Tabled CLP for Reasoning Over Stream Data

Author Joaquín Arias



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Joaquín Arias

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Joaquín Arias. Tabled CLP for Reasoning Over Stream Data. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 17:1-17:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)
https://doi.org/10.4230/OASIcs.ICLP.2016.17

Abstract

The interest in reasoning over stream data is growing as quickly as the amount of data generated. Our intention is to change the way stream data is analyzed. This is an important problem because we constantly have new sensors collecting information, new events from electronic devices and/or from customers and we want to reason about this information. For example, information about traffic jams and costumer order could be used to define a deliverer route. When there is a new order or a new traffic jam, we usually restart from scratch in order to recompute the route. However, if we have several deliveries and we analyze the information from thousands of sensors, we would like to reduce the computation requirements, e.g. reusing results from the previous computation. Nowadays, most of the applications that analyze stream data are specialized for specific problems (using complex algorithms and heuristics) and combine a computation language with a query language. As a result, when the problems become more complex (in e.g. reasoning requirements), in order to modify the application complex and error prone coding is required. We propose a framework based on a high-level language rooted in logic and constraints that will be able to provide customized services to different problems. The framework will discard wrong solutions in early stages and will reuse previous results that are still consistent with the current data set. The use of a constraint logic programming language will make it easier to translate the problem requirements into the code and will minimize the amount of re-engineering needed to comply with the requirements when they change.
Keywords
  • logic
  • languages
  • tabling
  • constraints
  • graph
  • analysis
  • reasoning

