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| Overview | |
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| Data Mining | |
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| Text Mining | |
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| Genetic Algorithms | |
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| Visualization | |
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| Computational Biology | |
Contacts: Jiawei Han, hanj@cs.uiuc.edu; Michael Welge, welge@ncsa.uiuc.edu; Loretta Auvil, lauvil@ncsa.uiuc.edu;
Funding Source: Pending
Project Website: http://maids.ncsa.uiuc.edu
Funding Source: Pending
Project Website: http://maids.ncsa.uiuc.edu
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Problem Definition Data streams are transient data that pass through the system (observer) continuously and in huge volumes. There are many applications that require handling data in the form of a stream, such as sensor data, network traffic flow, time-series data, stock exchange data, telecommunications, Web click streams, weather or environment monitoring, and so on. Different from the finite, static data sets stored in flat files or in database systems, data streams are in high volume, potentially infinite, dynamically changing, and require fast response. These unique characteristics make data stream analysis a great challenge.  Approach The MAIDS (Mining Alarming Incidents in Data Streams) project is aimed to perform a systematic investigation of stream data mining principles and algorithms, develop effective, efficient, and scalable methods for mining the dynamics of data streams, and implement a system prototype for online multi-dimensional stream data mining applications. This project will develop and implement some new and existing algorithms to discover changes, trends and evolution characteristics in data streams, construct clusters and classification models from data streams, and explore frequent patterns and similarities among data streams. The methods developed by this project will be applied to network intrusion detection, telecommunication data flow analysis, credit card fraud prevention, Web click streams analysis, financial data trend prediction, and other applications. The prototype system will be modified to support the OptiPuter and LOOKING research activities. |
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 Videos Introduction (AVI 56 MB) (WIN AVI 11 MB) Global Settings (AVI 36 MB) (WIN AVI 7.9 MB) Query Engine (AVI 29 MB) (WIN AVI 6.2 MB) Pattern Finder (AVI 80 MB) (WIN AVI 14.8 MB) Classifier (AVI 44 MB) (WIN AVI 8.2 MB) Clusterer (AVI 52 MB) (WIN AVI 10.7 MB) |
