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| Content Provider | ACM Digital Library |
|---|---|
| Author | Adaikkalavan, Raman Chakravarthy, Sharma Elkhalifa, Laali |
| Abstract | Information filtering includes monitoring text streams to detect patterns that are more complex than those handled by search engines. Text stream monitoring and pattern detection have far reaching applications such as tracking information flow among terrorist outfits, web parental control, and business intelligence. Pattern characterization requirements of applications entail an expressive language for specifying patterns than what is currently provided by Information Retrieval Query Languages (IRQLs) and current information filtering systems. Pattern specification alone does not suffice, as detecting these complex patterns is equally important in order to use these systems for real-world applications.InfoFilter, a content-based information filtering system, presented in this paper, allows users to specify complex patterns and detects these patterns in incoming text streams from various sources such as news feed, emails, web pages and caption text from streaming videos. Complex patterns such as combinations of sequential, structural patterns, wild cards, word frequencies, proximity, Boolean operators and synonyms are formulated using the expressive pattern specification language, PSL, proposed in this paper. Once specified, these complex patterns are detected using a data flow paradigm over Pattern Detection Graphs (PDGs). |
| Starting Page | 1084 |
| Ending Page | 1088 |
| Page Count | 5 |
| File Format | |
| ISBN | 1581139640 |
| DOI | 10.1145/1066677.1066923 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2005-03-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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|---|---|---|---|
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