The week's pick
Random Articles
Reseach Article
Process Mining by using Event Logs
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 116 - Number 19 |
| Year of Publication: 2015 |
| Authors: Swapnali B. Sonawane, Ravi P. Patki |
10.5120/20447-2798
|
Swapnali B. Sonawane, Ravi P. Patki . Process Mining by using Event Logs. International Journal of Computer Applications. 116, 19 ( April 2015), 31-35. DOI=10.5120/20447-2798
Abstract
Process mining techniques have usual notable attention within the literature for their ability to help within the redesign of complex processes by mechanically discovering models that specify the events registered in some log traces provided as input. Process mining refers to the extraction of process models from event logs. Now real-life processes tend to be less structured and a lot of flexible. Traditional process mining algorithms have issues dealing with such unstructured processes and generate "spaghetti-like" process models that are exhausting to understand. An approach to beat this is often to cluster process instances specified every of the ensuing clusters correspond to coherent sets of process instances which will every be adequately represented by a process model. To overcome these issues projected system aims to produce associate automatic means for code engineers to get mined models from systematic event logs specification embrace drawback finding, operating to learn others and technical challenge. This technique at first converts the Systematic Event Logs into some intermediate type like translated tokenized log file and keyword filtered log file. Then this filtered log file format is analyzed to extract the knowledge like Similarity matrix, Frequency count, Most read/write information, database queries and these event logs data measure accustomed build the clusters. Any system would generates the clusters using ActiTraC algorithm to produce refined description of generated models therefore incorrectness and additional overhead in analysis part of model development is removed to extended extent. This is supported on an repetitious, graded, refinement of the process model, where, at every step, traces sharing similar behavior patterns are clustered along and equipped with a specialized schema. The formula guarantees that every refinement results in an progressively sound model, so attaining a monotonic search.
References
- Joachim Herbst: "An Inductive Approach to the Acquisition and Adaptation of Workflow Models" (1999).
- Wil van der aalst, ?-algorithm: "Process mining: Overview and opportunities" (2004).
- B. F. van Dongen and W. M. P. van der Aalst, Instance graphs : "Multi-phase Process mining: Aggregating Instance Graphs into EPCs and Petri Nets" (2005).
- Rakesh Agrawal , Johannes Gehrke , Dimitrios Gunopulos and Prabhakar Raghavan, Hierarchical clustering : "Automatic Subspace Clustering of High Dimensional Data" (2005).
- K. A. de Medeiros, A. J. M. M. Weijters, and W. M. P. van der Aalst, Genetic algorithms : "Genetic process mining: An experimental evaluation" (2007).
- Ferreira et al, Sequence Clustering: "Techniques for Process Mining Sequence clustering" (2007).
- Goedertier et al, Negative events : "Declarative Techniques for Modeling and Mining Business Processes" (2008).
- Christian W. Günther : "Activity Mining by Global Trace Segmentation" (2009).
- R. P. Jagadeesh Chandra Bose (JC) : "Abstractions in Process Mining: A Taxonomy of Patterns" (2009).
- Philip Weber, Behzad Bordbar, and Peter Ti˜no : "A Framework for the Analysis of Process Mining Algorithms" (2013)
- Can Wang, Xiangjun Dong, Fei Zhou, Longbing Cao: "Coupled Attribute Similarity Learning on Categorical Data" (2014)
- Jianmin Wang, Raymond K. Wong, Jianwei Ding, Qinlong Guo, and Lijie Wen : "Efficient Selection of Process Mining Algorithms" (2013)
- Yongkweon Jeon and Sungroh Yoon,"Multi-Threaded Hierarchical Clustering by Parallel Nearest-Neighbor Chaining" (2013)
- Wil van der Aalst, Senior Member, "Service Mining: Using Process Mining to Discover, Check, and Improve Service Behavior"(2013)
- W. M. P. van der Aalst, Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, (2011).
- W. M. P. van der Aalst, A. J. M. M. Weijters, and L. Maruster, "Workflow Mining: Discovering Process Models from Event Logs",(2004).
- R. P. Jagadeesh Chandra Bose and W. M. P. van der Aalst, "Context Aware Trace Clustering: Towards Improving Process Mining Results,"(2009).
- G. Greco, A. Guzzo, L. Pontieri, and D. Sacca', "Discovering Expressive Process Models by Clustering Log Traces," IEEE Trans. Knowledge and Data Eng. , (2006).
- M. Song, C. W. Gu nther, and W. M. P. van der Aalst, "Trace Clustering in Process Mining," (2008).
- A. J. M. M. Weijters, W. M. P. van der Aalst, and A. K. Alves de Medeiros,"Process Mining with the Heuristics miner Algorithm," (2006).
Index Terms
Keywords