The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Stream Data Management: Research Directions and Opportunities
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
ACM SIGMOD Record
Supporting sliding window queries for continuous data streams
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
SQLST: a spatio-temporal data model and query language
ER'00 Proceedings of the 19th international conference on Conceptual modeling
Measurement and gender-specific analysis of user publishing characteristics on myspace
IEEE Network: The Magazine of Global Internetworking
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Online social networks (OSNs) have become popular platforms for people to interact with each other in the cyber space. Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. We demonstrate the effectiveness of our system using a recent result of predicting seasonal flu trends using Twitter data.