Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
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With the emergence of grid computing, researchers in different fields are making use of the huge computing power of the grid to carry out massive computing tasks that are beyond the power of a single processor. When a computing task (or job) is submitted to the grid, some useful information about the job is logged in the database by the Scheduler. The computing infrastructure that makes up the grid is expensive; hence, it is of great importance to understand the resource usage pattern. In this paper, we propose an incremental attribute-oriented approach that mines data within a given time interval. We test our approach using a real life data of logs of jobs submitted to Western Canada Research Grid (WestGrid). We also develop an incremental attribute-oriented mining tool to implement the proposed approach. Our approach uncovers some hidden patterns and changes that take place over a period of time.