Query answering-based view selection

A data warehouse is designed for answering decision making queries. These queries are usually long, complex and exploratory in nature and involve aggregates over a large number of dimensions. As a result, the processing time for such queries, against a continuously growing data warehouse, is high. This problem can be addressed by materialising views over a data warehouse. This paper presents a query answering view selection algorithm QAVSA that considers the size and query answering capability of views to select the top-K views for materialisation from a multi-dimensional lattice. The views selected using QAVSA are likely to be beneficial both with respect to their size and their ability to answer decision making queries. Further, experimental results show that QAVSA, in comparison to the well known greedy algorithm HRUA, is able to efficiently select views that can provide answers to greater number of queries. This in turn would facilitate decision making.

[1]  Jérôme Darmont,et al.  Clustering-Based Materialized View Selection in Data Warehouses , 2006, ADBIS.

[2]  Kamalakar Karlapalem,et al.  View Relevance Driven Materialized View Selection in Data Warehousing Environment , 2002, Australasian Database Conference.

[3]  Surajit Chaudhuri,et al.  Automated Selection of Materialized Views and Indexes in SQL Databases , 2000, VLDB.

[4]  T. V. Vijay Kumar,et al.  Greedy Views Selection Using Size and Query Frequency , 2011 .

[5]  T. V. Vijay Kumar,et al.  Proposing Candidate Views for Materialization , 2010, ICISTM.

[6]  Alexeis Garcia-Perez,et al.  Revisiting knowledge warehousing: theoretical foundations , 2008, Int. J. Bus. Inf. Syst..

[7]  T. V. Vijay Kumar,et al.  Materialized View Selection Using Iterative Improvement , 2012, ACITY.

[8]  Xin Yao,et al.  An evolutionary approach to materialized views selection in a data warehouse environment , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[9]  Neeraj Jain,et al.  Mining information for constructing materialised views , 2010, Int. J. Inf. Commun. Technol..

[10]  T. V. Vijay Kumar,et al.  An Architectural Framework for Constructing Materialized Views in a Data Warehouse , 2013 .

[11]  W. H. Inmon,et al.  Building the Data Warehouse,3rd Edition , 2002 .

[12]  T. V. Vijay Kumar,et al.  Selection of Views for Materialization Using Size and Query Frequency , 2011 .

[13]  John F. Roddick,et al.  Advances and Research Directions in Data-Warehousing Technology , 1999, Australas. J. Inf. Syst..

[14]  Jeffrey F. Naughton,et al.  Materialized View Selection for Multidimensional Datasets , 1998, VLDB.

[15]  T. V. Vijay Kumar,et al.  A View Recommendation Greedy Algorithm for Materialized Views Selection , 2011, ICISTM.

[16]  T. V. Vijay Kumar,et al.  Materialized Views Selection for Answering Queries , 2010, ICDEM.

[17]  Cheng-Yan Kao,et al.  Materialized view selection using genetic algorithms in a data warehouse system , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[18]  T. V. Vijay Kumar,et al.  Materialized View Selection Using Simulated Annealing , 2012, BDA.

[19]  Xin Yao,et al.  Evolving materialized views in data warehouse , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[20]  Ziyu Lin,et al.  User-Oriented Materialized View Selection , 2007, 7th IEEE International Conference on Computer and Information Technology (CIT 2007).

[21]  T. V. Vijay Kumar,et al.  Materialised views selection using size and query frequency , 2011 .

[22]  Nick Roussopoulos,et al.  Materialized views and data warehouses , 1998, SGMD.

[23]  Elena Baralis,et al.  Materialized Views Selection in a Multidimensional Database , 1997, VLDB.

[24]  T. V. Vijay Kumar,et al.  Materialised view construction in data warehouse for decision making , 2012, Int. J. Bus. Inf. Syst..

[25]  T. V. Vijay Kumar,et al.  Materialized View Selection Using Genetic Algorithm , 2012, IC3.

[26]  Toby J. Teorey,et al.  A progressive view materialization algorithm , 1999, DOLAP '99.

[27]  T. V. Vijay Kumar,et al.  A Query Answering Greedy Algorithm for Selecting Materialized Views , 2010, ICCCI.

[28]  Gang Luo,et al.  Partial Materialized Views , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[29]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[30]  Jérôme Darmont,et al.  Data mining-based materialized view and index selection in data warehouses , 2007, Journal of Intelligent Information Systems.

[31]  M. T. Serna-Encinas,et al.  Algorithm for selection of materialized views: based on a costs model , 2007 .

[32]  Jennifer Widom,et al.  Research problems in data warehousing , 1995, CIKM '95.

[33]  Karthik Ramachandran,et al.  A Hybrid Approach for Data Warehouse View Selection , 2006, Int. J. Data Warehous. Min..

[34]  Matteo Golfarelli,et al.  View materialization for nested GPSJ queries , 2000, DMDW.

[35]  Timos K. Sellis,et al.  Data Warehouse Configuration , 1997, VLDB.

[36]  T. V. Vijay Kumar,et al.  Answering query-based selection of materialised views , 2013, Int. J. Inf. Decis. Sci..

[37]  Michael Lawrence,et al.  Multiobjective genetic algorithms for materialized view selection in OLAP data warehouses , 2006, GECCO '06.

[38]  Jeffrey D. Ullman,et al.  Implementing data cubes efficiently , 1996, SIGMOD '96.

[39]  Jeffrey D. Ullman,et al.  Index selection for OLAP , 1997, Proceedings 13th International Conference on Data Engineering.

[40]  Inderpal Singh Mumick,et al.  Selection of Views to Materialize in a Data Warehouse , 2005, IEEE Trans. Knowl. Data Eng..

[41]  Wolfgang Lehner,et al.  Improving query response time in scientific databases using data aggregation -a case study , 1996, Proceedings of 7th International Conference and Workshop on Database and Expert Systems Applications: DEXA 96.

[42]  T. V. Vijay Kumar,et al.  A Reduced Lattice Greedy Algorithm for Selecting Materialized Views , 2009, ICISTM.

[43]  Alan D. Smith,et al.  Quality assurance practices for competitive data warehouse management systems , 2011, Int. J. Bus. Inf. Syst..