ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Parallel computation of high dimensional robust correlation and covariance matrices
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
bitSPADE: A Lattice-based Sequential Pattern Mining Algorithm Using Bitmap Representation
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Recommendation via Query Centered Random Walk on K-Partite Graph
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A General Model for Sequential Pattern Mining with a Progressive Database
IEEE Transactions on Knowledge and Data Engineering
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems
IEEE Transactions on Knowledge and Data Engineering
Improvements of incspan: incremental mining of sequential patterns in large database
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Apriori-based frequent itemset mining algorithms on MapReduce
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Computing n-gram statistics in MapReduce
Proceedings of the 16th International Conference on Extending Database Technology
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The progressive sequential pattern mining problem has been discussed in previous research works With the increasing amount of data, single processors struggle to scale up Traditional algorithms running on a single machine may have scalability troubles Therefore, mining progressive sequential patterns intrinsically suffers from the scalability problem In view of this, we design a distributed mining algorithm to address the scalability problem of mining progressive sequential patterns The proposed algorithm DPSP, standing for Distributed Progressive Sequential Pattern mining algorithm, is implemented on top of Hadoop platform, which realizes the cloud computing environment We propose Map/Reduce jobs in DPSP to delete obsolete itemsets, update current candidate sequential patterns and report up-to-date frequent sequential patterns within each POI The experimental results show that DPSP possesses great scalability and consequently increases the performance and the practicability of mining algorithms.