Database Research Challenges and Opportunities of Big Graph Data

摘要

Large volumes of graph-structured data are becoming increasingly prevalent in areas such as 1.social and professional network analysis. 2.recommendation services, such as product advertisement, news and media alerts, learning resource recommendation, itinerary recommendation. 3.scientific computing: life and health sciences, physical sciences. 4.crime investigation and intelligence gathering. 5.telecoms network management, for dependency analysis, root cause analysis, location-based service provision. 6.linked open data. 7.geospatial data. 8.business process management: logistics, finance chains, fraud detection, risk analysis, asset management. 9.organization management. Graph-structured data differs from other "big" data in its greater focus on the relationships between entities, regarding these relationships as important as the entities themselves, and allowing the possibility of modelling the attributes of relationships just as for entities, specifying constraints on relationships, and undertaking querying, analysis and reasoning over relationships.