Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Discovering Workflow Performance Models from Timed Logs
EDCIS '02 Proceedings of the First International Conference on Engineering and Deployment of Cooperative Information Systems
RFID Systems and Security and Privacy Implications
CHES '02 Revised Papers from the 4th International Workshop on Cryptographic Hardware and Embedded Systems
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A Pipelined Framework for Online Cleaning of Sensor Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Flowcube: constructing RFID flowcubes for multi-dimensional analysis of commodity flows
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Finding popular categories for RFID tags
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
RFID Tags Without Customers ID in Book Library for Detecting Latent Interest of User Group
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Privacy protection for RFID data
Proceedings of the 2009 ACM symposium on Applied Computing
Joined Q-ary tree anti-collision for massive tag movement distribution
ACSC '10 Proceedings of the Thirty-Third Australasian Conferenc on Computer Science - Volume 102
Unified Q-ary tree for RFID tag anti-collision resolution
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
Discovering event correlation rules for semi-structured business processes
Proceedings of the 5th ACM international conference on Distributed event-based system
LocTrackJINQS: An Extensible Location-aware Simulation Tool for Multiclass Queueing Networks
Electronic Notes in Theoretical Computer Science (ENTCS)
An effective and adaptive data cleaning technique for colossal RFID data sets in healthcare
WSEAS Transactions on Information Science and Applications
RFID-data compression for supporting aggregate queries
ACM Transactions on Database Systems (TODS)
Evaluating the performance of a discrete manufacturing process using RFID: A case study
Robotics and Computer-Integrated Manufacturing
Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port
Expert Systems with Applications: An International Journal
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Radio Frequency Identification (RFID) technology is fast becoming a prevalent tool in tracking commodities in supply chain management applications. The movement of commodities through the supply chain forms a gigantic workflow that can be mined for the discovery of trends, flow correlations and outlier paths, that in turn can be valuable in understanding and optimizing business processes.In this paper, we propose a method to construct compressed probabilistic workflows that capture the movement trends and significant exceptions of the overall data sets, but with a size that is substantially smaller than that of the complete RFID workflow. Compression is achieved based on the following observations: (1) only a relatively small minority of items deviate from the general trend, (2)only truly non-redundant deviations, ie, those that substantially deviate from the previously recorded ones, are interesting, and (3) although RFID data is registered at the primitive level, data analysis usually takes place at a higher abstraction level. Techniques for workflow compression based on non-redundant transition and emission probabilities are derived; and an algorithm for computing approximate path probabilities is developed. Our experiments demonstrate the utility and feasibility of our design, data structure, and algorithms.