Abstract
Methods of top-k search with no random access can be used to find k best objects using sorted access to the sources of attribute values. In this paper we present new heuristics over the NRA algorithm that can be used for fast search of top-k objects using wide range of user preferences. NRA algorithm usually needs a periodical scan of a large number of candidates during the computation. In this paper we propose methods of no random access top-k search that optimize the candidate list maintenance during the computation to speed up the search. The proposed methods are compared to a table scan method typically used in databases. We present results of experiments showing speed improvement depending on number of object attributes expressed in a user preferences or selectivity of user preferences.
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References
Akbarinia, R., Pacitti, E., Valduriez, P.: Best Position Algorithms for Top-k Queries. In: VLDB (2007)
Bast, H., Majumdar, D., Schenkel, R., Theobald, M., Weikum, G.: IO-Top-k: Index-Access Optimized Top-k Query Processing. In: VLDB (2006)
Balke, W., Güntzer, U.: Multi-objective Query Processing for Database Systems. In: VLDB (2004)
Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web-accessible databases. In: ICDE (2002)
Chang, K.C.C., Hwang, S.W.: Minimal probing: Supporting expensive predicates for top-k queries. In: SIGMOD (2002)
Fagin, R., Lotem, A., Naor, M.: Optimal Aggregation Algorithms for Middleware. In: ACM PODS (2001)
Gurský, P., Lencses, R., Vojtáš, P.: Algorithms for user dependent integration of ranked distributed information. In: Böhlen, M.H., Gamper, J., Polasek, W., Wimmer, M.A. (eds.) TCGOV 2005. LNCS (LNAI), vol. 3416. Springer, Heidelberg (2005)
Gurský, V., Vojtáš, P.: On top-k search with no random access using small memory. In: ADBIS (accepted, 2008), http://klud.ics.upjs.sk/~gursky/papers/2008adbis.pdf
Gurský, P., Horváth, T., Novotný, R., Vaneková, V., Vojtáš, P.: UPRE: User preference based search system. In: IEEE/WIC/ACM Web Inteligence (2006)
Gurský, P., Vaneková, V., Pribolová, J.: Fuzzy User Preference Model for Top-k Search. In: Zurada, J.M., Yen, G.G., Wang, J. (eds.) Computational Intelligence: Research Frontiers. LNCS, vol. 5050. Springer, Heidelberg (2008)
Güntzer, U., Balke, W., Kiessling, W.: Towards efficient multi-feature queries in heterogeneous enviroments. In: ITCC (2001)
Horváth, T., Vojtáš, P.: Ordinal Classification with Monotonicity Constraints. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065. Springer, Heidelberg (2006)
Hristidis, V., Papakonstantinou, Y.: Algorithms and Applications for answering Ranked Queries using Ranked Views. VLDB Journal 13(1) (2004)
Ilyas, I., Aref, W., Elmagarmid, A.: Supporting top-k join queries in relational database. In: VLDB (2003)
Ilyas, I., Shah, R., Aref, W.G., Vitter, J.S., Elmagarmid, A.K.: Rank-aware query optimization. In: SIGMOD (2004)
Li, C., Chang, K., Ilyas, I., Song, S.: RankSQL: Query algebra and optimization for relational top-k queries. In: SIGMOD (2005)
Ramakrishnan, R., Gherke, J.: Database management systems, 3rd edn. McGraw-Hill, New York (2003)
Soliman, M.A., Ilyas, I.F., Chang, K.C.C.: Top-k Query Processing in Uncertain Databases. In: Proc. ICDE (2007)
Re, Ch., Dalvi, N.N., Suciu, D.: Efficient Top-k Query Evaluation on Probabilistic Data. In: Proc. ICDE (2007)
Theobald, M., Schenkel, R., Weikum, G.: An Efficient and Versatile Query Engine for TopX Search. In: VLDB (2005)
Yu, H., Hwang, S., Chang, K.: Enabling Soft Queries for Data Retrieval. Information Systems. Elsevier, Amsterdam (2007)
Xin, D., Han, J., Chang, K.: Progressive and Selective Merge: Computing Top-K with Ad-Hoc Ranking Functions. In: SIGMOD (2007)
Zhang, Z., Hwang, S., Chang, K., Wang, M., Lang, C., Chang, Y.: Boolean + Ranking: Querying a Database by K-Constrained Optimization. In: SIGMOD (2006)
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Gurský, P., Vojtáš, P. (2008). Speeding Up the NRA Algorithm. In: Greco, S., Lukasiewicz, T. (eds) Scalable Uncertainty Management. SUM 2008. Lecture Notes in Computer Science(), vol 5291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87993-0_20
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DOI: https://doi.org/10.1007/978-3-540-87993-0_20
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