Introduction to algorithms
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mobile wireless computing: challenges in data management
Communications of the ACM
Reducing location update cost in a PCS network
IEEE/ACM Transactions on Networking (TON)
An adaptive data replication algorithm
ACM Transactions on Database Systems (TODS)
Escrow techniques for mobile sales and inventory applications
Wireless Networks
Per-user profile replication in mobile environments: algorithms, analysis, and simulation results
Mobile Networks and Applications
A mobile transaction model that captures both the data and movement behavior
Mobile Networks and Applications
Efficient and flexible location management techniques for wireless communication systems
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
ACM SIGMOBILE Mobile Computing and Communications Review
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Association Rules: Anti-Skew Algorithms
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Querying in Highly Mobile Distributed Environments
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Dynamic and Adaptive Cache Retrieval Scheme for Mobile Computing
COOPIS '98 Proceedings of the 3rd IFCIS International Conference on Cooperative Information Systems
Location Dependent Data and its Management in Mobile Databases
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Teletraffic modeling for personal communications services
IEEE Communications Magazine
Modeling techniques for large-scale PCS networks
IEEE Communications Magazine
Evolution of wireless data services: IS-95 to cdma2000
IEEE Communications Magazine
Optimizing Index Allocation for Sequential Data Broadcasting in Wireless Mobile Computing
IEEE Transactions on Knowledge and Data Engineering
Exploring group mobility for replica data allocation in a mobile environment
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Supporting terminal mobility by means of self-adaptive communication object migration
Proceedings of the 3rd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Complex spatio-temporal pattern queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
On the Effect of Group Mobility to Data Replication in Ad Hoc Networks
IEEE Transactions on Mobile Computing
A Storage Management for Mining Object Moving Patterns in Object Tracking Sensor Networks
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Discovering fuzzy personal moving profiles in wireless networks
Applied Soft Computing
A greedy location management scheme using predictive dynamic reservation into dynamic set method
Proceedings of the 4th Asian Conference on Internet Engineering
Discovering mobile users' moving behaviors in wireless networks
Expert Systems with Applications: An International Journal
Server decision making process for the wireless network environment
IMSA '07 Proceedings of the Eleventh IASTED International Conference on Internet and Multimedia Systems and Applications
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
Applying data mining to wireless networks
ICCOM'06 Proceedings of the 10th WSEAS international conference on Communications
A three criteria data replication scheme using data mining for wireless cellular network
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Exploring regression for mining user moving patterns in a mobile computing system
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
STMPE: an efficient movement pattern extraction algorithm for spatio-temporal data mining
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
An efficient broadcast scheme for wireless data schedule under a new data affinity model
ICOIN'05 Proceedings of the 2005 international conference on Information Networking: convergence in broadband and mobile networking
Mining linguistic mobility patterns for wireless networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Mining generalized spatio-temporal patterns
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Mining Travel Patterns from Geotagged Photos
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining trajectory patterns using hidden Markov models
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
International Journal of Intelligent Information and Database Systems
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In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. As can be seen later, the former is useful for the allocation of read-intensive data objects, whereas the latter is good for the allocation of update-intensive data objects. The data allocation schemes for shared data, which are able to achieve local optimization and global optimization, are also developed. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the knowledge obtained from the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system.