Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

GRADES-NDA 2018 is the merger of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The focus of GRADES-NDA is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped da...

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Bibliographic Details
Corporate Author: Association for Computing Machinery-Digital Library, contributor (contributor)
Other Authors: Roy, Shourya, editor (editor), West, Robert, editor, Fletcher, George, editor, Bhattacharya, Arnab, editor, Arora, Akhil, editor, Larriba-Pey, Josep Lluís, editor
Format: eBook
Language:Inglés
Published: New York NY : ACM 2018.
Series:ACM Conferences
Subjects:
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009713650306719
Description
Summary:GRADES-NDA 2018 is the merger of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The focus of GRADES-NDA is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped data. The workshop is a forum for exchanging ideas and methods for mining, querying and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines. GRADES-NDA aims to present technical contributions inside graph, RDF and other data management systems on massive graphs. The purpose of this workshop is to bring together researchers from academia, industry, and government, (1) to create a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards (2) addressing domain specific challenges or (3) handling noise in real-world graphs.
Physical Description:1 online resource (94 pages)