Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Similarity based method for manufacturing process performance prediction and diagnosis
Computers in Industry
Design of a reconfigurable prognostics platform for machine tools
Expert Systems with Applications: An International Journal
EKNOW '10 Proceedings of the 2010 Second International Conference on Information, Process, and Knowledge Management
Towards a process model for identifying knowledge-related structures in product data
PAKM'06 Proceedings of the 6th international conference on Practical Aspects of Knowledge Management
On comparing bills of materials: a similarity/distance measure for unordered trees
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Events, neural systems and time series
ServiceWave'10 Proceedings of the 2010 international conference on Towards a service-based internet
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Today's enterprises have complex manufacturing processes with several automation systems. These systems generate enormous amount of data in real-time representing feedbacks, positions, and alerts, among others. This data can be stored in relational databases as historical data which can be used for product tracking and genealogy, and so forth. However, historical data is not been utilized to proactively control the manufacturing processes. The current contribution proposes a novel methodology to overcome the aforementioned drawback. The methodology encompasses three process steps. First, offline identification of critical control-related parameters of manufacturing processes and defining a case base utilizing previously identified process parameters. Second, update the case base with real-time data acquired from automation systems during execution of manufacturing processes. Finally, employ similarity search algorithms to retrieve similar cases from the case base and adapt the retrieved cases to control the manufacturing processes proactively. The proposed methodology is validated to proactively control the manufacturing process of a molding machine.