Paper Info
Title
Geospatial Multimedia Data for Situation Recognition.
Abstract
Many emerging problems increasingly rely on integrating complex heterogeneous sensor streams, ranging from photos, texts, environmental data streams and other participating human sensors. The diversity of data types from disparate sensors poses a major challenge in data aggregation and assimilation. EventShop was originally designed for situation recognition using diverse data sources. We propose and develop new interpolation and prediction models on top of EventShop, allowing for effective ingesting and combining appropriate data streams to improve data quality and predict specific situations. We also incorporate data from participatory sensing into the system. The synergy of data gives powerful insight into better understanding of evolving situations, in which participatory sensing is integrated with the surrounding environment. Furthermore, the enhanced system is used for two real-world problems: asthma risk management and smart city.
Year
DOI
Venue
2016
10.1145/2964284.2971472
ACM Multimedia
Field
DocType
Citations 
Geospatial analysis,Data science,Data stream mining,Data quality,Computer science,Data type,Smart city,Environmental data,Multimedia,Participatory sensing,Data aggregator
Conference
0
PageRank 
References 
Authors
0.34
13
1
Name
Order
Citations
PageRank
Mengfan Tang1363.81