ABSTRACT
Tools that automatically generate queries are useful when schemas are hard to understand due to size or complexity. Usually, these tools find minimal tree patterns that contain a given set (or bag) of labels. The labels could be, for example, XML tags or relation names. The only restriction is that, in a tree pattern, adjacent labels must be among some specified pairs. A more expressive framework is developed here, where a schema is a mapping of each label to a collection of bags of labels. A tree pattern conforms to the schema if for all nodes v, the bag comprising the labels of the neighbors is contained in one of the bags to which the label of v is mapped. The problem at hand is to find a minimal tree pattern that conforms to the schema and contains a given bag of labels. This problem is NP-hard even when using the simplest conceivable language for describing schemas. In practice, however, the set of labels is small, so efficiency is realized by means of an algorithm that is fixed-parameter tractable (FPT). Two languages for specifying schemas are discussed. In the first, one expresses pairwise mutual exclusions between labels. Though W[1]-hardness (hence, unlikeliness of an FPT algorithm) is shown, an FPT algorithm is described for the case where the mutual exclusions form a circular-arc graph (e.g., disjoint cliques). The second language is that of regular expressions, and for that another FPT algorithm is described.
- H. Achiezra, K. Golenberg, B. Kimelfeld, and Y. Sagiv. Exploratory keyword search on data graphs. In SIGMOD Conference, pages 1163--1166. ACM, 2010. Google Scholar
Digital Library
- C. Beeri and T. Milo. Schemas for integration and translation of structured and semi-structured data. In ICDT, pages 296--313. Springer, 1999. Google Scholar
Digital Library
- G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. In ICDE, pages 431--440, 2002. Google Scholar
Digital Library
- K. S. Booth and G. S. Lueker. Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms. J. Comput. Syst. Sci., 13(3):335--379, 1976. Google Scholar
Digital Library
- S. Cohen, Y. Kanza, B. Kimelfeld, and Y. Sagiv. Interconnection semantics for keyword search in XML. In CIKM, pages 389--396. ACM, 2005. Google Scholar
Digital Library
- R. G. Downey and M. R. Fellows. Parameterized Complexity. Monographs in Computer Science. Springer, 1999.Google Scholar
Digital Library
- S. Dreyfus and R. Wagner. The Steiner problem in graphs. Networks, 1:195--207, 1972.Google Scholar
- K. Golenberg, B. Kimelfeld, and Y. Sagiv. Keyword proximity search in complex data graphs. In SIGMOD Conference, pages 927--940. ACM, 2008. Google Scholar
Digital Library
- M. Grohe and J. Flum. Parameterized Complexity Theory. Theoretical Computer Science. Springer, 2006. Google Scholar
Digital Library
- M. Habib, R. M. McConnell, C. Paul, and L. Viennot. Lex-BFS and partition refinement, with applications to transitive orientation, interval graph recognition and consecutive ones testing. Theor. Comput. Sci., 234(1-2):59--84, 2000. Google Scholar
Digital Library
- V. Hristidis and Y. Papakonstantinou. DISCOVER: Keyword search in relational databases. In VLDB, pages 670--681. Morgan Kaufmann, 2002. Google Scholar
Digital Library
- A. Kemper, D. Kossmann, and B. Zeller. Performance tuning for SAP R/3. IEEE Data Eng. Bull., 22(2):32--39, 1999.Google Scholar
- B. Kimelfeld and Y. Sagiv. Finding and approximating top-k answers in keyword proximity search. In PODS, pages 173--182. ACM, 2006. Google Scholar
Digital Library
- B. Kimelfeld and Y. Sagiv. New algorithms for computing Steiner trees for a fixed number of terminals. Accessible from the first author's home page (http://www.cs.huji.ac.il/ bennyk), 2006.Google Scholar
- B. Kimelfeld, Y. Sagiv, and G. Weber. ExQueX: exploring and querying XML documents. In SIGMOD Conference, pages 1103--1106. ACM, 2009. Google Scholar
Digital Library
- Y. Li, C. Yu, and H. V. Jagadish. Schema-free XQuery. In VLDB, pages 72--83. Morgan Kaufmann, 2004. Google Scholar
Digital Library
- Y. Luo, W. Wang, and X. Lin. SPARK: A keyword search engine on relational databases. In ICDE, pages 1552--1555. IEEE, 2008. Google Scholar
Digital Library
- A. Markowetz, Y. Yang, and D. Papadias. Keyword search over relational tables and streams. ACM Trans. Database Syst., 34(3), 2009. Google Scholar
Digital Library
- Y. Mass, M. Ramanath, Y. Sagiv, and G. Weikum. IQ: The case for iterative querying for knowledge. In CIDR 2011, Fifth Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 9-12, 2011, Online Proceedings, pages 38--44. www.crdrdb.org, 2011.Google Scholar
- McConnell. Linear-time recognition of circular-arc graphs. Algorithmica, 37(2):93--147, 2003.Google Scholar
Digital Library
- L. Qin, J. X. Yu, and L. Chang. Keyword search in databases: the power of RDBMS. In SIGMOD Conference, pages 681--694. ACM, 2009. Google Scholar
Digital Library
- P. P. Talukdar, M. Jacob, M. S. Mehmood, K. Crammer, Z. G. Ives, F. Pereira, and S. Guha. Learning to create data-integrating queries. PVLDB, 1(1):785--796, 2008. Google Scholar
Digital Library
- Tucker. An efficient test for circular-arc graphs. SIAM J. Comput., 9(1):1--24, 1980.Google Scholar
Cross Ref
- M. Y. Vardi. The complexity of relational query languages (extended abstract). In Proceedings of the Fourteenth Annual ACM Symposium on Theory of Computing, pages 137--146. ACM, 1982. Google Scholar
Digital Library
- G. Zenz, X. Zhou, E. Minack, W. Siberski, and W. Nejdl. From keywords to semantic queries - incremental query construction on the semantic Web. J. Web Sem., 7(3):166--176, 2009. Google Scholar
Digital Library
Index Terms
Finding a minimal tree pattern under neighborhood constraints
Recommendations
Extracting minimum-weight tree patterns from a schema with neighborhood constraints
ICDT '13: Proceedings of the 16th International Conference on Database TheoryThe task of formulating queries is greatly facilitated when they can be generated automatically from some given data values, schema concepts or both (e.g., names of particular entities and XML tags). This automation is the basis of various database ...
Algorithms for the minimal cutsets enumeration of networks by graph search and branch addition
LCN '00: Proceedings of the 25th Annual IEEE Conference on Local Computer NetworksThis paper presents effective algorithms for enumerating the minimal cutsets of networks. After a graph is modeled after a network, first, by a graph method the spanning tree of the graph and binary tree whose event is complement to it are evaluated. ...






Comments