context of BPMN since they identified the extent to which business rules can actually be learnt from application code, logs, or models. Some authors have worked on checking whether the implementation of a business rule actually conforms to it or not, namely: Hnatkowska and Mazurek 41 focused on structural rules, which are a particular kind of constraint rules that focus on describing the structure of data, and checking whether a UML model conforms to them or not; Minock et al 42 leveraged the C-Phrase system, 43 which was originally intended to consult entity-relation databases using natural language, in order to check whether some Swedish Defence Materiel Administration databases conformed to some governmental constraint rules.…”

Section: Other Related Proposalsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

On the design of an advanced business rule engine

Jiménez

Corchuelo

2022

Softw Pract Exp

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Business rules govern how well‐managed companies perform every day. They are expected to be written in natural language because they are devised by business people. That makes it difficult to translate them automatically into executable rules that can be integrated into typical information systems. Our industrial and academic research concludes that the current tools have a number of problems, namely: many of them do not pay any attention to the SBVR standard; some of them are not multi‐language; most of them cannot achieve perfect parsing accuracy; some of them are not domain agnostic; some of them use proprietary technologies; some of them do not produce executable rules; and none supports exploratory what‐if analyses natively. In this article, we present Tier‐Rules, which is a system that overcomes the previous problems. We also report on an industrial case study that helps illustrate it in practice.

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“…The loop only terminates once every condition of the BRs is False. We proved in [29] that there is a universal BR program which can simulate any Turing Machine (TM), which makes the BR language Turing-complete.…”

Section: Preliminariesmentioning

confidence: 99%

Controlling Some Statistical Properties of Business Rules Programs

Wang

Liberti

2017

Lecture Notes in Computer Science

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Business Rules programs encode decision-making processes using "if-then" constructs in a way that is easy for non-programmers to manipulate. A common example is the process of automatic validation of a loan request for a bank. The decision process is defined by bank managers relying on the bank strategy and their own experience. Bank-side, such processes are often required to meet goals of a statistical nature, such as having at most some given percentage of rejected loans, or having the distribution of requests that are accepted, rejected, and flagged for examination by a bank manager be as uniform as possible. We propose a mathematical programming-based formulation for the cases where the goals involve constraining or comparing values from the quantized output distribution. We then examine a simulation for the specific goals of (1) a max percentage for a given output interval and (2) an almost uniform distribution of the quantized output. The proposed methodology rests on solving mathematical programs encoding a statistically supervised machine learning process where known labels are an encoding of the required distribution.

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The Learnability of Business Rules

Cited by 4 publications

References 9 publications

Universality and prediction in business rules

Universality and prediction in business rules

On the design of an advanced business rule engine

Controlling Some Statistical Properties of Business Rules Programs

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