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Title
Abstract
Introduction
Semantics for P2P Data Integration Systems
Limitations of First-Order Approaches
Future Research Directions and Conclusions
References

Data management in peer-to-peer data integration systems

Profile image of Diego CalvaneseDiego Calvanese

2006

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25 pages

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Abstract

Abstract. Decentralized data management has been addressed during the years by means of several technical solutions, ranging from distributed DBMSs, to mediatorbased data integration systems. Recently, such an issue has been investigated in the context of Peer-to-Peer (P2P) architectures.

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References (70)

  1. K. Aberer, M. Punceva, M. Hauswirth, and R. Schmidt. Improving data access in P2P sys- tems. IEEE Internet Computing, 2002.
  2. S. Abiteboul and O. Duschka. Complexity of answering queries using materialized views. In Proc. of the 17th ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS'98), pages 254-265, 1998.
  3. M. Arenas, P. Barcelo, R. Fagin, and L. Libkin. Locally consistent transformations and query answering in data exchange. In Proc. of the 23rd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2004), pages 229-240, 2004.
  4. M. Arenas, L. E. Bertossi, and J. Chomicki. Consistent query answers in inconsistent databases. In Proc. of the 18th ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS'99), pages 68-79, 1999.
  5. D. Beneventano, S. Bergamaschi, F. Guerra, and M. Vincini. Querying a super-peer in a schema-based super-peer network. In Proc. of the 3rd VLDB Int. Workshop on Databases, Information Systems and Peer-to-Peer Computing (DBISP2P 2005), 2005.
  6. P. A. Bernstein, F. Giunchiglia, A. Kementsietsidis, J. Mylopoulos, L. Serafini, and I. Za- ihrayeu. Data management for peer-to-peer computing: A vision. In Proc. of the 5th Int. Workshop on the Web and Databases (WebDB 2002), 2002.
  7. M. Bouzeghoub and M. Lenzerini. Introduction to the special issue on data extraction, clean- ing, and reconciliation. Information Systems, 26(8):535-536, 2001.
  8. L. Bravo and L. Bertossi. Logic programming for consistently querying data integration systems. In Proc. of the 18th Int. Joint Conf. on Artificial Intelligence (IJCAI 2003), pages 10-15, 2003.
  9. L. Bravo and L. Bertossi. Disjunctive deductive databases for computing certain and consis- tent answers to queries from mediated data integration systems. Journal of Applied Logic - Special Issue on Logic-based Methods for Information Integration, 3(2):329-367, 2005.
  10. A. Calì, D. Calvanese, G. De Giacomo, and M. Lenzerini. Data integration under integrity constraints. Information Systems, 29:147-163, 2004.
  11. A. Calì, D. Calvanese, G. De Giacomo, M. Lenzerini, P. Naggar, and F. Vernacotola. IBIS: Semantic data integration at work. In Proc. of the 15th Int. Conf. on Advanced Information Systems Engineering (CAiSE 2003), pages 79-94, 2003.
  12. A. Calì, D. Lembo, and R. Rosati. On the decidability and complexity of query answering over inconsistent and incomplete databases. In Proc. of the 22nd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2003), pages 260-271, 2003.
  13. A. Calì, D. Lembo, and R. Rosati. Query rewriting and answering under constraints in data in- tegration systems. In Proc. of the 18th Int. Joint Conf. on Artificial Intelligence (IJCAI 2003), pages 16-21, 2003.
  14. A. Calì, D. Lembo, R. Rosati, and M. Ruzzi. Experimenting data integration with DIS@DIS. In Proc. of the 16th Int. Conf. on Advanced Information Systems Engineering (CAiSE 2004), volume 3084 of Lecture Notes in Computer Science, pages 51-56. Springer, 2004.
