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Themenheft Datenbank-Spektrum (2/2017) - Big Graph Data Management

 

A graph is an intuitive mathematical abstraction to capture how things are connected. In the past decade, the focal point in many data management applications has shifted from individual entities and aggregations thereof toward the connection between entities. Hence today, the graph abstraction is appealing as a natural data model foundation for an increasing range of use cases in interactive as well as analytical graph data management scenarios. Graph-specific use cases can be found in various domains, such as social network analysis, product recommendations, and knowledge graphs. Graph oriented scenarios also emerge in more traditional enterprise scenarios, such as supply chain management or business process analysis. Therefore, the database community reacts to this newly sparked interest in graph data management with a vast number of projects in research as well as in industry.

Graph management use cases pose novel and unique challenges to data management systems. On the operational side, typical interactive queries involve transitive closure computation along paths. Common analytical measures, such as page rank and other vertex centrality measures are also significantly more complex than traditional group by/ aggregate queries. From a data structure perspective, the irregular and skewed structure of graphs makes it challenging to achieve a good distribution over non-uniform memory access or cluster nodes for efficient parallelization – particularly, if the graph is large and changing over time. Further challenges among others are declarative graph analytics abstractions for static as well as for dynamic graphs, graph-query-aware optimization strategies, topology indexing, temporal topology indexing, topology estimation, materialized view usage, and maintenance for graph analytical measures.

Graph data management is an exciting research field, now and for the years to come. This special issue aims at exhibiting our community’s current work in the field. We therefore welcome contributions from research and industry that provide original research on the problems mentioned above or that are generally related to big graph data management and processing. We also welcome case studies that showcase the challenges of graph management and graph query processing from a practical perspective, point out particular research questions, and potentially outline novel research directions.

We are looking for contributions from researchers and practitioners in the above described context, which may be submitted in German or in English.

Possible Topics

  • Anfrageverarbeitung und -optimierung
  • Graph query processing (traversal, reachability, pattern matching, etc.)
  • Graph-query-aware optimization
  • Reachability- and topology indexing
  • Temporal graphs
  • Graph summarization
  • Graph analysis & mining
  • Graph processing and analytics infrastructures
  • Graph distribution and partitioning

Dates

  • Notice of intent for a contribution: December 15th, 2016
  • Deadline for submissions: February 1st, 2017

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