Meaningful change detection in structured data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical change detection for multi-dimensional data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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Stream mining is a challenging problem that has attracted considerable attention in the last decade. As a result there are numerous algorithms for mining data streams, from summarizing and analyzing, to change and anomaly detection. However, most research focuses on proposing, adapting or improving algorithms and studying their computational performance. For a practitioner of stream mining, there is very little guidance on choosing a technology suited for a particular task or application. In this paper, we address the practical aspect of choosing a suitable algorithm by drawing on the statistical properties of power and robustness. For the purpose of illustration, we focus on change detection algorithms (CDAs). We define an objective performance measure, streaming power, and use it to explore the robustness of three different algorithms. The measure is comparable for disparate algorithms, and provides a common framework for comparing and evaluating change detection algorithms on any data set in a meaningful fashion. We demonstrate on real world applications, and on synthetic data. In addition, we present a repository of data streams for the community to test change detection algorithms for streaming data.