Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T03:04:48.842Z Has data issue: false hasContentIssue false
  • English
  • Français

A Logic Framework for P2P Deductive Databases

Published online by Cambridge University Press:  19 June 2019

LUCIANO CAROPRESE  and
ESTER ZUMPANO
LUCIANO CAROPRESE
Affiliation:
Department of Computer, Modelling, Electronics and Systems Engineering, University of Calabria, Rende (CS), Italy (e-mails: [email protected], [email protected])
ESTER ZUMPANO
Affiliation:
Department of Computer, Modelling, Electronics and Systems Engineering, University of Calabria, Rende (CS), Italy (e-mails: [email protected], [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a logic framework for modeling the interaction among deductive databases in a peer-to-peer (P2P) environment. Each peer joining a P2P system provides or imports data from its neighbors by using a set of mapping rules, that is, a set of semantic correspondences to a set of peers belonging to the same environment. By using mapping rules, as soon as it enters the system, a peer can participate and access all data available in its neighborhood, and through its neighborhood it becomes accessible to all the other peers in the system. A query can be posed to any peer in the system and the answer is computed by using locally stored data and all the information that can be consistently imported from the neighborhood. Two different types of mapping rules are defined: mapping rules allowing to import a maximal set of atoms not leading to inconsistency (called maximal mapping rules) and mapping rules allowing to import a minimal set of atoms needed to restore consistency (called minimal mapping rules). Implicitly, the use of maximal mapping rules states it is preferable to import as long as no inconsistencies arise; whereas the use of minimal mapping rules states that it is preferable not to import unless a inconsistency exists. The paper presents three different declarative semantics of a P2P system: (i) the Max Weak Model Semantics, in which mapping rules are used to import as much knowledge as possible from a peer’s neighborhood without violating local integrity constraints; (ii) the Min Weak Model Semantics, in which the P2P system can be locally inconsistent and the information provided by the neighbors is used to restore consistency, that is, to only integrate the missing portion of a correct, but incomplete database; (iii) the Max-Min Weak Model Semantics that unifies the previous two different perspectives captured by the Max Weak Model Semantics and Min Weak Model Semantics. This last semantics allows to characterize each peer in the neighborhood as a resource used either to enrich (integrate) or to fix (repair) the knowledge, so as to define a kind of integrate–repair strategy for each peer. For each semantics, the paper also introduces an equivalent and alternative characterization, obtained by rewriting each mapping rule into prioritized rules so as to model a P2P system as a prioritized logic program. Finally, results about the computational complexity of P2P logic queries are investigated by considering brave and cautious reasoning.

Keywords

peer data exchangeincompletenessinconsistencyintegrity constraintsrelational databasesprioritized logic program
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 1 , January 2020 , pp. 1 - 43
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abiteboul, S. and Duschka, O. M. 1998. Complexity of answering queries using materialized views. In Proc. of the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Seattle, Washington, 1–3 Jun. 1998, 254–263.Google Scholar
Abiteboul, S., Hull, R. and Vianu, V. 1995. Foundations of Databases. Addison-Wesley Publishing Company, Inc., Boston (USA).Google Scholar
Adjiman, P., Chatalic, P., Goasdoué, F., Rousset, M. and Simon, L. 2006. Distributed reasoning in a peer-to-peer setting: Application to the semantic web. Journal of Artificial Intelligence Research 25, 269–314.CrossRefGoogle Scholar
Antoniou, G. and Williams, M. 1997. Nonmonotonic Reasoning. MIT Press Cambridge, Massachusetts (USA).CrossRefGoogle Scholar
Arenas, M., Bertossi, L. and Chomicki, J. 1999. Consistent query answers in inconsistent databases. ACM, New York, USA. In Proc. of the 18th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, May 31 - June 2, Philadelphia, Pennsylvania (USA), 68–79.Google Scholar
