{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:06:57Z","timestamp":1780636017335,"version":"3.54.1"},"reference-count":14,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2015,8]]},"abstract":"<jats:p>Distributed RDF systems partition data across multiple computer nodes. Partitioning is typically based on heuristics that minimize inter-node communication and it is performed in an initial, data pre-processing phase. Therefore, the resulting partitions are static and do not adapt to changes in the query workload; as a result, existing systems are unable to consistently avoid communication for queries that are not favored by the initial data partitioning. Furthermore, for very large RDF knowledge bases, the partitioning phase becomes prohibitively expensive, leading to high startup costs.<\/jats:p>\n          <jats:p>In this paper, we propose AdHash, a distributed RDF system which addresses the shortcomings of previous work. First, AdHash initially applies lightweight hash partitioning, which drastically minimizes the startup cost, while favoring the parallel processing of join patterns on subjects, without any data communication. Using a locality-aware planner, queries that cannot be processed in parallel are evaluated with minimal communication. Second, AdHash monitors the data access patterns and adapts dynamically to the query load by incrementally redistributing and replicating frequently accessed data. As a result, the communication cost for future queries is drastically reduced or even eliminated. Our experiments with synthetic and real data verify that AdHash (i) starts faster than all existing systems, (ii) processes thousands of queries before other systems become online, and (iii) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in sub-seconds. In this demonstration, audience can use a graphical interface of AdHash to verify its performance superiority compared to state-of-the-art distributed RDF systems.<\/jats:p>","DOI":"10.14778\/2824032.2824083","type":"journal-article","created":{"date-parts":[[2015,9,16]],"date-time":"2015-09-16T12:18:17Z","timestamp":1442405897000},"page":"1848-1851","source":"Crossref","is-referenced-by-count":33,"title":["Evaluating SPARQL queries on massive RDF datasets"],"prefix":"10.14778","volume":"8","author":[{"given":"Razen","family":"Harbi","sequence":"first","affiliation":[{"name":"King Abdullah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Abdelaziz","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panos","family":"Kalnis","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikos","family":"Mamoulis","sequence":"additional","affiliation":[{"name":"University of Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2015,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732951.2732957"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/568522.568525"},{"key":"e_1_2_1_3_1","volume-title":"Partout: A Distributed Engine for Efficient RDF Processing. CoRR, abs\/1212.5636","author":"Galarraga L.","year":"2012","unstructured":"L. Galarraga , K. Hose , and R. Schenkel . Partout: A Distributed Engine for Efficient RDF Processing. CoRR, abs\/1212.5636 , 2012 . L. Galarraga, K. Hose, and R. Schenkel. Partout: A Distributed Engine for Efficient RDF Processing. CoRR, abs\/1212.5636, 2012."},{"key":"e_1_2_1_4_1","volume-title":"USEWOD","author":"Gallego M.","year":"2011","unstructured":"M. Gallego , J. D. Fern\u00e1ndez , M. A. Mart\u00ednez-Prieto , and P. de la Fuente. An empirical study of real-world SPARQL queries . In USEWOD , 2011 . M. Gallego, J. D. Fern\u00e1ndez, M. A. Mart\u00ednez-Prieto, and P. de la Fuente. An empirical study of real-world SPARQL queries. In USEWOD, 2011."},{"key":"e_1_2_1_5_1","volume-title":"SIGMOD","author":"Gurajada S.","year":"2014","unstructured":"S. Gurajada , S. Seufert , I. Miliaraki , and M. Theobald . TriAD: A Distributed Shared-nothing RDF Engine Based on Asynchronous Message Passing . In SIGMOD , 2014 . 10.1145\/2588555.2610511 S. Gurajada, S. Seufert, I. Miliaraki, and M. Theobald. TriAD: A Distributed Shared-nothing RDF Engine Based on Asynchronous Message Passing. In SIGMOD, 2014. 10.1145\/2588555.2610511"},{"key":"e_1_2_1_6_1","volume-title":"ICDEW","author":"Hose K.","year":"2013","unstructured":"K. Hose and R. Schenkel . WARP: Workload-aware replication and partitioning for RDF . In ICDEW , 2013 . K. Hose and R. Schenkel. WARP: Workload-aware replication and partitioning for RDF. In ICDEW, 2013."},{"key":"e_1_2_1_7_1","volume-title":"Scalable SPARQL Querying of Large RDF Graphs. PVLDB, 4(11)","author":"Huang J.","year":"2011","unstructured":"J. Huang , D. Abadi , and K. Ren . Scalable SPARQL Querying of Large RDF Graphs. PVLDB, 4(11) , 2011 . J. Huang, D. Abadi, and K. Ren. Scalable SPARQL Querying of Large RDF Graphs. PVLDB, 4(11), 2011."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/2556549.2556571"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453927"},{"key":"e_1_2_1_10_1","volume-title":"H2rdf+: High-performance distributed joins over large-scale rdf graphs","author":"Papailiou N.","year":"2013","unstructured":"N. Papailiou , I. Konstantinou , D. Tsoumakos , P. Karras , and N. Koziris . H2rdf+: High-performance distributed joins over large-scale rdf graphs . In IEEE Big Data , 2013 . N. Papailiou, I. Konstantinou, D. Tsoumakos, P. Karras, and N. Koziris. H2rdf+: High-performance distributed joins over large-scale rdf graphs. In IEEE Big Data, 2013."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11915-1_6"},{"key":"e_1_2_1_12_1","volume-title":"ICDE","year":"2015","unstructured":"Wu, Buwen and Zhou, Yongluan and Yuan, Pingpeng and Liu, Ling and Jin, Hai. Scalable SPARQL Querying using Path Partitioning . In ICDE , 2015 . Wu, Buwen and Zhou, Yongluan and Yuan, Pingpeng and Liu, Ling and Jin, Hai. Scalable SPARQL Querying using Path Partitioning. 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