cid 35e828965dc441d41d63bbf1f6c6a0916eff6aec7a144e682e41c664ec609da5 · original · via openalex
Title: Percolator Authors: Sutanay Choudhury; Sumit Purohit; Peng Lin; Yinghui Wu; Lawrence B. Holder; Khushbu Agarwal Venue: ACM International Conference on Web Search and Data Mining (WSDM) Year: 2018 Canonical: doi:10.1145/3159652.3160589 URL: https://doi.org/10.1145/3159652.3160589 Collector: openalex OA: false ABSTRACT We demonstrate \perco, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of \perco is to dynamically decide and verify a small fraction of patterns and their instances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a( the feasibility of incremental pattern mining by walking through each component of \perco, b) the efficiency and scalability of \perco over the sheer size of real-world dynamic graphs, and c) how the user-friendly \gui of \perco interacts with users to support keyword-based queries that detect, browse and inspect trending patterns. We demonstrate how \perco effectively supports event and trend analysis in social media streams and research publication, respectively.