Dionysis Athanasopoulos
Apostolos Zarras
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Abstract
Schema integration has been a long-standing challenge for the data-engineering community that has received steady attention over the past three decades. General-purpose integration approaches construct unified schemas that encompass all schema elements. Schema integration has been revisited in the past decade in service-oriented computing since the input/output data-types of service interfaces are heterogeneous XML schemas. However, service integration differs from the traditional integration problem, since it should generalize schemas (mining abstract data-types) instead of unifying all schema elements. To mine well-formed abstract data-types, the fundamental Liskov Substitution Principle (LSP), which generally holds between abstract data-types and their subtypes, should be followed. However, due to the heterogeneity of service data-types, the strict employment of LSP is not usually feasible. On top of that, XML offers a rich type system, based on which data-types are defined via combining type patterns (e.g., composition, aggregation). The existing integration approaches have not dealt with the challenges of a defining subtyping relation between XML type patterns. To address these challenges, we propose a relaxed version of LSP between XML type patterns and an automated generalization process for mining abstract XML data-types. We evaluate the effectiveness and the efficiency of the process on the schemas of two datasets against two representative state-of-the-art approaches.
- A. Doan and A. Y. Halevy. 2005. Semantic integration research in the database community: A brief survey. AI Magazine 26, 1 (2005), 83--94. Google Scholar
Digital Library
- Carlo Batini, Maurizio Lenzerini, and Shamkant B. Navathe. 1986. A comparative analysis of methodologies for database schema integratison. ACM Computings Surveys 18, 4 (1986), 323--364. Google ScholarDigital Library
- R. Pottinger and P. A. Bernstein. 2003. Merging models based on given correspondences. In Proceedings of the International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Berlin, 826--873. Google ScholarDigital Library
- T. Erl. 2005. Service-Oriented Architecture: Concepts, Technology, and Design. Prentice Hall. Google ScholarDigital Library
- D. Athanasopoulos, A. Zarras, P. Vassiliadis, and V. Issarny. 2011. Mining service abstractions. In Proceedings of the International Conference on Software Engineering. IEEE, HI, Hawaii, 944--947. Google ScholarDigital Library
- X. Liu and H. Liu. 2012. Automatic abstract service generation from web service communities. In Proceedings of the International Conference on Web Services. IEEE, HI, Hawaii, 154--161. Google ScholarDigital Library
- B. Liskov and J. M. Wing. 1994. A behavioural notion of subtyping. ACM Transactions on Programming Languages and Systems 16, 6 (1994), 1811--1841. Google ScholarDigital Library
- Erhard Rahm, Hong Hai Do, and Sabine Massmann. 2004. Matching large XML schemas. SIGMOD Record 33, 4 (2004). ACM, 26--31. Google ScholarDigital Library
- K. Saleem, Z. Bellahsene, and E. Hunt. 2008. PORSCHE: Performance ORiented SCHEma mediation. Information Systems 33, 7--8 (2008). Elsevier, 637--657. Google ScholarDigital Library
- A. Y. Halevy, A. Rajaraman, and J. J. Ordille. 2006. Data integration: The teenage years. In Proceedings of the International Conference on Very Large Data Bases. ACM, Seoul, 9--16. Google ScholarDigital Library
- R. Pottinger and P. A. Bernstein. 2008. Schema merging and mapping creation for relational sources. In Proceedings of the International Conference on Extending Database Technology: Advances in Database Technology. ACM, Nantes, 73--84. Google ScholarDigital Library
- C. Parent and S. Spaccapietra. 1998. Issues and approaches of database integration. Communications of the ACM 41, 5 (1998), 166--178. Google ScholarDigital Library
