Knowledge and Data Management White Papers
Fast Mining of Massive Tabular Data Via Approximate Distance Computations
Overview Tabular data abound in many data stores: traditional relational databases store tables, and new applications also generate massive tabular datasets. For example, consider the geographic distribution of cell phone traffic at different base stations across the country or the evolution of traffic at Internet routers over time. Detecting similarity patterns in such data sets is of great importance. Identification of such patterns poses many conceptual challenges (what is a suitable similarity distance function for two "Regions") as well as technical challenges (how to perform similarity computations efficiently as massive tables get accumulated over time) that are addressed. This paper presents methods for determining similar regions in massive tabular data.
| Publisher | University of Warwick | File Format | |
|---|---|---|---|
| Date Published | July 2002 | ||
| Format | White Papers | ||
| Topics | |||
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