{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:35:32Z","timestamp":1774949732012,"version":"3.50.1"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,3]]},"abstract":"<jats:p>\n            Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly \"think like a vertex\" programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new open-source system, called\n            <jats:bold>Quegel<\/jats:bold>\n            , for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.\n          <\/jats:p>","DOI":"10.14778\/2904483.2904488","type":"journal-article","created":{"date-parts":[[2016,4,12]],"date-time":"2016-04-12T12:24:41Z","timestamp":1460463881000},"page":"564-575","source":"Crossref","is-referenced-by-count":36,"title":["A general-purpose query-centric framework for querying big graphs"],"prefix":"10.14778","volume":"9","author":[{"given":"Da","family":"Yan","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"James","family":"Cheng","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"M. 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