Paper Info
Title
Incremental Maintenance of Online Summaries Over Multiple Streams
Abstract
We propose a novel predictive quantization (PQ) based approach for online summarization of multiple time varying data streams. A synopsis over a sliding window of most recent entries is computed in one pass and dynamically updated in constant time. The correlation between consecutive data elements is effectively taken into account without the need for preprocessing. We extend PQ to multiple streams and propose structures for real-time summarization and querying of a massive number of streams. Queries on any subsequence of a sliding window over multiple streams are processed in real-time. We examine each component of the proposed approach, prediction and quantization, separately and investigate the space-accuracy trade off for synopsis generation. Complementing the theoretical optimality of PQ based approaches, we show that the proposed technique, even for very short prediction windows, significantly outperforms the current techniques for a wide variety of query types on both synthetic and real data sets.
Year
DOI
Venue
2008
10.1109/TKDE.2007.190693
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
online summaries,multiple stream,incremental maintenance,constant time,consecutive data element,novel predictive quantization,online summarization,multiple time,proposed technique,varying data stream,multiple streams,data mining,quantization,databases,intrusion detection,sliding window,data security,prediction,real time,summarization,data analysis
Data mining,Automatic summarization,Data stream mining,Data set,Sliding window protocol,Computer science,Preprocessor,Artificial intelligence,Subsequence,Quantization (signal processing),Intrusion detection system,Machine learning
Journal
Volume
Issue
ISSN
20
2
1041-4347
Citations 
PageRank 
References 
18
0.65
28
Authors
3
Name
Order
Citations
PageRank
Fatih Altiparmak1395.56
Ertem Tuncel238636.48
Hakan Ferhatosmanoglu3135289.79