A Genetic-Firefly Hybrid Algorithm to Find the Best Data Location in a Data Cube

Authors

  • M. Faridi Masouleh Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • M. A. Afshar Kazemi Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • M. Alborzi Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • A. Toloie Eshlaghy Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
Volume: 6 | Issue: 5 | Pages: 1187-1194 | October 2016 | https://doi.org/10.48084/etasr.702

Abstract

Decision-based programs include large-scale complex database queries. If the response time is short, query optimization is critical. Users usually observe data as a multi-dimensional data cube. Each data cube cell displays data as an aggregation in which the number of cells depends on the number of other cells in the cube. At any given time, a powerful query optimization method can visualize part of the cells instead of calculating results from raw data. Business systems use different approaches and positioning of data in the data cube. In the present study, the data is trained by a neural network and a genetic-firefly hybrid algorithm is proposed for finding the best position for the data in the cube.

Keywords:

database, data cube, genetic algorithm, firefly algorithm, neural network

Downloads

Download data is not yet available.

References

A. Nandi, C. Yu, P. Bohannon, R. Ramakrishnan, “Data cube materialization and mining over mapreduce”, IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 10, pp. 1747-1759, 2012 DOI: https://doi.org/10.1109/TKDE.2011.257

H. Cheng, Y. Lu, C. Sheu, “An ontology-based business intelligence application in a financial knowledge management system”, Expert Systems with Applications,Vol. 36, No. 2, pp. 3614-3622, 2009 DOI: https://doi.org/10.1016/j.eswa.2008.02.047

L. Ogiela, “Data management in cognitive financial systems”, International Journal of Information Management, Vol. 33, No. 2, pp. 263-270, 2013 DOI: https://doi.org/10.1016/j.ijinfomgt.2012.11.008

H. K. Chow, K. L. Choy, W. B. Lee, F. T. Chan, “Design of a knowledge-based logistics strategy system”, Expert Systems with Applications, Vol. 29, No. 2 ,pp. 272-290, 2005 DOI: https://doi.org/10.1016/j.eswa.2005.04.001

D. Barbara, X. Wu, “Using loglinear models to compress datacubes”, In Web-Age Information Management, Springer Berlin Heidelberg, pp. 311-323, 2000 DOI: https://doi.org/10.1007/3-540-45151-X_30

D. Barbará, M. Sullivan, “Quasi-cubes: exploiting approximations in multidimensional databases”, ACM SIGMOD Record, Vol. 26, No. 3, pp. 12-17, 1997 DOI: https://doi.org/10.1145/262762.262764

D. Xin, J. Han, X. Li, B. W. Wah, “Star-cubing: Computing iceberg cubes by top-down and bottom-up integration”, 29th International Conference on Very Large Data Bases, Vol. 29, pp. 476-487, 2003 DOI: https://doi.org/10.1016/B978-012722442-8/50049-5

D. Xin, Z. Shao, J. Han, H. Liu, “C-cubing: Efficient computation of closed cubes by aggregation-based checking in data engineering”, 22nd International Conference on Data Engineering, (ICDE), April 3-7, 2006

E. F. Codd, S. B. Codd, C. T. Salley, Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate, Codd & Associates, 1993

S. Li, B. Lin, “Accessing information sharing and information quality in supply chain management”, Decision Support Systems, Vol. 42 No. 3, pp. 1641-1656, 2006 DOI: https://doi.org/10.1016/j.dss.2006.02.011

M. Kamber, J. Han, J. Chiang, “Metarule-guided mining of multi-dimensional association rules using data cubes”, 3rd International Conference on Knowledge Discovery and Data Mining, Vol. 97, pp. 207-211, 1997

J. Han, J. Pei, G. Dong, K. Wang, “Efficient computation of iceberg cubes with complex measures”, In ACM SIGMOD Record, Vol. 30, No. 2, pp. 1-12, ACM, 2001 DOI: https://doi.org/10.1145/376284.375664

