On the spatiotemporal burstiness of terms

Authors:
Theodoros Lappas;Marcos R. Vieira;Dimitrios Gunopulos;Vassilis J. Tsotras
Affiliations:
University of California, Riverside;University of California, Riverside;University of Athens;University of California, Riverside
Venue:
Proceedings of the VLDB Endowment
Year:
2012

Citing 31
Cited 4

On the maximum weight clique problem

Mathematics of Operations Research
Computing the maximum bichromatic discrepancy, with applications to computer graphics and machine learning

Journal of Computer and System Sciences
Optimal aggregation algorithms for middleware

PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Linear Time Algorithm for Finding All Maximal Scoring Subsequences

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Efficient Mining of Spatiotemporal Patterns

SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
A New Simple Linear Algorithm to Recognize Interval Graphs

CG '91 Proceedings of the International Workshop on Computational Geometry - Methods, Algorithms and Applications
Bursty and hierarchical structure in streams

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient elastic burst detection in data streams

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Querying about the Past, the Present, and the Future in Spatio-Temporal Databases

ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Identifying similarities, periodicities and bursts for online search queries

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A generalized framework for mining spatio-temporal patterns in scientific data

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Parameter free bursty events detection in text streams

VLDB '05 Proceedings of the 31st international conference on Very large data bases
A probabilistic approach to spatiotemporal theme pattern mining on weblogs

Proceedings of the 15th international conference on World Wide Web
System for spatio-temporal analysis of online news and blogs

Proceedings of the 15th international conference on World Wide Web
Indexing spatiotemporal archives

The VLDB Journal — The International Journal on Very Large Data Bases
BlogScope: spatio-temporal analysis of the blogosphere

Proceedings of the 16th international conference on World Wide Web
Using Burstiness to Improve Clustering of Topics in News Streams

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Discovery of convoys in trajectory databases

Proceedings of the VLDB Endowment
Mining interesting locations and travel sequences from GPS trajectories

Proceedings of the 18th international conference on World wide web
Trajectory Outlier Detection: A Partition-and-Detect Framework

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On burstiness-aware search for document sequences

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
TwitterStand: news in tweets

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
On-line discovery of flock patterns in spatio-temporal data

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Querying trajectories using flexible patterns

Proceedings of the 13th International Conference on Extending Database Technology
Earthquake shakes Twitter users: real-time event detection by social sensors

Proceedings of the 19th international conference on World wide web
Situation detection and control using spatio-temporal analysis of microblogs

Proceedings of the 19th international conference on World wide web
Searching trajectories by locations: an efficiency study

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Swarm: mining relaxed temporal moving object clusters

Proceedings of the VLDB Endowment
Identifying, attributing and describing spatial bursts

Proceedings of the VLDB Endowment
Efficient mining of emerging events in a dynamic spatiotemporal environment

PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining

STEM: a spatio-temporal miner for bursty activity

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
How the live web feels about events

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Spatio-temporal characteristics of bursty words in Twitter streams

Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
EvenTweet: online localized event detection from twitter

Proceedings of the VLDB Endowment

Quantified Score

Hi-index	0.00

Visualization

Abstract

Thousands of documents are made available to the users via the web on a daily basis. One of the most extensively studied problems in the context of such document streams is burst identification. Given a term t, a burst is generally exhibited when an unusually high frequency is observed for t. While spatial and temporal burstiness have been studied individually in the past, our work is the first to simultaneously track and measure spatiotemporal term burstiness. In addition, we use the mined burstiness information toward an efficient document-search engine: given a user's query of terms, our engine returns a ranked list of documents discussing influential events with a strong spatiotemporal impact. We demonstrate the efficiency of our methods with an extensive experimental evaluation on real and synthetic datasets.