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RuleRS: a rule-based architecture for decision support systems

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Abstract

Decision-makers in governments, enterprises, businesses and agencies or individuals, typically, make decisions according to various regulations, guidelines and policies based on existing records stored in various databases, in particular, relational databases. To assist decision-makers, an expert system, encompasses interactive computer-based systems or subsystems to support the decision-making process. Typically, most expert systems are built on top of transaction systems, databases, and data models and restricted in decision-making to the analysis, processing and presenting data and information, and they do not provide support for the normative layer. This paper will provide a solution to one specific problem that arises from this situation, namely the lack of tool/mechanism to demonstrate how an expert system is well-suited for supporting decision-making activities drawn from existing records and relevant legal requirements aligned existing records stored in various databases.We present a Rule-based (pre and post) reporting systems (RuleRS) architecture, which is intended to integrate databases, in particular, relational databases, with a logic-based reasoner and rule engine to assist in decision-making or create reports according to legal norms. We argue that the resulting RuleRS provides an efficient and flexible solution to the problem at hand using defeasible inference. To this end, we have also conducted empirical evaluations of RuleRS performance.

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Notes

  1. We fully recognise that the notion of “reasonable” time is a very open texture one. It depends on the applications: while a response in seconds could ot be suitable for a real time critical systems, an overnight computation is suitable for a task, e.g., auditing the whole set of transaction of a bank when such auditing would take months based on a limited sample data.

  2. Prohibitions can be expressed as maintenance obligations with a negated content, i.e., \([\hbox {OM}]\lnot p\).

  3. http://json.org accessed on 19 January 2017.

  4. The are various query types as basic (compares two values), quantified (compares a value or values with a collection of values), BETWEEN (compares a value with a range of values), NULL (tests for null values), LIKE (searches for strings that have a certain pattern), EXISTS (tests for the existence of specific information), IN [another approach to compare a value with a collection of values (Yu et al. 1992; International Business Machines Corporation 1993, 2001)] and different aggregate functions [AVG, MAX, MIN, SUM, COUNT(*), or COUNT(DISTINCT)].

  5. When “SELECT” statement is used in a predicate, is called a “subquery” (International Business Machines Corporation 1993, 2001).

  6. SPINDle Reasoner is available to download freely from http://spindle.data61.csiro.au/spindle/tools.html accessed on 31 January 2018 under LGPL license agreement. https://opensource.org/licenses/lgpl-license accessed on 31 January 2018.

  7. http://stat-computing.org/dataexpo/2009/the-data.html accessed on 19 January 2017.

  8. http://www.transtats.bts.gov/Fields.asp?Table_ID=236 accessed on 19 January 2017.

  9. By equivalent, we mean that given an input the decision trees and our rules produce same outcome.

  10. The snippets reported here have been selected for the type of SQL query they employ to evaluate the corresponding response time, not for the meaning of the data associated with the query.

  11. http://bit.ly/1Lyg6Z4 accessed on 19 January 2017.

  12. with 2-way joins (1-INNER JOIN and 1-LEFT JOIN), “GROUP BY”, “HAVING”, “COUNT” and “ORDER BY” predicate.

  13. http://java.sys-con.com/node/45082 accessed on 19 January 2017.

  14. http://intelligence.worldofcomputing.net/expert-systems-articles/rule-based-expert-systems.html accessed on 20 January 2017.

  15. http://xsb.sourceforge.net accessed on 20 January 2017.

  16. http://www.dcc.fc.up.pt/~vsc/Yap/ accessed on 20 January 2017.

  17. http://www.dlvsystem.com/ accessed on 20 January 2017.

  18. http://sourceforge.net/projects/iris-reasoner/ accessed on 20 January 2017.

  19. http://www.semafora-systems.com/en/products/ontobroker/ accessed on 20 January 2017.

  20. http://www.drools.org accessed on 20 January 2017.

  21. http://www.jessrules.com/jess/index.shtml accessed on 20 January 2017.

  22. https://prova.ws accessed on 20 January 2017.

  23. http://jena.apache.org accessed on 20 January 2017.

  24. https://confluence.ontotext.com/display/OWLIMv35/SwiftOWLIM+Introduction last accessed on 20 January 2016.

  25. https://confluence.ontotext.com/display/OWLIMv35/BigOWLIM+Introduction accessed on 20 January 2017.

  26. http://www.cyc.com accessed on 20 January 2017.

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Acknowledgements

Preliminary version of the material included in this paper appeared at ICAIL 2015 (Islam and Governatori 2015).

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  1. Data61, CSIRO, Brisbane, Australia

    Mohammad Badiul Islam & Guido Governatori

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  1. Mohammad Badiul Islam
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  2. Guido Governatori
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Correspondence to Mohammad Badiul Islam.

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Islam, M.B., Governatori, G. RuleRS: a rule-based architecture for decision support systems. Artif Intell Law 26, 315–344 (2018). https://doi.org/10.1007/s10506-018-9218-0

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