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Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) / Akhil Arora, Arnab Bhattacharya, George Fletcher, editors.
- Format:
- Book
- Series:
- ACM international conference proceedings series.
- ACM international conference proceedings series
- Language:
- English
- Subjects (All):
- Data mining--Congresses.
- Data mining.
- Database management--Congresses.
- Database management.
- Graph theory Data processing.
- Physical Description:
- 1 online resource (88 pages) : illustrations.
- Other Title:
- Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems
- Place of Publication:
- New York, NY : Association for Computing Machinery, 2019.
- Summary:
- We are delighted to present the papers from the 2nd GRADES-NDA Joint Workshop on Graph Data Management Experiences & Systems and Network Data Analytics, which took place on 30th June, 2019 co-located with the ACM SIGMOD conference in Amsterdam, Netherlands. GRADES-NDA 2019 is the second joint meeting 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
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