J. S. Vitter, M. Wang, B. Iyer, “Data cube approximation and histograms via wavelets”, 7th ACM International Conference on Information and Knowledge Management, pp. 96-104, 1998 DOI: https://doi.org/10.1145/288627.288645

K. C. Lee, S. Lee, I. W. Kang, “KMPI: measuring knowledge management performance”, Information and Management, Vol. 42, No. 3, pp. 469-482, 2005 DOI: https://doi.org/10.1016/j.im.2004.02.003

J. Brownlee, Clever Algorithms. Nature-Inspired Programming Recipes, Jason Brownlee, 2011

K. Beyer, R. Ramakrishnan, “Bottom-up computation of sparse and iceberg cube”, In ACM SIGMOD Record ,Vol. 28, No. 2, pp. 359-370, 1999 DOI: https://doi.org/10.1145/304181.304214

N. Wiwatwattana, H. V. Jagadish, L. V. Lakshmanan, D. Srivastava, “X^3: A cube operator for xml olap”, IEEE 23rd International Conference on Data Engineering, pp. 916-925, 2007 DOI: https://doi.org/10.1109/ICDE.2007.367937

K. A. Ross, D. Srivastava, “Fast computation of sparse datacubes”, 23rd International Conference on Very Large Data Bases, Vol. 97, pp. 25-29, 1997

A. Kavousi-Fard, H. Samet, F. Marzbani, “A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting”, Expert Systems with Applications, Vol. 41, No. 13, 6047-6056, 2014 DOI: https://doi.org/10.1016/j.eswa.2014.03.053

K. J. Kim, I. Han, “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index”, Expert systems with Applications, Vol. 19, No. 2, pp. 125-132, 2000 DOI: https://doi.org/10.1016/S0957-4174(00)00027-0

L. V. Lakshmanan, J. Pei, J. Han, “Quotient cube: How to summarize the semantics of a data cube”, 28th International Conference on Very Large Data Bases, pp. 778-789, 2002 DOI: https://doi.org/10.1016/B978-155860869-6/50074-3

L. V. Lakshmanan, J. Pei, J. Han, “QC-Trees: An efficient summary structure for semantic OLAP”, 2003 ACM SIGMOD International Conference on Management of Data, pp. 64-75, 2003 DOI: https://doi.org/10.1145/872757.872768

C. K. M. Lee, W. Ho, G. T. Ho, H. C. Lau, “Design and development of logistics workflow systems for demand management with RFID”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5428-5437, 2011 DOI: https://doi.org/10.1016/j.eswa.2010.10.012

D. Pelusi, “Designing neural networks to improve timing performances of intelligent controllers”, Journal of Discrete Mathematical Sciences and Cryptography, Vol. 16, No. 2-3, pp. 187-193, 2013 DOI: https://doi.org/10.1080/09720529.2013.821333

Y. Yuan, X. Lin, Q. Liu, W. Wang, J. X. Yu, Q. Zhang, “Efficient computation of the skyline cube”, 31st International Conference on Very Large Data Bases, pp. 241-252, 2005

M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, J. D. Ullman, “Computing Iceberg Queries Efficientl”, Internaational Conference on Very Large Databases, New York, Stanford InfoLab, 1998.

S. H. Min, J. Lee, I. Han, “Hybrid genetic algorithms and support vector machines for bankruptcy prediction”, Expert Systems with Applications, Vol. 31, No. 3, pp. 652-660, 2006 DOI: https://doi.org/10.1016/j.eswa.2005.09.070

D. Pelusi, “PID and intelligent controllers for optimal timing performances of industrial actuators”, International Journal of Simulation: Systems, Science and Technology, Vol. 13, No. 2, pp. 65-71, 2012

D. Pelusi, “Optimization of a fuzzy logic controller using genetic algorithms”, IEEE International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2, pp. 143-146, 2011 DOI: https://doi.org/10.1109/IHMSC.2011.105

R. T. Ng, A. Wagner, Y. Yin, “Iceberg-cube computation with PC clusters”, In ACM SIGMOD Record, Vol. 30, No. 2, pp. 25-36, 2001 DOI: https://doi.org/10.1145/376284.375666

