Paper Info
Title
Inferring User Context from Spatio-Temporal Pattern Mining for Mobile Application Services
Abstract
Recent research on geographical data mining that focuses on user behavior is lacking some fundamental aspects, measurements rely on large quantities of geographic data and lack contextual information. This work introduces a novel knowledge discovery architecture that brings together machine learning techniques with readily available information from popular Location Social Networks, in order to enrich geographical locations with context and add meaning to user behavior. Results show that through analysis of context enriched data we are capable of inferring context for detected user points of interest and patterns, such as where the user lives, works and spends his free time, without a large quantity of information or prior knowledge of the user and his private data.
Year
DOI
Venue
2012
10.1109/WI-IAT.2012.10
WI-IAT), 2012 IEEE/WIC/ACM International Conferences
Keywords
Field
DocType
data mining,data privacy,geographic information systems,inference mechanisms,learning (artificial intelligence),mobile computing,social networking (online),spatiotemporal phenomena,contextual information,geographical data mining,geographical locations,knowledge discovery architecture,location social networks,machine learning techniques,mobile application services,spatio-temporal pattern mining,user behavior,user context inference,user private data,context extraction,mobile applications,social networks,spatio-temporal mining,user behavior
Data science,Data mining,Contextual information,Data stream mining,Architecture,Social network,Information retrieval,Computer science,Temporal pattern mining,Knowledge extraction,Point of interest
Conference
Volume
ISBN
Citations 
2
978-1-4673-6057-9
0
PageRank 
References 
Authors
0.34
19
2
Name
Order
Citations
PageRank
Daniel Pereira100.34
Luis Loyola2416.84