  15. D. Calvanese, E. Damaggio, G. De Giacomo, M. Lenzerini, and R. Rosati. Semantic data integration in P2P systems. In Proc. of the Int. Workshop on Databases, Information Systems and Peer-to-Peer Computing (DBISP2P 2003), 2003.
  16. D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, and R. Rosati. What to ask to a peer: Ontology-based query reformulation. In Proc. of the 9th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR 2004), pages 469-478, 2004.
  17. D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, and R. Rosati. DL-Lite: Tractable description logics for ontologies. In Proc. of the 20th Nat. Conf. on Artificial Intelligence (AAAI 2005), pages 602-607, 2005.
  18. D. Calvanese, G. De Giacomo, M. Lenzerini, D. Nardi, and R. Rosati. Description logic framework for information integration. In Proc. of the 6th Int. Conf. on Principles of Knowl- edge Representation and Reasoning (KR'98), pages 2-13, 1998.
  19. D. Calvanese, G. De Giacomo, M. Lenzerini, D. Nardi, and R. Rosati. Data integration in data warehousing. Int. J. of Cooperative Information Systems, 10(3):237-271, 2001.
  20. D. Calvanese, G. De Giacomo, M. Lenzerini, and R. Rosati. Logical foundations of peer- to-peer data integration. In Proc. of the 23rd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2004), pages 241-251, 2004.
  21. D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Y. Vardi. Rewriting of regular expres- sions and regular path queries. J. of Computer and System Sciences, 64(3):443-465, 2002.
  22. D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Y. Vardi. View-based query processing: On the relationship between rewriting, answering and losslessness. In Proc. of the 10th Int. Conf. on Database Theory (ICDT 2005), volume 3363 of Lecture Notes in Computer Science, pages 321-336. Springer, 2005.
  23. D. Calvanese, G. De Giacomo, and M. Y. Vardi. Decidable containment of recursive queries. Theoretical Computer Science, 336(1):33-56, 2005.
  24. M. J. Carey, L. M. Haas, P. M. Schwarz, M. Arya, W. F. Cody, R. Fagin, M. Flickner, A. Lu- niewski, W. Niblack, D. Petkovic, J. Thomas, J. H. Williams, and E. L. Wimmers. Towards heterogeneous multimedia information systems: The Garlic approach. In Proc. of the 5th Int. Workshop on Research Issues in Data Engineering -Distributed Object Management (RIDE-DOM'95), pages 124-131. IEEE Computer Society Press, 1995.
  25. M. A. Casanova, R. Fagin, and C. H. Papadimitriou. Inclusion dependencies and their in- teraction with functional dependencies. J. of Computer and System Sciences, 28(1):29-59, 1984.
  26. T. Catarci and M. Lenzerini. Representing and using interschema knowledge in cooperative information systems. J. of Intelligent and Cooperative Information Systems, 2(4):375-398, 1993.
  27. A. K. Chandra and M. Y. Vardi. The implication problem for functional and inclusion depen- dencies is undecidable. SIAM J. on Computing, 14(3):671-677, 1985.
  28. S. S. Chawathe, H. Garcia-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J. D. Ullman, and J. Widom. The TSIMMIS project: Integration of heterogeneous information sources. In Proc. of the 10th Meeting of the Information Processing Society of Japan (IPSJ'94), pages 7-18, 1994.
  29. B. F. Chellas. Modal Logic: An introduction. Cambridge University Press, 1980.
  30. J. Chomicki, J. Marcinkowski, and S. Staworko. Computing consistent query answers using conflict hypergraphs. In Proc. of the 13th Int. Conf. on Information and Knowledge Manage- ment (CIKM 2004), pages 417-426, 2004.
  31. J. Chomicki, J. Marcinkowski, and S. Staworko. Hippo: a system for computing consistent query answers to a class of SQL queries. In Proc. of the 9th Int. Conf. on Extending Database Technology (EDBT 2004), pages 841-844. Springer, 2004.
