A Glimpse on Iceberg Query Evaluation Techniques

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Queries are required to determine distinct quality worth’s and also their accumulation that is over a predefined limit from this significant variety of documents. The information keeping as well as getting are playing a significant function in the information clustering and also information warehousing methods. The performance of an information retrieving approach relies on the information details queries for getting the information from the data source. Iceberg query is an one-of-a-kind course of gathering query, which calculates accumulation worth’s over a provided limit. The queries which are having this sort of nature are called as Iceberg( IB) queries.

                    Existing data source system perform it similar to typical query so it take even more time to implement. It is difficult job to essence fascinating as well as crucial details promptly from big data source. Great deals of research study has actually been done to raise the rate of IB query. Initially scientists makes use of tuple check strategy to perform IB query which is time consuming and also need even more memory. To get rid of these troubles scientists recommended IB query examination utilizing Bitmap Indexing strategies. This method prevent total table check so time called for to carry out IB query is decreased as well as memory demand is likewise lowered.

     

     


  • Keywords


    Bitmap Index , Aggregation functions, Iceberg Queries, Counting co-occurrence

  • References


      [1] M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, as well as J.D. Ullman, "Computer Iceberg Queries Successfully", Proc. Int' l Conf. Huge Information Bases (VLDB), pp. 299-310, 1998.

      [2] Whang, K.Y., B.T.V. Zanden and also H.M. Taylor, "Alinear-time probabilistic checking formula for data source applications", ACM Trans. Data source Syst., 15: 208-229, 1990.

      [3] J.Bae and also S.Lee. "Dividing formulas for the calculation of ordinary iceberg questions." Proc. 2nd Intl Conf. Information Ware- real estate as well as Knowl-edge Exploration (DaWaK), pp. 276-286, 2000.

      [4] K.P. Leela, P.M. Tolani, as well as J.R. Haritsa, "On Integrating Iceberg Queries in Inquiry Processors", Proc. Intl Conf. Data-base Solutions for Developments Applications (DASFAA), pp. 431-442, 2004.

      [5] J. Han, J. Pei, G. Dong, and also K. Wang, "Reliable Calculation of Iceberg Cubes with Complicated Procedures", Proc. ACM SIGMOD Int' l Conf. Administration of Information, pp. 1-12, 2001.

      [6] A. Gilbert, Y. Kotidis, S. Muthukrishnan as well as M. Strauss, "Surfing wavelets on streams: one- pass recaps for approximate accumulation inquiries,", Proc. of 27th Intl. Conf. on Large Information Bases, 2001.

      [7] G. Graefe, "Inquiry Examination Methods for Big Data Sources", ACM Comput. Surv., 25, 2,73-- 170, June 1993.

      [8] Y. Ioannidis as well as V. Poosala, "Histogram-Based Solutions to Diverse Data Source Evaluation Issues", IEEE Information Design, Vol. 18, No. 3, pp. 10-18, September 1995.

      [9] I. Lazaridis and also S. Mehrotra, "Progressive Approximate Accumulation Queries with a Multi- Resolution Tree Framework", Proc. of ACM SIGMOD Conf., 2001.

      [10] K. Leela, P. Tolani and also J. Haritsa, "On Including Iceberg Queries in Inquiry Processors",Technology. Rep. TR-2002-01, DSL/SERC, Indian Institute of Scientific Research, February 2002.

      [11] Bin He, Hui-I Hsiao, Ziyang Liu, Yu Huang and Yi Chen, “Efficient Iceberg Query Evaluation Using Compressed Bitmap Index”, IEEE Transactions On Knowledge and Data Engineering, vol 24, issue 9, sept 2011, pp.1570-1589.

      [12] J. Bae and S.Lee, “Partitioning Algorithms for the Computation of Average Iceberg Queries”, DAWAK,2000. .[13] R. Ramakrishnan as well as J. Gehrke, "Data Source Administration Solutions", McGraw-Hill, 2000.

      [13] P. Selinger, M. Astrahan, D. Chamberlin, R. Lorie and also T. Rate, "Gain Access To Course Choice in a.Relational Data source Administration System", Proc. of ACM SIGMOD Conf., 1979.

      [14] K.S. Beyer and R. Ramakrishnan, “Bottom-Up Computation of Sparse and Iceberg CUBEs”, Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 359-370, 1999

      [15] Vuppu shanker et al, “Efficient iceberg evaluation in Distributed databases by Developing Deferred Strategies”,2016

      [16] K.Beyer and R.Ramakrishnan,”Bottom-Up Computation of sparse and iceberg CUBEs”,In Proc.of the ACM SIGMOD Conf.,Pages 359-370,1999.

      [17] Leela krishna poola”Efficiently evaluating N-iceberg queries”


 

View

Download

Article ID: 27745
 
DOI: 10.14419/ijet.v7i4.39.27745




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.