- Xiang Li. 2012. Constraint-Driven Schema Merging. Ph.D. Dissertation. RWTH Aachen University.Google Scholar
- A. Baqasah, E. Pardede, and J. W. Rahayu. 2014. A new approach for meaningful XML schema merging. In Proceedings of the International Conference on Information Integration and Web-based Applications 8 Services. ACM, Hanoi, 430--439. Google ScholarDigital Library
- H. Ma, K.-D. Schewe, B. Thalheim, and J. Zhao. 2005. View integration and cooperation in databases, data warehouses and web information systems. Journal on Data Semantics. Springer, 213--249. Google ScholarDigital Library
- V. Kashyap and A. P. Sheth. 1996. Semantic and schematic similarities between database objects: A context-based approach. The VLDB Journal 5, 4 (1996). Springer, 276--304. Google ScholarDigital Library
- X. Li and C. Quix. 2011. Merging relational views: A minimization approach. In Proceedings of the International Conference on Conceptual Modeling. Springer, Brussels, 379--392. Google ScholarDigital Library
- M. Arenas, J. Pérez, J. L. Reutter, and C. Riveros. 2010. Foundations of schema mapping management. In Proceedings of the ACM Symposium on Principles of Database Systems. ACM, Indianapolis, Indiana, 227--238. Google ScholarDigital Library
- P. A. Bernstein, S. Melnik, M. Petropoulos, and C. Quix. 2004. Industrial-strength schema matching. ACM SIGMOD Record 33, 4 (2004), 38--43. Google ScholarDigital Library
- A. Radwan, L. Popa, I. R. Stanoi, and A. Younis. 2009. Top-k generation of integrated schemas based on directed and weighted correspondences. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, Providence, Rhode Island, 641--654. Google ScholarDigital Library
- A. D. Sarma, X. Dong, and A. Halevy. 2008. Bootstrapping pay-as-you-go data integration systems. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, Vancouver, 861--874. Google ScholarDigital Library
- S. Melnik, E. Rahm, and P. A. Bernstein. 2003. Rondo: A programming platform for generic model management. In Proceedings of the ACM SIGMOD International conference on Management of Data. ACM, San Diego, California, 193--204. Google ScholarDigital Library
- Aída Jiménez, Fernando Berzal, and Juan Carlos Cubero Talavera. 2010. Frequent tree pattern mining: A survey. Intelligent Data Analysis 14, 6 (2010). IOS Press, 603--622. Google ScholarDigital Library
- M. J. Zaki. 2005. Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae 66, 1--2 (2005). IOS Press, 33--52. Google ScholarDigital Library
- Y. Chi, R. R. Muntz, S. Nijssen, and J. N. Kok. 2005. Frequent subtree mining -- An overview. Fundamenta Informaticae 66, 1--2 (2005). IOS Press, 161--198. Google ScholarDigital Library
- M. J. Zaki. 2005. Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 17, 8 (2005), 1021--1035. Google ScholarDigital Library
- J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu. 2004. Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Transactions on Knowledge and Data Engineering 16, 11 (2004), 1424--1440. Google ScholarDigital Library
- X. Yan, J. Han, and R. Afshar. 2003. CloSpan: Mining closed sequential patterns in large databases. In Proceedings of the SIAM International Conference on Data Mining. SIAM, San Francisco, 166--177.Google Scholar
- C. Wang, M. Hong, J. Pei, H. Zhou, W. Wang, and B. Shi. 2004. Efficient pattern-growth methods for frequent tree pattern mining. In Proceedings of the Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Springer, Sydney, 441--451.Google Scholar
- L. Zou, Y. Lu, H. Zhang, and R. Hu. 2006. PrefixTreeESpan: A pattern growth algorithm for mining embedded subtrees. In Proceedings of the International Conference on Web Information Systems Engineering. Springer, Wuhan, 499--505. Google ScholarDigital Library
- J. I. Chowdhury and R. Nayak. 2014. BEST: An Efficient Algorithm for Mining Frequent Unordered Embedded Subtrees. In Proceedings of the Pacific Rim International Conference on Artificial Intelligence. Springer, Gold Coast, 459--471.Google Scholar