  32. I. Clarke, S. G. Miller, T. W. Hong, O. Sandberg, and B. Wiley. Freenet: A distributed anony- mous information storage and retrieval system. In Proc. of the Int. Workshop on Design Issues in Anonymity and Unobservability (DIAU 2000), 2000.
  33. O. M. Duschka, M. R. Genesereth, and A. Y. Levy. Recursive query plans for data integration. J. of Logic Programming, 43(1):49-73, 2000.
  34. R. Fagin, J. Y. Halpern, Y. Moses, and M. Y. Vardi. Reasoning about Knowledge. The MIT Press, 1995.
  35. R. Fagin, P. G. Kolaitis, R. J. Miller, and L. Popa. Data exchange: Semantics and query answering. In Proc. of the 9th Int. Conf. on Database Theory (ICDT 2003), pages 207-224, 2003.
  36. R. Fagin, P. G. Kolaitis, and L. Popa. Data exchange: Getting to the core. In Proc. of the 22nd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2003), pages 90-101, 2003.
  37. E. Franconi, G. Kuper, A. Lopatenko, and L. Serafini. A robust logical and computational characterisation of peer-to-peer database systems. In Proc. of the VLDB International Work- shop On Databases, Information Systems and Peer-to-Peer Computing (DBISP2P 2003), 2003.
  38. A. Fuxman, E. Fazli, and R. J. Miller. ConQuer: Efficient management of inconsistent databases. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 155-166, 2005.
  39. A. Fuxman, P. G. Kolaitis, R. Miller, and W. C. Tan. Peer data exchange. In Proc. of the 24rd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2005), pages 160-171, 2005.
  40. A. Fuxman and R. J. Miller. First-order query rewriting for inconsistent databases. In Proc. of the 10th Int. Conf. on Database Theory (ICDT 2005), volume 3363 of LNCS, pages 337-351. Springer, 2005.
  41. H. Garcia-Molina, Y. Papakonstantinou, D. Quass, A. Rajaraman, Y. Sagiv, J. D. Ullman, V. Vassalos, and J. Widom. The TSIMMIS approach to mediation: Data models and lan- guages. J. of Intelligent Information Systems, 8(2):117-132, 1997.
  42. M. Gelfond and V. Lifschitz. The stable model semantics for logic programming. In Proc. of the 5th Logic Programming Symposium, pages 1070-1080. The MIT Press, 1988.
  43. M. R. Genereseth, A. M. Keller, and O. M. Duschka. Infomaster: An information integration system. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 539-542, 1997.
  44. C. H. Goh, S. Bressan, S. E. Madnick, and M. D. Siegel. Context interchange: New features and formalisms for the intelligent integration of information. ACM Trans. on Information Systems, 17(3):270-293, 1999.
  45. G. Grahne and A. O. Mendelzon. Tableau techniques for querying information sources through global schemas. In Proc. of the 7th Int. Conf. on Database Theory (ICDT'99), volume 1540 of Lecture Notes in Computer Science, pages 332-347. Springer, 1999.
  46. G. Greco, S. Greco, and E. Zumpano. A logical framework for querying and repairing in- consistent databases. IEEE Trans. on Knowledge and Data Engineering, 15(6):1389-1408, 2003.
  47. S. Gribble, A. Halevy, Z. Ives, M. Rodrig, and D. Suciu. What can databases do for peer-to- peer? In Proc. of the 4th Int. Workshop on the Web and Databases (WebDB 2001), 2001.
  48. L. Grieco, D. Lembo, M. Ruzzi, and R. Rosati. Consistent query answering under key and exclusion dependencies: Algorithms and experiments. In Proc. of the 14th Int. Conf. on Information and Knowledge Management (CIKM 2005), pages 792-799, 2005.
  49. J. Gryz. Query rewriting using views in the presence of functional and inclusion dependen- cies. Information Systems, 24(7):597-612, 1999.
  50. A. Halevy, Z. Ives, D. Suciu, and I. Tatarinov. Schema mediation in peer data management systems. In Proc. of the 19th IEEE Int. Conf. on Data Engineering (ICDE 2003), pages 505-516, 2003.
