INSITE: A Tool for Real-Time Knowledge Discovery from Users Web Navigation
Summary: INSITE is an integrated platform for low-overhead, participation-free tracking of web navigation and real-time/offline profiling via its Connectivity Matrix Model. It supports scalable path clustering, temporal queries, and visualization for privacy-aware, actionable web analytics. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Cyrus Shahabi (University of Southern California)
- 2. Adil Faisal (University of Southern California)
- 3. Farnoush Banaei Kashani (University of Southern California)
- 4. Jabed Faruque (University of Southern California)
BibTeX Citation
@article{shahabi_vldb00,
title = {{INSITE: A Tool for Real-Time Knowledge Discovery from Users Web Navigation}},
author = {Shahabi, Cyrus and Faisal, Adil and Kashani, Farnoush Banaei and Faruque, Jabed},
journal = {PVLDB},
series = {{VLDB} '00},
year = {2000}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 31 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00050347119 |
| 88 | Efficient and Effective Clustering Methods for Spatial Data Mining | 1994 | VLDB | 0.00035240327 |
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