{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T22:38:42Z","timestamp":1778279922807,"version":"3.51.4"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>\n            Property graphs are becoming pervasive in a variety of graph processing applications using interconnected data. They allow to encode multi-labeled nodes and edges, as well as their properties, represented as key\/value pairs. Although property graphs are widely used in several open-source and commercial graph databases, they lack a schema definition, unlike their relational counterparts. The property graph schema discovery problem consists of extracting the underlying schema concepts and types from such graph datasets. We showcase DiscoPG, a system for efficiently and accurately discovering and exploring property graph schemas. To this end, it leverages hierarchical clustering using a Gaussian Mixture Model, which accounts for both node labels and properties. DiscoPG allows users to perform schema discovery for both static and dynamic graph datasets. Suitable visualization layouts and dedicated dashboards enable the user perception of the static and dynamic inferred schema on the node clusters, as well as the differences in runtimes and clustering quality. To the best of our knowledge, DiscoPG is the\n            <jats:italic>first system to tackle the property graph schema discovery problem.<\/jats:italic>\n            As such, it supports the insightful exploration of the graph schema components and their evolving behavior, while revealing the underpinnings of the clustering-based discovery process.\n          <\/jats:p>","DOI":"10.14778\/3554821.3554867","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3654-3657","source":"Crossref","is-referenced-by-count":14,"title":["DiscoPG"],"prefix":"10.14778","volume":"15","author":[{"given":"Angela","family":"Bonifati","sequence":"first","affiliation":[{"name":"Lyon 1 University"}]},{"given":"Stefania","family":"Dumbrava","sequence":"additional","affiliation":[{"name":"Inst. Polytech. de Paris"}]},{"given":"Emile","family":"Martinez","sequence":"additional","affiliation":[{"name":"ENS Lyon"}]},{"given":"Fatemeh","family":"Ghasemi","sequence":"additional","affiliation":[{"name":"ENS Lyon"}]},{"given":"Malo","family":"Jaffr\u00e9","sequence":"additional","affiliation":[{"name":"ENS Lyon"}]},{"given":"Pac\u00f4me","family":"Luton","sequence":"additional","affiliation":[{"name":"ENS Lyon"}]},{"given":"Thomas","family":"Pickles","sequence":"additional","affiliation":[{"name":"ENS Lyon"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"The Property Graph Database Model. In AMW (CEUR Workshop Proceedings)","volume":"2100","author":"Angles Renzo","year":"2018","unstructured":"Renzo Angles . 2018 . The Property Graph Database Model. In AMW (CEUR Workshop Proceedings) , Vol. 2100 . CEUR-WS.org. Renzo Angles. 2018. The Property Graph Database Model. In AMW (CEUR Workshop Proceedings), Vol. 2100. CEUR-WS.org."},{"key":"e_1_2_1_2_1","unstructured":"Angela Bonifati Stefania Dumbrava and Nicolas Mir. 2022. Hierarchical Clustering for Property Graph Schema Discovery. In EDBT. 449--453.  Angela Bonifati Stefania Dumbrava and Nicolas Mir. 2022. Hierarchical Clustering for Property Graph Schema Discovery. In EDBT. 449--453."},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Angela Bonifati George H. L. Fletcher Hannes Voigt and Nikolay Yakovets. 2018. Querying Graphs. Morgan & Claypool Publishers.  Angela Bonifati George H. L. Fletcher Hannes Voigt and Nikolay Yakovets. 2018. Querying Graphs. Morgan & Claypool Publishers.","DOI":"10.1007\/978-3-031-01864-0"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Redouane Bouhamoum Zoubida Kedad and St\u00e9phane Lopes. 2021. Incremental Schema Discovery at Scale for RDF Data. In ESWC. 195--211.  Redouane Bouhamoum Zoubida Kedad and St\u00e9phane Lopes. 2021. Incremental Schema Discovery at Scale for RDF Data. In ESWC. 195--211.","DOI":"10.1007\/978-3-030-77385-4_12"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Orri Erling Alex Averbuch and etal 2015. The LDBC Social Network Benchmark: Interactive Workload. In SIGMOD. 619--630.  Orri Erling Alex Averbuch and et al. 2015. The LDBC Social Network Benchmark: Interactive Workload. In SIGMOD. 619--630.","DOI":"10.1145\/2723372.2742786"},{"key":"e_1_2_1_6_1","unstructured":"HealthECCO. 2021. CovidGraph. https:\/\/covidgraph.org\/ (visited: 14-06-2022).  HealthECCO. 2021. CovidGraph. https:\/\/covidgraph.org\/ (visited: 14-06-2022)."},{"key":"e_1_2_1_7_1","unstructured":"Han\u00e2 Lbath Angela Bonifati and Russ Harmer. 2021. Schema Inference for Property Graphs. In EDBT. 499--504.  Han\u00e2 Lbath Angela Bonifati and Russ Harmer. 2021. Schema Inference for Property Graphs. In EDBT. 499--504."},{"key":"e_1_2_1_8_1","volume-title":"Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das.","author":"Rahman Md Farhadur","year":"2017","unstructured":"Md Farhadur Rahman , Weimo Liu , Saad Bin Suhaim , Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das. 2017 . Density Based Clustering over Location Based Services. In ICDE. 461--469. Md Farhadur Rahman, Weimo Liu, Saad Bin Suhaim, Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das. 2017. Density Based Clustering over Location Based Services. In ICDE. 461--469."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-019-00548-x"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3434642"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Shinya Takemura and etal 2015. Synaptic circuits and their variations within different columns in the visual system of Drosophila. PNAS 112 (2015).  Shinya Takemura and et al. 2015. 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PNAS 112 (2015).","DOI":"10.1073\/pnas.1509820112"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554867","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:33:00Z","timestamp":1672227180000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554867"}},"subtitle":["property graph schema discovery and exploration"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":11,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554867"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554867","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}