An Architectural Framework for Constructing Materialized Views in a Data Warehouse

Abstract—Data warehouse stores data accumulated over a period of time from disparate data sources for providing answers to analytical queries. These queries, which are long and complex in nature, have high query response time when processed against a large data warehouse. This response time can be reduced by constructing materialized views and storing them in a data warehouse. These views need to contain relevant and required information for answering future queries, so that future queries can be answered in a reduced response time to make decision making more efficient. A Materialized Views Construction Framework (MVCF), presented in this paper, lays down a strategy for constructing materialized views from previously posed queries on the data warehouse. The objective of MVCF is to enable construction of materialized views that are subject-specific and contain frequently accessed information that are capable of providing answers to future queries. This in turn would facilitate decision making.

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

[2]  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).

[3]  Gaurav Dubey,et al.  Frequent Queries Selection for View Materialization , 2012, ACITY.

[4]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[5]  Rada Chirkova,et al.  A formal perspective on the view selection problem , 2002, The VLDB Journal.

[6]  Kalyani Devi,et al.  Frequent queries identification for constructing materialized views , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[7]  Neeraj Jain,et al.  Selection of Frequent Queries for Constructing Materialized Views in Data Warehouse , 2010 .

[8]  Dion Hoe-Lian Goh,et al.  Clustering Using a Hybrid Query Similarity Measure , 2003 .

[9]  John F. Roddick,et al.  Association mining , 2006, CSUR.

[10]  T. V. Vijay Kumar,et al.  Greedy Selection of Materialized Views , 2009 .

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

[12]  Ashish Gupta,et al.  Generalized Projections: A Powerful Approach To Aggregation , 1995 .

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

[14]  Edwin L. Bradley,et al.  A nonparametric measure of the overlapping coefficient , 2000 .

[15]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

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

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

[18]  Werner Nutt,et al.  Intelligent Access to Heterogeneous Information, Proceedings of the 4th Workshop KRDB-97, Athens, Greece, August 30, 1997 , 1997, CEUR Workshop Proceedings.

[19]  Gaurav Dubey,et al.  Mining Queries for Constructing Materialized Views in a Data Warehouse , 2012 .

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

[21]  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.

[22]  T. V. Vijay Kumar,et al.  Computing full disjunction using COJO , 2009, Inf. Technol. Manag..

[23]  Inderpal Singh Mumick,et al.  Selection of views to materialize in a data warehouse , 1997, IEEE Transactions on Knowledge and Data Engineering.

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

[25]  Anand Rajaraman,et al.  Integrating Information by Outerjoins and Full Disjunctions , 1996, PODS 1996.

[26]  César A. Galindo-Legaria,et al.  Outerjoins as disjunctions , 1994, SIGMOD '94.

[27]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

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

[29]  W. S. Cho,et al.  An Efficient OLAP Query Processing Technique Using Measure Attribute Indexes , 2004, WISE.

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

[31]  Stephen R. Gardner.  Building the data warehouse , 1998, CACM.

[32]  KarlapalemKamalakar,et al.  View relevance driven materialized view selection in data warehousing environment , 2002 .

[33]  M. Teschke,et al.  Using Materialized Views To Speed Up Data Warehousing , 1998 .

[34]  Feng Yu,et al.  Materialized View Selection in a Data Warehouse , 2000, Web-Age Information Management.

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

[36]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

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

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

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

[40]  Goetz Graefe,et al.  Multi-table joins through bitmapped join indices , 1995, SGMD.

[41]  Ciro Cattuto,et al.  Evaluating similarity measures for emergent semantics of social tagging , 2009, WWW '09.