- E. Rahm and P. A. Bernstein. 2001. A survey of approaches to automatic schema matching. VLDB Journal 10, 4 (2001). Springer, 334--350. Google ScholarDigital Library
- Z. Bellahsene, A. Bonifati, and E. Rahm (Eds.). 2011. Schema Matching and Mapping. Springer. Google ScholarDigital Library
- P. Shvaiko and J. Euzenat. 2013. Ontology matching: State of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering 25, 1 (2013), 158--176. Google ScholarDigital Library
- M. Hamdaqa and L. Tahvildari. 2014. Prison break: A generic schema matching solution to the cloud vendor lock-in problem. In Proceedings of the International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems. IEEE, Victoria, British Columbia, 37--46. Google ScholarDigital Library
- F. Duchateau, Z. Bellahsene, and M. Roche. 2007. A context-based measure for discovering approximate semantic matching between schema elements. In Proceedings of the International Conference on Research Challenges in Information Science. IEEE, Ouarzazate, 9--20.Google Scholar
- F. Duchateau, Z. Bellahsene, M. Roantree, and M. Roche. 2007. Poster session: An indexing structure for automatic schema matching. In Proceedings of the IEEE International Conference on Data Engineering Workshop. IEEE, Istanbul, 485--491. Google ScholarDigital Library
- P. De Meo, G. Quattrone, G. Terracina, and D. Ursino. 2006. Integration of XML schemas at various “severity” levels. Information Systems 31, 6 (2006). Elsevier, 397--434. Google ScholarDigital Library
- F. Duchateau, Z. Bellahsene, and M. Roche. 2007. BMatch: A semantically context-based tool enhanced by an indexing structure to accelerate schema matching. In Journées Bases de Données Avancées. IEEE, Marseille, 1--20.Google Scholar
- W. Hu, Y. Qu, and G. Cheng. 2008. Matching large ontologies: A divide-and-conquer approach. Data 8 Knowledge Engineering 67, 1 (2008). Elsevier, 140--160. Google ScholarDigital Library
- H. H. Do and E. Rahm. 2002. COMA -- A system for flexible combination of schema matching approaches. In Proceedings of the International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Hong Kong, 610--621. Google ScholarDigital Library
- H. H. Do and E. Rahm. 2007. Matching large schemas: Approaches and evaluation. Information Systems 32, 6 (2007). Elsevier, 857--885. Google ScholarDigital Library
- J. Madhavan, P. A. Bernstein, and E. Rahm. 2001. Generic schema matching with CUPID. In Proceedings of the International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Roma, 49--58. Google ScholarDigital Library
- A. Algergawy, E. Schallehn, and G. Saake. 2009. Improving XML schema matching performance using Prüfer sequences. Data and Knowledge Engineering 68, 8 (2009). Elsevier, 728--747. Google ScholarDigital Library
- M. Lee, L. H. Yang, W. Hsu, and X. Yang. 2002. XClust: Clustering XML schemas for effective integration. In Proceedings of the ACM International Conference on Information and Knowledge Management. ACM, McLean, Virginia, 292--299. Google ScholarDigital Library
- F. Giunchiglia, P. Shvaiko, and M. Yatskevich. 2004. S-Match: An algorithm and an implementation of semantic matching. In Proceedings of the European Semantic Web Symposium. Springer, Heraklion, Crete, 61--75.Google Scholar
- R. Nayak and W. Iryadi. 2007. XML schema clustering with semantic and hierarchical similarity measures. Knowledge-Based Systems 20, 4 (2007). ACM, 336--349. Google ScholarDigital Library
- A. Algergawy, R. Nayak, and G. Saake. 2010. Element similarity measures in XML schema matching. Information Sciences 180, 24 (2010). Elsevier, 4975--4998. Google ScholarDigital Library
- J. Kim, Y. Peng, N. Ivezik, and J. Shin. 2011. An optimization approach for semantic-based XML schema matching. International Journal of Trade, Economics, and Finance 2, 1 (2011). IACSIT Press, 78--86.Google Scholar
- M. M. Meijer. 2008. On a method for XML schema matching. In Proceedings of the 8th Twente Student Conference on Information Technology. University of Twente, Twente, 1--10.Google Scholar