S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. Naughton, R. Ramakrishnan, S. Sarawagi, “On the computation of multidimensional aggregates”, 22th International Conference on Very Large Data Bases, Vol. 96, pp. 506-521, 1996

S. Sarawagi, M. Stonebraker, “Efficient organization of large multidimensional arrays”, 10th International Conference on Data Engineering, pp. 328-336, 1994

K. S. Shin, Y. J. Lee, “A genetic algorithm application in bankruptcy prediction modeling”, Expert Systems with Applications, Vol. 23, No. 3, pp. 321-328, 2002 DOI: https://doi.org/10.1016/S0957-4174(02)00051-9

S. Haykin, Neural Networks, a comprehensive foundation, 2004

V. Poosala, V. Ganti, “Fast approximate answers to aggregate queries on a data cube”, IEEE 11th International Conference on Scientific and Statistical Database Management, pp. 24-33, 1999

V. Harinarayan, A. Rajaraman, J. D. Ullman, “Implementing data cubes efficiently”, In ACM SIGMOD Record, Vol. 25, No. 2, pp. 205-216, 1996 DOI: https://doi.org/10.1145/235968.233333

A. Vellido, P. J. Lisboa, P. J., J. Vaughan, “Neural networks in business: a survey of applications (1992–1998)”, Expert Systems with Applications, Vol. 17, No. 1, pp. 51-70, 1999 DOI: https://doi.org/10.1016/S0957-4174(99)00016-0

W. Wang, J. Feng, H. Lu, J. X. Yu, “Condensed cube: An effective approach to reducing data cube size”, IEEE 18th International Conference on Data Engineering, pp. 155-165, 2002

X. Li, J. Han, H. Gonzalez, “High-dimensional OLAP: a minimal cubing approach”, 30th International Conference on Very large Data Bases, Vol. 30, pp. 528-539, 2004 DOI: https://doi.org/10.1016/B978-012088469-8.50048-6

Y. Zhao, P. M. Deshpande, J. F. Naughton, “An array-based algorithm for simultaneous multidimensional aggregates”, In ACM SIGMOD Record, Vol. 26, No. 2, pp. 159-170, 1997 DOI: https://doi.org/10.1145/253262.253288

Z. Shao, J. Han, D. Xin, “MM-Cubing: Computing iceberg cubes by factorizing the lattice space in scientific and statistical database management”, IEEE 16th International Conference on Scientific and Statistical Database Management, pp. 213-222, 2004

C. W. Choo, “The knowing organization: How organizations use information to construct meaning, create knowledge and make decisions”, International journal of information management, Vol. 16, No. 5, pp. 329-340, 1996. DOI: https://doi.org/10.1016/0268-4012(96)00020-5

P. K. Dey, S. O. Ogunlana, “Selection and application of risk management tools and techniques for build-operate-transfer projects”, Industrial Management and Data Systems, Vol. 104, No. 4, pp. 334-346, 2004 DOI: https://doi.org/10.1108/02635570410530748

J. A. Johannessen, B. Olsen, B., J. Olaisen, “Aspects of innovation theory based on knowledge-management”, International Journal of Information Management, Vol. 19, No. 2, pp. 121-139, 1999 DOI: https://doi.org/10.1016/S0268-4012(99)00004-3

D. Pelusi, R. Mascella, “Optimal control algorithms for second order systems”, Journal of Computer Science, Vol. 9, No. 2, pp. 183-190, 2013 DOI: https://doi.org/10.3844/jcssp.2013.183.197

Downloads

How to Cite

[1]
Faridi Masouleh, M., Afshar Kazemi, M.A., Alborzi, M. and Toloie Eshlaghy, A. 2016. A Genetic-Firefly Hybrid Algorithm to Find the Best Data Location in a Data Cube. Engineering, Technology & Applied Science Research. 6, 5 (Oct. 2016), 1187–1194. DOI:https://doi.org/10.48084/etasr.702.

Metrics

Abstract Views: 963
PDF Downloads: 362

Metrics Information

Most read articles by the same author(s)