Fast parallel algorithms for graph matching problems
Fast parallel algorithms for graph matching problems
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Graph OLAP: Towards Online Analytical Processing on Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A survey of graph edit distance
Pattern Analysis & Applications
Graph cube: on warehousing and OLAP multidimensional networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Adding regular expressions to graph reachability and pattern queries
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
SAP HANA database: data management for modern business applications
ACM SIGMOD Record
Efficient transaction processing in SAP HANA database: the end of a column store myth
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Hi-index | 0.00 |
Graph-structured data is ubiquitous and with the advent of social networking platforms has recently seen a significant increase in popularity amongst researchers. However, also many business applications deal with this kind of data and can therefore benefit greatly from graph processing functionality offered directly by the underlying database. This paper summarizes the current state of graph data processing capabilities in the SAP HANA database and describes our efforts to enable large graph analytics in the context of our research project SynopSys. With powerful graph pattern matching support at the core, we envision OLAP-like evaluation functionality exposed to the user in the form of easy-to-apply graph summarization templates. By combining them, the user is able to produce concise summaries of large graph-structured datasets. We also point out open questions and challenges that we plan to tackle in the future developments on our way towards large graph analytics.