![]() ![]() Some of the keyĬomponents of our overall system include: Historical querying and analytics over very large, dynamic, heterogeneous, and noisy graphs. In this project, we are building a graph data management system and a suite of tools aimed at supporting real-time and However, graph operations are notoriously hard to parallelize. Use of parallel and distributed solutions, both for efficiency and for better fault-tolerance Number of operations that need to be supported are growing at an unprecedented pace, necessitating Similarly there is increasing interest in continuous query processing and real-timeĪnalytics, especially anomaly or event detection, on streaming graph data. Has also opened up opportunities in temporalĮvolutionary analysis as well as in data mining and comparative analytics over historical The increasing availability of historical traces of time-evolving graphs Of statistical models before querying and analysis. The raw observational network data is also often noisy and needs to cleaned and annotated through use Very large volumes of heterogeneous, complex-structured, and rapidly changing data. Provide declarative frameworks for querying and analyzing such graph-structured data, especially Programming frameworks in recent years, there is still a lack of established data management systems that However, despite much work on graph querying algorithms and graph Network data is most naturally represented as a graph, with nodes representing the entities and edges denoting the ![]() ![]() Real-time ingest, storage, querying, and complex analytics over such network data. There is a growing need for data management systems that can support Social contact graphs are expected to be available for analysis in near future, andĬan potentially be used to gain insights into various social phenomena as well as in disease IP traffic data, or parcel shipment data. Network data arises even in mundane applications like phone call data, Networks, disease transmission networks, ecological food networks, sensor networks, social contact Networks, communication networks, financial transaction networks, citation networks, gene regulatory Over the last decade, information networks have become ubiquitous and widespread. Declarative Graph Analytics and Querying over Very Large, Dynamic Information Networks ![]()
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