A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Just-in-time data integration in action
Proceedings of the VLDB Endowment
Linked data query processing strategies
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
A survey of schema-based matching approaches
Journal on Data Semantics IV
DrillBeyond: enabling business analysts to explore the web of open data
Proceedings of the VLDB Endowment
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Integrating up-to-date information into databases from different heterogeneous data sources is still a time-consuming and mostly manual job that can only be accomplished by skilled experts. For this reason, enterprises often lack information regarding the current market situation, preventing a holistic view that is needed to conduct sound data analysis and market predictions. Ironically, the Web consists of a huge and growing number of valuable information from diverse organizations and data providers, such as the Linked Open Data cloud, common knowledge sources like Freebase, and social networks. One desirable usage scenario for this kind of data is its integration into a single database in order to apply data analytics. However, in today's business intelligence tools there is an evident lack of support for so-called situational or ad-hoc data integration. What we need is a system which 1) provides a flexible storage of heterogeneous information of different degrees of structure in an ad-hoc manner, and 2) supports mass data operations suited for data analytics. In this paper, we will provide our vision of such a system and describe an extension of the well-studied property graph model that allows to "integrate and analyze as you go" external data exposed in the RDF format in a seamless manner. The proposed integration approach extends the internal graph model with external data from the Linked Open Data cloud, which stores over 31 billion RDF triples (September 2011) from a variety of domains.