Ariew, R. 1976. Ockham’s Razor: A Historical and Philosophical Analysis of Ockham’s Principle of Parsimony, University of Illinois, Champaign-Urbana.Google Scholar
Ben-Eliyahu, R. and Dechter, R. 1992. Propositional semantics for disjunctive logic programs. In Proc. of the Joint International Conference and Symposium on Logic Programming (JICSLP 1992), Nov. 1992, Washington, DC, 813–827.Google Scholar
Bernstein, P. A., Giunchiglia, F., Kementsietsidis, A., Mylopoulos, J., Serafini, L. and Zaihrayeu, I. 2002. Data management for peer-to-peer computing : A vision. In Proc. of the 5th Inter national Workshop on the Web and Databases, WebDB 2002, in conjunction with ACM PODS/SIGMOD 2002. Informal proceedings, Madison, Wisconsin, 6–7 Jun. 2002, 89–94.Google Scholar
Bertossi, L. E. and Bravo, L. 2004. Query answering in peer-to-peer data exchange systems. In Current Trends in Database Technology – EDBT 2004 Workshops, EDBT 2004 Workshops PhD, DataX, PIM, P2P&DB, and ClustWeb, Heraklion, Crete, Greece, 14–18 Mar. 2004. Revised Selected Papers, 476–485.Google Scholar
Bertossi, L. E. and Bravo, L. 2007. The semantics of consistency and trust in peer data exchange systems. In Proc. of the 14th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR 2007), Yerevan, Armenia, 15–19 Oct. 2007, 107–122.Google Scholar
Bertossi, L. E. and Bravo, L. 2017. Consistency and trust in peer data exchange systems. Theory and Practice of Logic Programming 17, 2, 148–204.CrossRefGoogle Scholar
Bikakis, A., Antoniou, G. and Hassapis, P. 2011. Strategies for contextual reasoning with conflicts in ambient intelligence. Knowledge and Information Systems 27, 1, 45–84.CrossRefGoogle Scholar
Binas, A. and Mcilraith, S. A. 2008. Peer-to-peer query answering with inconsistent knowledge. In Proc. of the 11th International Conference on Principles of Knowledge Representation and Reasoning (KR 2008), Sydney, Australia, 16–19 Sep. 2008, 329–339.Google Scholar
Brewka, G. and Eiter, T. 1999. Preferred answer sets for extended logic programs. Artificial Intelligence 109, 1–2, 297–356.10.1016/S0004-3702(99)00015-6CrossRefGoogle Scholar
Brewka, G. and Eiter, T. 2007. Equilibria in heterogeneous nonmonotonic multi-context systems. In 22nd AAAI Conference on Artificial Intelligence (AAAI07), Holte, R. C. and Howe, A., Eds. AAAI Press, 1–2, 385–390.Google Scholar
Brewka, G., Niemelä, I. and Truszczynski, M. 2003. Answer set optimization. In IJCAI-03, Proc. of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 9–15 Aug. 2003, 867–872.Google Scholar
Buccafurri, F., Leone, N. and Rullo, P. 2000. Enhancing Disjunctive Datalog by constraints. IEEE Transactions on Knowledge and Data Engineering 12, 5, 845–860.CrossRefGoogle Scholar
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M. and Rosati, R. 2005. Inconsistency tolerance in P2P data integration: An epistemic logic approach. In Database Programming Languages, 10th International Symposium, DBPL 2005, Trondheim, Norway, 28–29 August 2005. Revised selected papers, 90–105.Google Scholar
Calì, A., Calvanese, D., De Giacomo, G. and Lenzerini, M. 2004. Data integration under integrity constraints. Information Systems 29, 2, 147–163.CrossRefGoogle Scholar
Calì, A., Lembo, D. and Rosati, R. 2003. On the decidability and complexity of query answering over inconsistent and incomplete databases. In Proc. of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, San Diego, CA, USA, 9–12 Jun. 2003, 260–271.Google Scholar
Calimeri, F., Faber, W., Pfeifer, G. and Leone, N. 2006. Pruning operators for disjunctive logic programming systems. Fundamenta Informaticae 71, 2–3, 183–214.Google Scholar
Calvanese, D., Damaggio, E., De Giacomo, G., Lenzerini, M. and Rosati, R. 2003. Semantic data integration in P2P systems. In Databases, Information Systems, and Peer-to-Peer Computing, First International Workshop, DBISP2P, Berlin, Germany, 7–8 Sep. 2003. Revised Papers, 77–90.Google Scholar
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M. and Rosati, R. 2008. Inconsistency tolerance in P2P data integration: An epistemic logic approach. Information Systems 33, 4–5, 360–384.CrossRefGoogle Scholar