- I. F. Cruz, F. P. Antonelli, and C. Stroe. 2009. AgreementMaker: Efficient matching for large real-world schemas and ontologies. VLDB Endowment 2, 2 (2009). ACM, 1586--1589. Google ScholarDigital Library
- Y. R. Jean-Mary, E. P. Shironoshita, and M. R. Kabuka. 2009. Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web 7, 3 (2009). Elsevier, 235--251. Google ScholarDigital Library
- P. Lambrix and H. Tan. 2006. SAMBO -- A system for aligning and merging biomedical ontologies. Web Semantics: Science, Services and Agents on the World Wide Web 4, 3 (2006). Elsevier, 196--206. Google ScholarDigital Library
- K. Voigt. 2011. Structural Graph-Based Metamodel Matching. Ph.D. Dissertation. Technical University of Dresden, Department of Computer Science.Google Scholar
- C. H. Papadimitriou. 1994. Computational Complexity. Addison-Wesley.Google Scholar
- D. Aumueller, H. H. Do, S. Massmann, and E. Rahm. 2005. Schema and ontology matching with COMA++. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Baltimore, MD. 906--908. Google ScholarDigital Library
- P. Bille. 2005. A survey on tree edit distance and related problems. Theoretical Computer Science 337, 1--3 (2005). Elsevier, 217--239. Google ScholarDigital Library
- S. Melnik, H. Garcia-Molina, and E. Rahm. 2002. Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proceedings of the International Conference on Data Engineering. IEEE, San Jose, California, 117--128. Google ScholarDigital Library
- G. Valiente. 2002. Algorithms on Trees and Graphs. Springer. Google ScholarDigital Library
- T. H. Cormen, C. Stein, R. L. Rivest, and C. E. Leiserson. 2001. Introduction to Algorithms (2nd ed.). McGraw-Hill Higher Education. Google ScholarDigital Library
- T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa. 2002. Efficient substructure discovery from large semi-structured data. In Proceedings of the SIAM International Conference on Data Mining. SIAM, Maebashi City, 158--174.Google Scholar
- M. J. Zaki. 2002. Efficiently mining frequent trees in a forest. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, AB, 71--80. Google ScholarDigital Library
- P. Plebani and B. Pernici. 2009. URBE: Web service retrieval based on similarity evaluation. IEEE Transactions on Knowledge and Data Engineering 21, 11 (2009), 1629--1642. Google ScholarDigital Library
- E. Stroulia and Y. Wang. 2005. Structural and semantic matching for assessing web-service similarity. International Journal of Cooperative Information Systems (2005). World Scientific, 407--438.Google Scholar
- G. A. Miller. 1995. WordNet: A lexical database for english. ACM Communications 38, 11 (1995), 39--41. Google ScholarDigital Library
- T. Pedersen, S. Patwardhan, and J. Michelizzi. 2004. WordNet: : Similarity -- Measuring the relatedness of concepts. In Proceedings of the National Conference on Innovative Applications of Artificial Intelligence. AAAI Press, San Jose, California, 1024--1025. Google ScholarDigital Library
- R. Burkard, M. Dell’Amico, and S. Martello. 2009. Assignment Problems. Society for Industrial and Applied Mathematics, USA. SIAM. Google ScholarDigital Library
- A. V. Aho, J. E. Hopcroft, and J. Ullman. 1983. Data Structures and Algorithms. Addison-Wesley. Google ScholarDigital Library
- F. Duchateau and Z. Bellahsene. 2010. Measuring the Quality of an Integrated Schema. In Proceedings of the International Conference on Conceptual Modeling. Springer, Vancouver, BC, 261--273. Google ScholarDigital Library
- R. A. Baeza-Yates and B. A. Ribeiro-Neto. 1999. Modern Information Retrieval. ACM Press/Addison-Wesley. Google ScholarDigital Library
- D. Zhang and J. P. Tsai. 2007. Advances in Machine Learning Applications in Software Engineering. IGI Global, Hershey, PA, USA. Google ScholarDigital Library
Index Terms
- Mining Abstract XML Data-Types
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