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Ordering the attributes of query results

Published: 27 June 2006 Publication History

Abstract

There has been a great deal of interest in the past few years on ranking of results of queries on structured databases, including work on probabilistic information retrieval, rank aggregation, and algorithms for merging of ordered lists. In many applications, for example sales of homes, used cars or electronic goods, data items have a very large number of attributes. When displaying a (ranked) list of items to users, only a few attributes can be shown. Traditionally, these are selected manually. We argue that automatic selection of attributes is required to deal with different requirements of different users. We formulate the problem as an optimization problem of choosing the most "useful" set of attributes, that is, the attributes that are most influential in the ranking of the items. We discuss different variants of our notion of attribute usefulness, and propose a hybrid Split-Pane approach that returns a composite of the top attributes of each variant. We conduct both a performance and a user study illustrating the benefits of our algorithms in terms of efficiency and quality of explanation.

References

[1]
Sanjay Agrawal, Surajit Chaudhuri, Gautam Das, and A. Gionis. Automated ranking of database query results. In CIDR, 2003.
[2]
Alexander Aiken, Jolly Chen, Michael Stonebraker, and Allison Woodruff. Tioga-2: A direct manipulation database visualization environment. In ICDE, 1996.
[3]
A. Balmin, V. Hristidis, and Y. Papakonstantinou. ObjectRank: Authority-Based Keyword Search in Databases. In VLDB, 2004.
[4]
Nicolas Bruno, Luis Gravano, and Amelie Marian. Evaluating top-k queries over web-accessible databases. In ICDE, 2002.
[5]
Kaushik Chakrabarti, Surajit Chaudhuri, and Seung won Hwang. Automatic categorization of query results. In SIGMOD, pages 755--766, 2004.
[6]
Surajit Chaudhuri, Gautam Das, Vagelis Hristidis, and Gerhard Weikum. Probabilistic ranking of database query results. In VLDB, 2004.
[7]
P. Diaconis and R. Graham. Spearman's footrule as a measure of disarray. In J. of the Royal Statistical Society, Series B, 39(2):262--268, 1977.
[8]
Cynthia Dwork, Ravi Kumar, Moni Naor, and D. Sivakumar. Rank aggregation methods for the Web. In WWW Conf., 2001.
[9]
Ronald Fagin, Ravi Kumar, Mohammad Mahdiany, D. Sivakumar, and Erik Veez. Comparing and aggregating rankings with ties. In PODS, 2004.
[10]
Ronald Fagin, Ravi Kumar, and D. Sivakumar. Comparing top k lists. In Procs.ACM-SIAM Symposium on Discrete Algorithms (SODA), 2003.
[11]
Ronald Fagin, Amnon Lotem, and Moni Naor. Optimal aggregation algorithms for middleware. In PODS, 2001.
[12]
Christos Faloutsos and King-Ip Lin. FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In SIGMOD, 1995.
[13]
M. R. Garey and D. S. Johnson. Computers and Intractability-A Guide to the Theory of NP-Completeness. W. H. Freeman And Company, 1979.
[14]
Isabelle Guyon and Andre Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3(mar):1157--1182, 2003.
[15]
V. Hristidis, N. Koudas, and Y. Papakonstantinou. PREFER:a system for the efficient execution of multi-parametric ranked queries. In SIGMOD, May 2001.
[16]
D. Keim and H-P. Kriegel. VisDB: Database exploration using multidimensional visualization. Computer Graphics and Applications Journal, 1994.
[17]
Eamonn Keogh, Kaushik Chakrabarti, Sharad Mehrotra, and Michael Pazzani. Locally adaptive dimensionality reduction for indexing large time series databases. In SIGMOD, 2001.
[18]
Ullas Nambiar and Subbarao Kambhampati. Mining approximate functional dependencies and concept similarities to answer imprecise queries. In WebDB: Int'l Workshop on the Web and Databases, 2004.
[19]
Penny Rheingans and Marie desJardins. Assessing projection quality for high-dimensional information visualization. Technical report, Univ. Maryland at Baltimore County, 2002.
[20]
Chris Stolte, Diane Tang, and Pat Hanrahan. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. Vis. Comput. Graph, 8(1):52--65, 2002.
[21]
Lisa A. Torrey. An active learning approach to efficiently ranking retrieval engines. Technical Report TR2003-449, Dartmouth, 2003.
[22]
Yiming Yang and Jan O. Pederson. A comparative study on feature selection in text categorization. In ICML, 1997.

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cover image ACM Conferences
SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data
June 2006
830 pages
ISBN:1595934340
DOI:10.1145/1142473
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 June 2006

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Cited By

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  • (2019)Contextual Fact Ranking and Its Applications in Table Synthesis and CompressionProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330980(285-293)Online publication date: 25-Jul-2019
  • (2016)A Comparative Study of Query-biased and Non-redundant Snippets for Structured Search on Mobile DevicesProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983699(2389-2394)Online publication date: 24-Oct-2016
  • (2016)Expressive Query Construction through Direct Manipulation of Nested Relational ResultsProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2915210(1377-1392)Online publication date: 26-Jun-2016
  • (2016)Merlin: Exploratory Analysis with Imprecise QueriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.249627028:2(342-355)Online publication date: 1-Feb-2016
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  • (2015)Information Exploration in E-Commerce DatabasesProceedings of the 4th International Conference on Big Data Analytics - Volume 949810.1007/978-3-319-27057-9_3(41-56)Online publication date: 15-Dec-2015
  • (2014)Ranking item features by mining online user-item interactions2014 IEEE 30th International Conference on Data Engineering10.1109/ICDE.2014.6816673(460-471)Online publication date: Mar-2014
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