Eventshop: from heterogeneous web streams to personalized situation detection and control

Authors:
Mingyan Gao;Vivek K. Singh;Ramesh Jain
Affiliations:
University of California, Irvine;University of California, Irvine;University of California, Irvine
Venue:
Proceedings of the 3rd Annual ACM Web Science Conference
Year:
2012

Citing 1
Cited 5

Social pixels: genesis and evaluation

Proceedings of the international conference on Multimedia

Situation recognition: an evolving problem for heterogeneous dynamic big multimedia data

Proceedings of the 20th ACM international conference on Multimedia
From health-persona to societal health

Proceedings of the 22nd international conference on World Wide Web companion
EventShop: recognizing situations in web data streams

Proceedings of the 22nd international conference on World Wide Web companion
Social life networks: a multimedia problem?

Proceedings of the 21st ACM international conference on Multimedia
Situation fencing: making geo-fencing personal and dynamic

Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Web now has enormous volume of heterogeneous data being continuously reported by different sensors and humans from different locations. These data flows can be considered as spatio-temporal-thematic streams. Combined effectively, these streams can be used for detecting situations and saving lives and resources. We describe a system to combine streams from heterogeneous data sources, process them to detect situations, and use the detected situations to aid millions of users. This system uses a unified data model to integrate different web streams, and provides a set of generic operators to detect spatio-temporal characteristics of individual or combined data streams to detect complex situations. The detected situations can be combined with user parameters to provide personalized information and action alerts.