Calvanese, D., De Giacomo, G., Lenzerini, M. and Rosati, R. 2004. Logical foundations of peer-to-peer data integration. In Proc. of the 23rd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Paris, France, 14–16 Jun. 2004, 241–251.Google Scholar
Caroprese, L., Greco, S. and Zumpano, E. 2006a. A logic programming approach to querying and integrating P2P deductive databases. In Proc. of the 19th International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, Florida, USA, 11–13 May 2006, 31–36.Google Scholar
Caroprese, L., Molinaro, C. and Zumpano, E. 2006b. Integrating and querying P2P deductive databases. In Tenth International Database Engineering and Applications Symposium (IDEAS 2006), Delhi, India, 11–14 Dec. 2006, 285–290.Google Scholar
Caroprese, L. and Zumpano, E. 2007. Consistent data integration in P2P deductive databases. In 1st International Conference on Scalable Uncertainty Management (SUM 2007), Washington, DC, USA, 10–12 Oct. 2007, 230–243.Google Scholar
Caroprese, L. and Zumpano, E. 2008. Modeling cooperation in P2P data management systems. In Foundations of Intelligent Systems, 17th International Symposium, ISMIS 2008, Toronto, Canada, 20–23 May 2008, 225–235.Google Scholar
Caroprese, L. and Zumpano, E. 2011. Aggregates and priorities in P2P data management systems. In 15th International Database Engineering and Applications Symposium (IDEAS 2011), Lisbon, Portugal, 21–27 Sep. 2011, 1–7.Google Scholar
Caroprese, L. and Zumpano, E. 2012a. Handling preferences in P2P systems. In Foundations of Information and Knowledge Systems – 7th International Symposium, FoIKS 2012, Kiel, Germany, 5–9 Mar. 2012, 91–106.Google Scholar
Caroprese, L. and Zumpano, E. 2012b. Restoring consistency in P2P deductive databases. In Scalable Uncertainty Management – 6th International Conference, SUM 2012, Marburg, Germany, 17–19 Sep. 2012, 168–179.Google Scholar
Caroprese, L. and Zumpano, E. 2017a. P2P deductive databases: A system prototype. In Proc. of the 19th International Conference on Information Integration and Web-based Applications & Services, iiWAS 2017, Salzburg, Austria, 4–6 Dec. 2017, 258–265.Google Scholar
Caroprese, L. and Zumpano, E. 2017b. P2P deductive databases: Well founded semantics and dis tributed computation. In New Trends in Databases and Information Systems – ADBIS 2017 Short Papers and Workshops, AMSD, BigNovelTI, DAS, SW4CH, DC, Nicosia, Cyprus, 24–27 Sep. 2017, 91–99.Google Scholar
Chatalic, P., Nguyen, G. H. and Rousset, M. 2006. Reasoning with inconsistencies in propositional peer-to-peer inference systems. In ECAI 2006, 17th European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems (PAIS 2006), Riva del Garda, Italy, 29 Aug.–1 Sep. 2006, 352–356.Google Scholar
Delgrande, J. P., Schaub, T. and Tompits, H. 2003. A framework for compiling preferences in logic programs. Theory and Practice of Logic Programming 3, 2, 129–187.CrossRefGoogle Scholar
Eiter, T., Fink, M., Schüller, P. and Weinzierl, A. 2010. Finding explanations of inconsistency in multi-context systems. In Proc. of the 12th International Conference on Principles of Knowledge Representation and Reasoning, KR 2010, Toronto, Ontario, Canada, 9–13 May 2010.Google Scholar
Eiter, T., Fink, M., Schüller, P. and Weinzierl, A. 2014. Finding explanations of inconsistency in multi-context systems. Artificial Intelligence 216, 233–274.CrossRefGoogle Scholar
Fagin, R., Kolaitis, P. G., Miller, R. J. and Popa, L. 2005. Data exchange: Semantics and query answering. Theoretical Computer Science 336, 1, 89–124.CrossRefGoogle Scholar
Fagin, R., Kolaitis, P. G. and Popa, L. 2005. Data exchange: Getting to the core. ACM Transactions on Database Systems 30, 1, 174–210.CrossRefGoogle Scholar
Franconi, E., Kuper, G. M., Lopatenko, A. and Serafini, L. 2003. A robust logical and computational characterisation of peer-to-peer database systems. In Databases, Information Systems, and Peer-to-Peer Computing, First International Workshop, DBISP2P, Berlin, Germany, 7–8 Sep. 2003. Revised Papers, 64–76.Google Scholar
Franconi, E., Kuper, G. M., Lopatenko, A. and Zaihrayeu, I. 2004a. A distributed algorithm for robust data sharing and updates in P2P database networks. In Current Trends in Database Technology – EDBT 2004 Workshops, EDBT 2004 Workshops PhD, DataX, PIM, P2P&DB, and ClustWeb, Heraklion, Crete, Greece, 14–18 Mar. 2004. Revised Selected Papers, 446–455.Google Scholar
