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Auflistung nach Schlagwort "Graph databases"

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  • Zeitschriftenartikel
    Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs
    (Datenbank-Spektrum: Vol. 17, No. 2, 2017) Then, Manuel; Günnemann, Stephan; Kemper, Alfons; Neumann, Thomas
    Distance and centrality computations are important building blocks for modern graph databases as well as for dedicated graph analytics systems. Two commonly used centrality metrics are the compute-intense closeness and betweenness centralities, which require numerous expensive shortest distance calculations. We propose batched algorithm execution to run multiple distance and centrality computations at the same time and let them share common graph and data accesses. Batched execution amortizes the high cost of random memory accesses and presents new vectorization potential on modern CPUs and compute accelerators. We show how batched algorithm execution can be leveraged to significantly improve the performance of distance, closeness, and betweenness centrality calculations on unweighted and weighted graphs. Our evaluation demonstrates that batched execution can improve the runtime of these common metrics by over an order of magnitude.
  • Zeitschriftenartikel
    The Hydra.PowerGraph System
    (Datenbank-Spektrum: Vol. 17, No. 2, 2017) Meyer, Holger; Schering, Alf-Christian; Heuer, Andreas
    Directed hypergraphs are known from graph theory [11] and are well understood within their own domain [7–9, 22, 23]. This paper provides an overview on the expressiveness of directed and typed hypergraphs as a modeling paradigm not only for the content of digital libraries and archives but a variety of applications. Furthermore, hypergraphs are sufficiently expressive to provide an implementation logic for conceptual models like CIDOC/CRM [18] in the context of museum-related systems and digital archives.The directed hypergraph model supports typed nodes and individual flexible sets of attributes on a per node type basis. This allows for efficient mapping on object-relational database structures. It also features a flexible, semi-structured type system for hyperedges. The graph model is accompanied by a set of well defined graph operations forming an algebra and a descriptive hypergraph query language GrafL. This language supports typed, structure and value based queries as well as fundamental graph algorithms.The suitability of such a hypergraph-based model is illustrated with a large digital ethnological archive system, which is developed in the WossiDiA project [43, 52, 53].
  • Textdokument
    Understanding Trolls with Efficient Analytics of Large Graphs in Neo4j
    (BTW 2019, 2019) Allen, David; Hodler, Amy; Hunger, Michael; Knobloch, Martin; Lyon, William; Needham, Mark; Voigt, Hannes
    Analytics of large graph data set has become an important means of understanding and influencing the world. The use of graph database technology in the International Consortium of Investigative Journalists’ (ICIJ) investigation of the Panama Papers and Paradise Papers or in cancer research illustrates how analysing graph-structured data helps to uncover important but hidden relationships. A very current example in that regards shows how graph analytics can help shed light on the operations of social media troll-networks, e.g. on Twitter. In similar fashion, graph analytics can help enterprises to unearth hidden patterns and structures within connected data, to make more accurate predictions and faster decisions. All this requires efficient graph analytics well-integrated with management of graph data. The Neo4j Graph Platform provides such an environment. It provides transactional processing and analytical processing of graph data including data management and analytics tooling. A central element for graph analytics in the Graph Platform are the Neo4j graph algorithms. Neo4j graph algorithms provide efficiently implemented, parallel versions of common graph algorithms, integrated and optimized for the Neo4j transactional database. In this paper, we will describe the design and integration Neo4j Graph Algorithms, demonstrate its utility of our approach with a Twitter Troll analysis, and show case its performance with a few experiments on large graphs.