Synopsis Data Structures for Representing, Querying, and Mining Data Streams

Author:

Cuzzocrea Alfredo1

Affiliation:

1. University of Calabria, Italy

Abstract

Data-stream query processing and mining is an emerging challenge for the database research community. This issue has recently gained the attention from the academic as well as the industrial world. Data streams are continuously flooding data produced by untraditional information sources. They are generated by a growing number of data entities, among all we recall performance measurements in network monitoring and traffic management systems, call details records in telecommunication systems, transactions in retail chains, ATM operations, log records generated by Web servers, sensor network data, RFID- (radio frequency identification) based readings, and so forth.

Publisher

IGI Global

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. HapticSeer: A Multi-channel, Black-box, Platform-agnostic Approach to Detecting Video Game Events for Real-time Haptic Feedback;Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems;2021-05-06

2. Approximation Algorithms for Massive High-Rate Data Streams;Advances in Intelligent Systems and Computing;2013

3. Non-linear Data Stream Compression: Foundations and Theoretical Results;Lecture Notes in Computer Science;2012

4. Event-based lossy compression for effective and efficient OLAP over data streams;Data & Knowledge Engineering;2010-07

5. CAMS: OLAPing Multidimensional Data Streams Efficiently;Data Warehousing and Knowledge Discovery;2009

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