Graph Database Management systems (GDBs) are gaining popularity. They are used to analyze huge graph datasets that are naturally appearing in many application areas to model interrelated data. The objective of this paper is to raise a new topic of discussion in the benchmarking community and allow practitioners having a set of basic guidelines for GDB benchmarking. We strongly believe that GDBs will become an important player in the market field of data analysis, and with that, their performance and capabilities will also become important. For this reason, we discuss those aspects that are important from our perspective, i.e. the characteristics of the graphs to be included in the benchmark, the characteristics of the queries that are important in graph analysis applications and the evaluation workbench.
The increasing amount of graph like data from social networks, science and the web has grown an interest in analyzing the relationships between different entities. New specialized solutions in the form of graph databases, which are generic and able to adapt to any schema as an alternative to RDBMS, have appeared to manage attributed multigraphs efficiently. In this paper, we describe the internals of DEX graph database, which is based on a representation of the graph and its attributes as maps and bitmap structures that can be loaded and unloaded efficiently from memory. We also present the internal operations used in DEX to manipulate these structures. We show that by using these structures, DEX scales to graphs with billions of vertices and edges with very limited memory requirements. Finally, we compare our graph-oriented approach to other approaches showing that our system is better suited for out-of-core typical graph-like operations.Peer ReviewedPostprint (published version
The Linked Data Benchmark Council (LDBC) is an EU project that aims to develop industry-strength benchmarks for graph and RDF data management systems. It includes the creation of a non-profit LDBC organization, where industry players and academia come together for managing the development of benchmarks as well as auditing and publishing official results. We present an overview of the project including its goals and organization, and describe its process and design methodology for benchmark development. We introduce so-called "choke-point" based benchmark development through which experts identify key technical challenges, and introduce them in the benchmark workload. Finally, we present the status of two benchmarks currently in development, one targeting graph data management systems using a social network data case, and the other targeting RDF systems using a data publishing case.
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