Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Spatio-temporal conceptual models: data structures + space + time
Proceedings of the 7th ACM international symposium on Advances in geographic information systems
Multidimensional binary search trees used for associative searching
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Spatiotemporal Data Modeling and Management: A Survey
TOOLS '00 Proceedings of the 36th International Conference on Technology of Object-Oriented Languages and Systems (TOOLS-Asia'00)
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Traveling the Semantic Web through Space, Time, and Theme
IEEE Internet Computing
Hosted Universal Integration on the Web: The mashArt Platform
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
From microblogs to social images: event analytics for situation assessment
Proceedings of the international conference on Multimedia information retrieval
Social pixels: genesis and evaluation
Proceedings of the international conference on Multimedia
IT Professional
Eventshop: from heterogeneous web streams to personalized situation detection and control
Proceedings of the 3rd Annual ACM Web Science Conference
Building health persona from personal data streams
Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
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Web Observatories must address fundamental societal challenges using enormous volumes of data being created due to the significant progress in technology. The proliferation of heterogeneous data streams generated by social media, sensor networks, internet of things, and digitalization of transactions in all aspect of humans? life presents an opportunity to establish a new era of networks called Social Life Networks (SLN). The main goal of SLN is to connect People to Resources effectively, efficiently, and promptly in given Situations. Towards this goal, we present a computing framework, called EventShop, to recognize evolving situations from massive web streams in real-time. These web streams can be fundamentally considered as spatio-temporal-thematic streams and can be combined using a set of generic spatio-temporal analysis operators to recognize evolving situations. Based on the detected situations, the relevant information and alerts can be provided to both individuals and organizations. Several examples from the real world problems have been developed to test the efficacy of EventShop framework.