Activity Mining for Airport Event Logs Based on RankClus Algorithm

XU Tao; MENG Ye; LU Min

doi:10.11999/JEIT151137

Volume 38 Issue 8

Sep. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 2033-2039

XU Tao, MENG Ye, LU Min . Activity Mining for Airport Event Logs Based on RankClus Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2033-2039. doi: 10.11999/JEIT151137

Citation:

XU Tao, MENG Ye, LU Min . Activity Mining for Airport Event Logs Based on RankClus Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2033-2039. doi: 10.11999/JEIT151137

XU Tao, MENG Ye, LU Min . Activity Mining for Airport Event Logs Based on RankClus Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2033-2039. doi: 10.11999/JEIT151137

Citation:

XU Tao, MENG Ye, LU Min . Activity Mining for Airport Event Logs Based on RankClus Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2033-2039. doi: 10.11999/JEIT151137

PDF( 473 KB)

Activity Mining for Airport Event Logs Based on RankClus Algorithm

doi: 10.11999/JEIT151137

XU Tao¹,
MENG Ye²,
LU Min ¹

1.
2.
(College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China)

Funds:

The National Natural Science Foundation of China (61502499), The Civil Aviation Key Technologies RD Program of China (MHRD20140105), The Fundamental Research Funds for the Central Universities of China (3122013C005, 3122014D032, 3122015D015), The Scientific Research Foundation from Civil Aviation University of China (2013QD18X), The Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (CAAC-ITRB-201401)

Received Date: 2015-10-10
Rev Recd Date: 2016-04-15
Publish Date: 2016-08-19

Abstract

Abstract

Process mining is a technology which can extract non-trivial and useful information from airport event logs. However, the airport event logs are always on a detailed level of abstraction, which may not be in line with the expected abstract level of an analyst. Process models generated by these event logs are always spaghetti-like and too hard to comprehend. An approach to overcome this issue is to group low-level events into clusters, which represent the execution of a higher-level activity in the process model. Therefore, this paper presents a new activity mining method which is based on RankClus algorithm to generate activity clusters integrated with ranking. On this basis, the activity-clustered model which is easier to comprehend can be constructed. The experiment results show that this activity-clustered model, which shares a similar level of conformance with the meta model, is significantly less complex.
- Process mining,
- Activity mining,
- RankClus,
- Trace clustering

FullText(HTML)

References(20)

References

VAN DER AALST W M P. Process mining: Overview and opportunities[J]. ACM Transactions on Management Information Systems, 2012, 3(2): 1-17. doi: 10.1145/2229156. 2229157.

LANZ A, WEBER B, and REICHERT M. Time patterns for process-aware information systems[J]. Requirements Engineering, 2014, 19(2): 113-141. doi: 10.1007/s00766-012- 0162-3.

BOSE R P J C, VAN DER AALST W M P, ZLIOBAITE I, et al. Dealing with concept drifts in process mining[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(1): 154-171. doi: 10.1109/TNNLS.2013.2278313.

GNTHER C W, ROZINAT A, and VAN DER AALST W M P. Activity mining by global trace segmentation[C]. Proceedings of the 8th International Conference on Business Process Management, Hoboken, 2010: 128-139. doi: 10.1007/ 978-3-642-12186-9_13.

DESAI N, BHAMIDIPATY A, SHARMA B, et al. Process trace identification from unstructured execution logs[C]. Proceedings of the 7th International Conference on Services Computing, Miami, 2010: 17-24. doi: 10.1109/SCC.2010.86.

BAIER T, MENDLING J, and WESKE M. Bridging abstraction layers in process mining[J]. Information Systems, 2014, 46(12): 123-139. doi: 10.1016/j.is.2014.04.004.

SONG M, GNTHER C W, and VAN DER AALST W M P. Trace clustering in process mining[C]. Proceedings of the 7th International Conference on Business Process Management, Ulm, 2009: 109-120. doi: 10.1007/978-3-642-00328-8_11.

BOSE R P J C and VAN DER AALST W M P. Context aware trace clustering: towards improving process mining results[C]. Proceedings of the 2009 SIAM Data Mining Conference, Sparks, 2009: 401-412. doi: 10.1137/1. 9781611972795.35.

BOSE R P J C and VAN DER AALST W M P. Trace clustering based on conserved patterns: Towards achieving better process models[C]. Proceedings of the 8th International Conference on Business Process Management, Hoboken, 2010: 170-181. doi: 10.1007/978-3-642-12186-9_16.

SUN Y, HAN J, ZHAO P, et al. Rankclus: integrating clustering with ranking for heterogeneous information network analysis[C]. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, Saint-Petersburg, 2009: 565-576. doi: 10.1145/1516360.1516426.

FERREIRA D R, SZIMANSKI F, and RALHA C G. Improving process models by mining mappings of low-level events to high-level activities[J]. Journal of Intelligent Information Systems, 2014, 43(2): 379-407. doi: 10.1007/ s10844-014-0327-2.

SHAN S, WANG L, and LI L. Modeling of emergency response decision-making process using stochastic Petri net: an e-service perspective[J]. Information Technology and Management, 2012, 13(4): 363-376. doi: 10.1007/s10799- 012-0128-7.

陈季梦, 陈佳俊, 刘杰, 等. 基于结构相似度的大规模社交网络聚类算法[J]. 电子与信息学报, 2015, 37(2): 449-454. doi: 10.11999/JEIT140512.

CHEN Jimeng, CHEN Jiajun, LIU Jie, et al. Clustering algorithms for large-scale social networks based on structural similarity[J]. Journal of Electronics Information Technology, 2015, 37(2): 449-454. doi: 10.11999/JEIT140512.

陈丽敏, 杨静, 张健沛. 一种基于嵌入技术的异构信息网络的快速聚类算法[J]. 电子与信息学报, 2015, 37(11): 2634-2641. doi: 10.11999/JEIT150106.

CHEN Limin, YANG Jing, and ZHANG Jianpei. A fast clustering algorithm based on embedding technology for heterogeneous information networks[J]. Journal of Electronics Information Technology, 2015, 37(11): 2634-2641. doi: 10.11999/JEIT150106.

LEEMANS S J J, FAHLAND D, and VAN DER AALST W M P. Discovering block-structured process models from event logs containing infrequent behaviour[C]. Proceedings of the 11th International Conference on Business Process Management, Eindhoven, 2014: 66-78. doi: 10.1007/978-3- 319-06257-0_6.

GRABBE S R, SRIDHAR B, and MUKHERJEE A. Clustering days with similar airport weather conditions[C]. Proceedings of the 14th AIAA Aviation Technology, Integration, and Operations Conference, Atlanta, 2014: 2014-2712. doi: 10.2514/6.2014-2712.

JOHNSTONE M, LE V T, ZHANG J, et al. A dynamic time warped clustering technique for discrete event simulation- based system analysis[J]. Expert Systems with Applications, 2015, 42(21): 8078-8085. doi: 10.1016/j.eswa.2015.06.040.

ADRIANSYAH A, SIDOROVA N, and VAN DONGEN B F. Cost-based fitness in conformance checking[C]. Proceedings of the 11th International Conference on Application of Concurrency to System Design, Kanazawa, 2011: 57-66. doi: 10.1109/ACSD.2011.19.

Relative Articles

Supplements(0)

Cited By

Proportional views