A Multi-Objective Approach for Materialized View Selection

In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.

[1]  Santosh Kumar,et al.  A novel quantum-inspired evolutionary view selection algorithm , 2018, Sādhanā.

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

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

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

[5]  T. V. Vijay Kumar,et al.  Query Frequency based View Selection , 2017 .

[6]  T. V. Vijay Kumar,et al.  Materialized View Selection Using Bumble Bee Mating Optimization , 2017, Int. J. Decis. Support Syst. Technol..

[7]  Santosh Kumar,et al.  Materialised view selection using differential evolution , 2014 .

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

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

[10]  T. V. Vijay Kumar,et al.  Materialized View Selection Using Memetic Algorithm , 2013, MIKE.

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

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

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

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

[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.  Materialised view selection using randomised algorithms , 2015, Int. J. Bus. Inf. Syst..

[17]  Howard J. Karloff,et al.  On the complexity of the view-selection problem , 1999, PODS '99.

[18]  Amit Kumar,et al.  Materialized View Selection Using Set Based Particle Swarm Optimization , 2018, Int. J. Cogn. Informatics Nat. Intell..

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

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

[21]  T. V. Vijay Kumar,et al.  Query answering-based view selection , 2015, Int. J. Bus. Inf. Syst..

[22]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

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

[24]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[25]  T. V. Vijay Kumar,et al.  Materialised view selection using BCO , 2016, Int. J. Bus. Inf. Syst..

[26]  T. V. Vijay Kumar,et al.  Materialized View Selection using Improvement based Bee Colony Optimization , 2015, Int. J. Softw. Sci. Comput. Intell..

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

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

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

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

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

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

[33]  T. V. Vijay Kumar,et al.  Materialized View Selection using Artificial Bee Colony Optimization , 2017, Int. J. Intell. Inf. Technol..

[34]  Nick Roussopoulos.  The Logical Access Path Schema of a Database , 1982, IEEE Transactions on Software Engineering.

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

[36]  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.

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

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

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

[40]  T. V. Vijay Kumar,et al.  Materialized view selection using HBMO , 2017, Int. J. Syst. Assur. Eng. Manag..

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

[42]  T. V. Vijay Kumar,et al.  Materialized View Selection using Marriage in Honey Bees Optimization , 2015, Int. J. Nat. Comput. Res..

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

[44]  Jeffrey Xu Yu,et al.  What Difference Heuristics Make: Maintenance-Cost View-Selection Revisited , 2002, WAIM.

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

[46]  Amit Kumar,et al.  Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization , 2017 .

[47]  Jorng-Tzong Horng,et al.  Applying evolutionary algorithms to materialized view selection in a data warehouse , 2003, Soft Comput..

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

[49]  Jian Yang,et al.  Algorithms for Materialized View Design in Data Warehousing Environment , 1997, VLDB.