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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 257-260 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.126

Comparative Evaluation of Data Stream Indexing Models

Mahnoosh Kholghi and Mohammad Reza Keyvanpour

Abstract—In recent years, the management and processing of data streams has become a topic of active research in several fields of computer science such as, distributed systems, database systems, and data mining. A data stream can be thought of as a transient, continuously increasing sequence of data. In data streams' applications, because of online monitoring, answering to the user's queries should be time and space efficient. In this paper, we consider the special requirements of indexing to determine the performance of different techniques in data stream processing environments. Stream indexing has main differences with approaches in traditional databases. Also, we compare data stream indexing models analytically that can provide a suitable method for stream indexing.

Index Terms—Data stream, indexing, data streamprocessing.

Mahnoosh Kholghi is with the Department of Electronic, Computer and IT Islamic Azad University Qazvin, Iran and Young Researchers Club,Qazvin Branch, Islamic Azad University, Qazvin, Iran (e-mail:m.kholghi@qiau.ac.ir).
Mohammad Reza Keyvanpour is with the Department of Computer Engineering Alzahra University Tehran, Iran (e-mail:keyvanpour@alzahra.ac.ir).


Cite: Mahnoosh Kholghi and Mohammad Reza Keyvanpour, "Comparative Evaluation of Data Stream Indexing Models," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 257-260, 2012.

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