C4.5: programs for machine learning
C4.5: programs for machine learning
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Charting past, present, and future research in ubiquitous computing
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
Experiences of developing and deploying a context-aware tourist guide: the GUIDE project
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Enabling Technology for Personalizing Mobile Services
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 3 - Volume 3
Location management for mobile commerce applications in wireless Internet environment
ACM Transactions on Internet Technology (TOIT)
Evolution of mobile location-based services
Communications of the ACM - Mobile computing opportunities and challenges
Bluetooth and WAP push based location-aware mobile advertising system
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Supporting mobile decision making with association rules and multi-layered caching
Decision Support Systems
Mobile decision support for in-store purchase decisions
Decision Support Systems
Time-Related Factors of Data Quality in Multichannel Information Systems
Journal of Management Information Systems
Human-Computer Interaction
Experiences from real-world deployment of context-aware technologies in a hospital environment
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Constructs for successful implementation of m-business: an Australian case study
International Journal of Mobile Learning and Organisation
Measurement of analytical knowledge-based corporate memory and its application
Decision Support Systems
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
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Advances in mobile technologies have made the collection of customers' context information feasible. Appropriate customer centric strategies that make use of customers' context data are eagerly awaited by mobile service providers. To address this need, a framework is proposed in this paper that can be used for analyzing customers' context based behavioral data to provide suitable services to customers. Six case studies of context sensitive services are discussed to illustrate how the proposed framework can be used to improve them. Furthermore, simulated experiments are conducted using customers' behavioral data including location and time and it is found that the use of context related data leads to the discovery of deeper and improved knowledge of customers' behavior.