Franconi, E., Kuper, G. M., Lopatenko, A. and Zaihrayeu, I. 2004b. Queries and updates in the coDB peer to peer database system. In (e)Proc. of the 30th International Conference on Very Large Data Bases, Toronto, Canada, 31 Aug.–3 Sep. 2004, 1277–1280.Google Scholar
Fuxman, A., Kolaitis, P. G., Miller, R. J. and Tan, W. C. 2006. Peer data exchange. ACM Trans actions on Database Systems 31, 4, 1454–1498.CrossRefGoogle Scholar
Gelder, A. V. 1989. The alternating fixpoint of logic programs with negation. In Proc. of the 8th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Philadelphia, PA, USA29–31 Mar 1989, 1–10.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proc. of the 5th International Conference and Symposium on Logic Programming, Seattle, Washington, 15–19 Aug. 1988 (2 Volumes), 1070–1080.Google Scholar
Greco, G., Greco, S. and Zumpano, E. 2003. A logical framework for querying and repairing inconsistent databases. IEEE Transactions on Knowledge and Data Engineering 15, 6, 1389–1408.CrossRefGoogle Scholar
Gribble, S. D., Halevy, A. Y., Ives, Z. G., Rodrig, M. and Suciu, D. 2001. What can database do for peer-to-peer? In Proc. of the 4th International Workshop on the Web and Databases, WebDB 2001, Santa Barbara, California, USA, 24–25 May 2001, in conjunction with ACM PODS/SIGMOD 2001. Informal proceedings, 31–36.Google Scholar
Halevy, A. Y., Ives, Z. G., Suciu, D. and Tatarinov, I. 2003. Schema mediation in peer data man agement systems. In Proc. of the 19th International Conference on Data Engineering, Bangalore, India, 5–8 Mar. 2003, 505–516.Google Scholar
Halevy, A. Y., Ives, Z. G., Suciu, D. and Tatarinov, I. 2005. Schema mediation for large-scale semantic data sharing. VLDB Journal, 14, 1, 68–83.CrossRefGoogle Scholar
Lenzerini, M. 2002. Data integration: A theoretical perspective. In Proc. of the 21st ACM SIGACT SIGMOD-SIGART Symposium on Principles of Database Systems, Madison, Wisconsin, USA, 3–5 Jun. 2002, 233–246.Google Scholar
Leone, N., Greco, G., Ianni, G., Lio, V., Terracina, G., Eiter, T., Faber, W., Fink, M., Gottlob, G., Rosati, R., Lembo, D., Lenzerini, M., Ruzzi, M., Kalka, E., Nowicki, B. and Staniszkis, W. 2005. The INFOMIX system for advanced integration of incomplete and inconsistent data. In Proc. of the ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, 14–16 Jun. 2005, 915–917.Google Scholar
Lonc, Z. and Truszczynski, M. 2000. On the problem of computing the well-founded semantics. In 1st International Conference on Computational Logic – CL 2000, London, UK, 24–28 Jul. 2000, 673–687.Google Scholar
Madhavan, J. and Halevy, A. Y. 2003. Composing mappings among data sources. In VLDB 2003, Proc. 29th International Conference on Very Large Data Bases, Berlin, Germany, 9–12 Sep. 2003, 572–583.Google Scholar
Marek, V. W. and Truszczynski, M. 1993. Nonmonotonic Logic - Context-dependent Reasoning. Artificial Intelligence, Springer 1993, ISBN 978-3-540-56448-5, pp. I–XII, 1–417.Google Scholar
Nute, D. 1994. Defeasible logic. In Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning. Oxford University Press, Inc. New York, NY, USA, 353–395.Google Scholar
Papadimitriou, C. H. 1994. Computational Complexity. Addison-Wesley. ISBN 978-0-201-53082-7, pp. I–XV, 1–523.Google Scholar
Reiter, R. 1987. A theory of diagnosis from first principles. Artificial Intelligence 32, 1, 57–95.10.1016/0004-3702(87)90062-2CrossRefGoogle Scholar
Rousset, M., Adjiman, P., Chatalic, P., Goasdoué, F. and Simon, L. 2006. Somewhere: A scalable peer-to-peer infrastructure for querying distributed ontologies. In On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE, OTM Confederated International Conferences, CoopIS, DOA, GADA, and ODBASE 2006, Montpellier, France, 29 Oct.–3 Nov. 2006, Part I.Google Scholar
Sakama, C. and Inoue, K. 2000. Prioritized logic programming and its application to commonsense reasoning. Artificial Intelligence 123, 1–2, 185–222.CrossRefGoogle Scholar
Tatarinov, I. and Halevy, A. Y. 2004. Efficient query reformulation in peer-data management systems. In Proc. of the ACM SIGMOD International Conference on Management of Data, Paris, France, 13–18 Jun. 2004, 539–550.Google Scholar