Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 15
Year of Publication: 2014
V. Sidda Reddy
M. Narendra
K. Helini

Sidda V Reddy, M Narendra and K Helini. Article: Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges. International Journal of Computer Applications 87(15):22-25, February 2014. Full text available. BibTeX

	author = {V. Sidda Reddy and M. Narendra and K. Helini},
	title = {Article: Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {87},
	number = {15},
	pages = {22-25},
	month = {February},
	note = {Full text available}


Mining data streams has recently become an important active research work and more widespread in several fields of computer science and engineering. It has proven successfully in many domains such as wireless sensor networks, ATM transactions, search engines, web analysis and weather monitoring. Data steams can be considered a subfield of machine learning, data mining and knowledge discovery. Data Mining is a step in the process of knowledge discovery from large amount of data. Traditional data mining techniques can not be easily applied to the data stream mining due to unique characteristics of data streams. In this research work, we will survey the main techniques and applications of data mining and data stream mining. We then study, the computational and miming challenges in particular, on-line mining of continuous, high-speed massive data streams.


  • Carlo Zaniolo, and Hetal Thakkar: Mining Data Bases and Data Streams. University of California, Los Angeles.
  • J. Han and M. Kamber: Data Mining, Concepts and Techniques. Morgan Kaufman, 2001
  • R. Agrawal, T. Imielinski, and A. Swami: Mining Association Rules between Sets of Items in Large Databases. In Proceedings of 1993Internatinal Conference on Management of Data
  • R. Agrawal and R. Srikan: Fast Algorithms for Mining Association Rules. Proceeding on the 20th VLDB conference 1994.
  • Mahnoosh Kholghi, Hamed Hassanzadeh, and MohammadReza Keyvanpour: Clssification and Evaluation of Data Mining Techniques for Data Stream Requirements. ISCCCA, 2010.
  • Mohamed Medhat Gaber, Shonali Krishnaswamy and Arkady Zaslavsky: Cost-Efficient Mining Techniques for Data Streams. Conferences in Research and Practice in Information Technology (DMWI 2004), Vol. 32.
  • Albert Bifet and Richard Kirkby: Data Stream Mining A Practical Approach. The University of WAIKATO, 2009.
  • M. Henziger, P. Raghavan, and S. Rajagopalan: Computing on data streams. In TR1998-001 Compag System Research. 1996.
  • Aggarwal. C. C. : Data Streams: Models and Algorithms Springer Berlin Heidelberg, 2007.
  • Chang, J. and Lee, W. : A sliding window method for finding recently frequent itemsets over online data streams ,JISE ,Vol. 20, 2004.
  • J. Chang and W. Lee : A Sliding Window Method for Finding Recently Frequent Itemsets over Online Data Streams, JISE, Vol. 20, 2004
  • Mahnoosh Kholghi, and MohammadReza Keyvanpour: An Analytical Framework for Data Stream Mining Techniques Based on Challenges and Requirements, IJEST, 2011.
  • Gaber M. M. , Zaslavsky A, and, Krishnaswamy S: Mining Data Streams: A Review, SIGOD, 2005.
  • Gaber M. M. , Zaslavsky A, and, Krishnaswamy S: Resource-Aware Knowledge Discovery in Data Streams, In Proceedings of FIWKDDS, 2004.
  • Babcock, B. , Babu, S. , Datar, M. , Motwani, R. and Widom, J. : Models and issues in data stream systems, In proceedings of t SIGMOD-SIGACT-SIGART Symposium on Principles of database systems (PODS), New York , 2002.