The International Workshop on Graph Data Management and Analysis (GDMA 2017)
This workshop will be held in conjunction with APWeb-WAIM 2017 (July 07-09, 2017, Beijing, China)
Recently, there has been a lot of interest in the application of graphs in different domains. They have been widely used for data modeling of different application domains such as multimedia databases, protein networks, social networks and semantic web. With the continued emergence and increase of massive and complex structural graph data, a graph database that efficiently supports elementary data management mechanisms is crucially required to effectively understand and utilize any collection of graphs. On the other hand, it is not easy to analyze these massive graphs due to the complex interconnections between entities in a graph. Moreover, such problems will become much more difficult as the graph data is becoming larger and larger. Thus there is a growing interest in developing analytics and management techniques for graph data.
The overall goal of the workshop is to bring people from different fields together, exchange research ideas and results, and encourage discussion about how to provide efficient graph data management techniques in different application domains and to understand the research challenges of such area.
Topics related to graph data management and query processing are of interest. These include, but not limited to:
Big Graph Processing Systems – developing distributed/external-memory/single-machine graph management systems for processing large graphs.
Graph Data Management – developing graph management techniques to efficiently analyze large graph from different aspects, e.g., semantics web data, geometrical data, social network, business process management, etc.
Graph Query Processing Techniques – developing graph query processing algorithms to efficient processing queries on large graphs, etc., finding top-k relevant vertices, community search, reachability and shortest path queries, etc.
Graph Mining Applications – applying graph mining and graph matching techniques to discover useful knowledge from large graphs (e.g., social network, RDF data, knowledge graph)