In this short paper, we provide an early look at the LDBC Social Network Benchmark's Business Intelligence (BI) workload which tests graph data management systems on a graph business analytics workload. Its queries involve complex aggregations and navigations (joins) that touch large data volumes, which is typical in BI workloads, yet they depend heavily on graph functionality such as connectivity tests and path finding. We outline the motivation for this new benchmark, which we derived from many interactions with the graph database industry and its users, and situate it in a scenario of social network analysis. The workload was designed by taking into account technical "chokepoints" identified by database system architects from academia and industry, which we also describe and map to the queries. We present reference implementations in openCypher, PGQL, SPARQL, and SQL, and preliminary results of SNB BI on a number of graph data management systems.
We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity.
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.
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