The learnability of business rules

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

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

The learnability of business rules

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

IBM GBS France

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Among programming languages, a popular one in corporate environments is Business Rules. These are conditional statements which can be seen as a sort of “programming for non-programmers”, since they remove loops and function calls, which are typically the most difficult programming constructs to master by laypeople. A Business Rules program consists of a sequence of “IF condition THEN actions” statements. Conditions are verified over a set of variables, and actions assign new values to the variables. Medium-sized to large corporations often enforce, document and define their business processes by means of Business Rules programs. Such programs are executed in a special purpose virtual machine which verifies conditions and executes actions in an implicit loop. A problem of extreme interest in business environments is enforcing high-level strategic decisions by configuring the parameters of Business Rules programs so that they behave in a certain prescribed way on average. In this paper we show that Business Rules are Turing-complete. As a consequence, we argue that there can exist no algorithm for configuring the average behavior of all possible Business Rules programs.

Original language	English
Title of host publication	Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers
Editors	Giuseppe Nicosia, Giovanni Giuffrida, Piero Conca, Panos M. Pardalos
Publisher	Springer Verlag
Pages	257-268
Number of pages	12
ISBN (Print)	9783319514680
DOIs	https://doi.org/10.1007/978-3-319-51469-7_22
Publication status	Published - 1 Jan 2016
Event	2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016 - Volterra, Italy Duration: 26 Aug 2016 → 29 Aug 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10122 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016
Country/Territory	Italy
City	Volterra
Period	26/08/16 → 29/08/16

Access to Document

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

Cite this

Wang, O., Ke, C., Liberti, L., & de Sainte Marie, C. (2016). The learnability of business rules. In G. Nicosia, G. Giuffrida, P. Conca, & P. M. Pardalos (Eds.), Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers (pp. 257-268). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10122 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-51469-7_22

Wang, Olivier ; Ke, Changhai ; Liberti, Leo et al. / The learnability of business rules. Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers. editor / Giuseppe Nicosia ; Giovanni Giuffrida ; Piero Conca ; Panos M. Pardalos. Springer Verlag, 2016. pp. 257-268 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ee5d3789c17049169579b092b0648923,

title = "The learnability of business rules",

abstract = "Among programming languages, a popular one in corporate environments is Business Rules. These are conditional statements which can be seen as a sort of “programming for non-programmers”, since they remove loops and function calls, which are typically the most difficult programming constructs to master by laypeople. A Business Rules program consists of a sequence of “IF condition THEN actions” statements. Conditions are verified over a set of variables, and actions assign new values to the variables. Medium-sized to large corporations often enforce, document and define their business processes by means of Business Rules programs. Such programs are executed in a special purpose virtual machine which verifies conditions and executes actions in an implicit loop. A problem of extreme interest in business environments is enforcing high-level strategic decisions by configuring the parameters of Business Rules programs so that they behave in a certain prescribed way on average. In this paper we show that Business Rules are Turing-complete. As a consequence, we argue that there can exist no algorithm for configuring the average behavior of all possible Business Rules programs.",

author = "Olivier Wang and Changhai Ke and Leo Liberti and \{de Sainte Marie\}, Christian",

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doi = "10.1007/978-3-319-51469-7\_22",

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Wang, O, Ke, C, Liberti, L & de Sainte Marie, C 2016, The learnability of business rules. in G Nicosia, G Giuffrida, P Conca & PM Pardalos (eds), Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10122 LNCS, Springer Verlag, pp. 257-268, 2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016, Volterra, Italy, 26/08/16. https://doi.org/10.1007/978-3-319-51469-7_22

The learnability of business rules. / Wang, Olivier; Ke, Changhai; Liberti, Leo et al.
Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers. ed. / Giuseppe Nicosia; Giovanni Giuffrida; Piero Conca; Panos M. Pardalos. Springer Verlag, 2016. p. 257-268 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10122 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - The learnability of business rules

AU - Wang, Olivier

AU - Ke, Changhai

AU - Liberti, Leo

AU - de Sainte Marie, Christian

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Among programming languages, a popular one in corporate environments is Business Rules. These are conditional statements which can be seen as a sort of “programming for non-programmers”, since they remove loops and function calls, which are typically the most difficult programming constructs to master by laypeople. A Business Rules program consists of a sequence of “IF condition THEN actions” statements. Conditions are verified over a set of variables, and actions assign new values to the variables. Medium-sized to large corporations often enforce, document and define their business processes by means of Business Rules programs. Such programs are executed in a special purpose virtual machine which verifies conditions and executes actions in an implicit loop. A problem of extreme interest in business environments is enforcing high-level strategic decisions by configuring the parameters of Business Rules programs so that they behave in a certain prescribed way on average. In this paper we show that Business Rules are Turing-complete. As a consequence, we argue that there can exist no algorithm for configuring the average behavior of all possible Business Rules programs.

AB - Among programming languages, a popular one in corporate environments is Business Rules. These are conditional statements which can be seen as a sort of “programming for non-programmers”, since they remove loops and function calls, which are typically the most difficult programming constructs to master by laypeople. A Business Rules program consists of a sequence of “IF condition THEN actions” statements. Conditions are verified over a set of variables, and actions assign new values to the variables. Medium-sized to large corporations often enforce, document and define their business processes by means of Business Rules programs. Such programs are executed in a special purpose virtual machine which verifies conditions and executes actions in an implicit loop. A problem of extreme interest in business environments is enforcing high-level strategic decisions by configuring the parameters of Business Rules programs so that they behave in a certain prescribed way on average. In this paper we show that Business Rules are Turing-complete. As a consequence, we argue that there can exist no algorithm for configuring the average behavior of all possible Business Rules programs.

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DO - 10.1007/978-3-319-51469-7_22

M3 - Conference contribution

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers

A2 - Nicosia, Giuseppe

A2 - Giuffrida, Giovanni

A2 - Conca, Piero

A2 - Pardalos, Panos M.

PB - Springer Verlag

T2 - 2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016

Y2 - 26 August 2016 through 29 August 2016

ER -

Wang O, Ke C, Liberti L, de Sainte Marie C. The learnability of business rules. In Nicosia G, Giuffrida G, Conca P, Pardalos PM, editors, Machine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers. Springer Verlag. 2016. p. 257-268. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-51469-7_22

The learnability of business rules

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