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References

  1. James F Allen. Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11):832-843, 1983. Google Scholar
  2. J. Arias and M. Carro. Description and Evaluation of a Generic Design to Integrate CLP and Tabled Execution. In 18th Int'l. ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming (PPDP'16). ACM Press, September 2016. Google Scholar
  3. Witold Charatonik, Supratik Mukhopadhyay, and Andreas Podelski. Constraint-based infinite model checking and tabulation for stratified clp. In Peter J. Stuckey, editor, ICLP, volume 2401 of Lecture Notes in Computer Science, pages 115-129. Springer, 2002. Google Scholar
  4. P. Chico de Guzmán, M. Carro, M. Hermenegildo, and P. Stuckey. A General Implementation Framework for Tabled CLP. In Tom Schrijvers and Peter Thiemann, editors, FLOPS'12, number 7294 in LNCS, pages 104-119. Springer Verlag, May 2012. Google Scholar
  5. P. Chico de Guzmán, M. Carro, and David S. Warren. Swapping Evaluation: A Memory-Scalable Solution for Answer-On-Demand Tabling. Theory and Practice of Logic Programming, 26th Int'l. Conference on Logic Programming (ICLP'10) Special Issue, 10 (4-6):401-416, July 2010. Google Scholar
  6. Brian Chin, Daniel von Dincklage, Vuk Ercegovac, Peter Hawkins, Mark S Miller, Franz Och, Christopher Olston, and Fernando Pereira. Yedalog: Exploring knowledge at scale. In LIPIcs-Leibniz International Proceedings in Informatics, volume 32. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2015. Google Scholar
  7. Baoqiu Cui and David Scott Warren. A System for Tabled Constraint Logic Programming. In Computational Logic, pages 478-492, 2000. Google Scholar
  8. S. Dawson, C. R. Ramakrishnan, and D. S. Warren. Practical Program Analysis Using General Purpose Logic Programming Systems - A Case Study. In Proceedings of the ACM SIGPLAN'96 Conference on Programming Language Design and Implementation, pages 117-126, New York, USA, 1996. ACM Press. Google Scholar
  9. Deductive Application Language System. URL: http://wis.cs.ucla.edu/deals/.
  10. Juliana Freire, Terrance Swift, and David Scott Warren. Beyond Depth-First Strategies: Improving Tabled Logic Programs through Alternative Scheduling. Journal of Functional and Logic Programming, 1998(3), 1998. Google Scholar
  11. Todd J Green, Dan Olteanu, and Geoffrey Washburn. Live programming in the LogicBlox system: a MetaLogiQL approach. Proceedings of the VLDB Endowment, 8(12):1782-1791, 2015. Google Scholar
  12. J. Jaffar and M.J. Maher. Constraint LP: A Survey. JLP, 19/20:503-581, 1994. Google Scholar
  13. Paris C. Kanellakis, Gabriel M. Kuper, and Peter Z. Revesz. Constraint Query Languages. J. Comput. Syst. Sci., 51(1):26-52, 1995. Google Scholar
  14. David B Kemp and Peter J Stuckey. Semantics of logic programs with aggregates. In ISLP, volume 91, pages 387-401. Citeseer, 1991. Google Scholar
  15. Pei Lee, Laks VS Lakshmanan, and Evangelos E Milios. Incremental cluster evolution tracking from highly dynamic network data. In 2014 IEEE 30th International Conference on Data Engineering, pages 3-14. IEEE, 2014. Google Scholar
  16. OWL Web Ontology Language Guide. URL: http://www.w3.org/TR/owl-guide/.
  17. Emanuele Panigati, Fabio A Schreiber, and Carlo Zaniolo. Data streams and data stream management systems and languages. In Data Management in Pervasive Systems, pages 93-111. Springer International Publishing, 2015. Google Scholar
  18. Nikolay Pelov, Marc Denecker, and Maurice Bruynooghe. Well-Founded and Stable Semantics of Logic Programs with Aggregates. TPLP, 7(3):301-353, 2007. URL: http://dx.doi.org/10.1017/S1471068406002973.
  19. Protocol Buffers. URL: https://developers.google.com/protocol-buffers/.
  20. Y.S. Ramakrishna, C.R. Ramakrishnan, I.V. Ramakrishnan, S.A. Smolka, T. Swift, and D.S. Warren. Efficient Model Checking Using Tabled Resolution. In CAV, volume 1254 of LNCS, pages 143-154. Springer Verlag, 1997. Google Scholar
  21. Resource Description Framework (RDF). URL: https://www.w3.org/RDF/.
  22. Konstantinos F. Sagonas and Peter J. Stuckey. Just Enough Tabling. In Principles and Practice of Declarative Programming, pages 78-89. ACM, August 2004. Google Scholar
  23. Tom Schrijvers, Bart Demoen, and David Scott Warren. TCHR: a Framework for Tabled CLP. TPLP, 8(4):491-526, 2008. Google Scholar
  24. Terrance Swift. Incremental tabling in support of knowledge representation and reasoning. Theory and Practice of Logic Programming, 14(4-5):553-567, 2014. Google Scholar
  25. Terrance Swift and David Scott Warren. Tabling with answer subsumption: Implementation, applications and performance. In Tomi Janhunen and Ilkka Niemelä, editors, JELIA, volume 6341 of Lecture Notes in Computer Science, pages 300-312. Springer, 2010. URL: http://dx.doi.org/10.1007/978-3-642-15675-5.
  26. H. Tamaki and M. Sato. OLD Resol. with Tabulation. In ICLP, pages 84-98. LNCS, 1986. Google Scholar
  27. David Toman. Constraint Databases and Program Analysis Using Abstract Interpretation. In CDTA, volume 1191 of LNCS, pages 246-262, 1997. Google Scholar
  28. David Toman. Memoing Evaluation for Constraint Extensions of Datalog. Constraints, 2(3/4):337-359, 1997. URL: http://dx.doi.org/10.1023/A:1009799613661.
  29. Alexander Vandenbroucke, Maciej Pirog, Benoit Desouter, and Tom Schrijvers. Tabling with Sound Answer Subsumption. Theory and Practice of Logic Programming, 32th Int'l. Conference on Logic Programming (ICLP'16), 16, October 2016. Google Scholar
  30. D. S. Warren. Memoing for Logic Programs. CACM, 35(3):93-111, 1992. Google Scholar
  31. R. Warren, M. Hermenegildo, and S. K. Debray. On the Practicality of Global Flow Analysis of Logic Programs. In JICSLP, pages 684-699. MIT Press, August 1988. Google Scholar
  32. Peter T Wood. Query languages for graph databases. ACM SIGMOD Record, 41(1):50-60, 2012. Google Scholar
  33. Carlo Zaniolo. A logic-based language for data streams. In SEBD, pages 59-66, 2012. Google Scholar
  34. Youyong Zou, Tim Finin, and Harry Chen. F-OWL: An Inference Engine for Semantic Web. In Formal Approaches to Agent-Based Systems, volume 3228 of Lecture Notes in Computer Science, pages 238-248. Springer Verlag, January 2005. Google Scholar
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