Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/3282
Title: Query Estimation in Data Streams Using Micro-Clustering
Authors: Gupta, Sudhanshu
Supervisor: Garg, Deepak
Keywords: Clustering;query estimation;data streams;micro clustering;computer science
Issue Date: 10-Nov-2014
Abstract: Advancement in technology has lead to availability of inexpensive electronic devices everywhere. These devices and various applications automatically generate a large amount of data which is increasing exponentially. The data can grow at a high rate of millions of data items per day for business and scienti c applications. A large number of applications generate continuous, transient large stream of data. For example the applications that naturally generate data streams are nancial tickers, log records or click-streams in web tracking and personalization, manufacturing processes, data feeds from sensor applications, sensor network, performance measurements in network monitoring and tra c management, call detail records in telecommunications, email messages. The analysis of large amount of data generated by various applications can create a lot of opportunities. For example, analyzing data of patients to diagnose the cause of disease, to design marketing strategies, predicting investment strategies, analyzing customer behavior. We need e cient techniques to analyze and process these unbounded data streams for useful information. However conventional techniques may not be applicable for their analysis. The processing of data stream requires single pass processing with limited memory. A number of techniques have been proposed for analysis of data streams meeting rigid processing requirement. These methods use various synopsis techniques such as sampling, wavelets, sketch etc. Micro-clustering is a synopsis technique used for clustering and classi cation of data stream. In this work we investigate how to estimate queries over large data streams using micro-clustering and cosine series. We store summary of data stream in micro-clusters and process clusters of data for estimating queries over streams. In order to assess the technique we conducted an experimental study. As the results of this study reveal, our technique outperform competitor method.
Description: Ph.D, CSED, Thesis
URI: http://hdl.handle.net/10266/3282
Appears in Collections:Doctoral Theses@CSED

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