  51. J. Heflin and J. Hendler. A portrait of the Semantic Web in action. IEEE Intelligent Systems, 16(2):54-59, 2001.
  52. R. Hull, M. Benedikt, V. Christophides, and J. Su. E-services: a look behind the curtain. In Proc. of the 22nd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2003), pages 1-14. ACM Press and Addison Wesley, 2003.
  53. T. Kirk, A. Y. Levy, Y. Sagiv, and D. Srivastava. The Information Manifold. In Proceedings of the AAAI 1995 Spring Symp. on Information Gathering from Heterogeneous, Distributed Enviroments, pages 85-91, 1995.
  54. C. Koch. Query rewriting with symmetric constraints. In Proc. of the 2nd Int. Symp. on Foundations of Information and Knowledge Systems (FoIKS 2002), volume 2284 of Lecture Notes in Computer Science, pages 130-147. Springer, 2002.
  55. M. Lenzerini. Data integration: A theoretical perspective. In Proc. of the 21st ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2002), pages 233-246, 2002.
  56. N. Leone, T. Eiter, W. Faber, M. Fink, G. Gottlob, G. Greco, E. Kalka, G. Ianni, D. Lembo, M. Lenzerini, V. Lio, B. Nowicki, R. Rosati, M. Ruzzi, W. Staniszkis, and G. Terracina. The INFOMIX system for advanced integration of incomplete and inconsistent data. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 915-917, 2005.
  57. H. J. Levesque and G. Lakemeyer. The Logic of Knowledge Bases. The MIT Press, 2001.
  58. A. Y. Levy. Logic-based techniques in data integration. In J. Minker, editor, Logic Based Artificial Intelligence. Kluwer Academic Publisher, 2000.
  59. A. Y. Levy, A. Rajaraman, and J. J. Ordille. Querying heterogenous information sources using source descriptions. In Proc. of the 22nd Int. Conf. on Very Large Data Bases (VLDB'96), 1996.
  60. A. Y. Levy, D. Srivastava, and T. Kirk. Data model and query evaluation in global information systems. J. of Intelligent Information Systems, 5:121-143, 1995.
  61. J. Madhavan and A. Y. Halevy. Composing mappings among data sources. In Proc. of the 29th Int. Conf. on Very Large Data Bases (VLDB 2003), pages 572-583, 2003.
  62. J. C. Mitchell. The implication problem for functional and inclusion dependencies. Informa- tion and Control, 56:154-173, 1983.
  63. M. P. Papazoglou, B. J. Kramer, and J. Yang. Leveraging Web-services and peer-to-peer networks. In Proc. of the 15th Int. Conf. on Advanced Information Systems Engineering (CAiSE 2003), pages 485-501, 2003.
  64. R. Pottinger and A. Y. Levy. A scalable algorithm for answering queries using views. In Proc. of the 26th Int. Conf. on Very Large Data Bases (VLDB 2000), pages 484-495, 2000.
  65. L. Serafini and C. Ghidini. Using wrapper agents to answer queries in distributed information systems. In Proc. of the 1st Int. Conf. on Advances in Information Systems (ADVIS-2000), volume 1909 of Lecture Notes in Computer Science. Springer, 2000.
  66. A. Tomasic, L. Raschid, and P. Valduriez. Scaling access to heterogeneous data sources with DISCO. IEEE Trans. on Knowledge and Data Engineering, 10(5):808-823, 1998.
  67. J. D. Ullman. Information integration using logical views. Theoretical Computer Science, 239(2):189-210, 2000.
  68. S. R. Waterhouse, D. M. Doolin, G. Kan, and Y. Faybishenko. Distributed search in P2P networks. IEEE Internet Computing, 6(1):68-72, 2002.
  69. B. Yang and H. Garcia-Molina. Designing a super-peer network. In Proc. of the 19th IEEE Int. Conf. on Data Engineering (ICDE 2003), 2003.
  70. Z. Zhang and A. Mendelzon. Authorization views and conditional query containment. In Proc. of the 10th Int. Conf. on Database Theory (ICDT 2005), volume 3363 of Lecture Notes in Computer Science, pages 259-273. Springer, 2005.

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