Data Management In Order To Extract Knowledge

Authors

1 Master of Arts (MA), Islamic Azad University, Science and Research Branch, Tehran, Iran.

2 Assistant Professor, Islamic Azad University, Science and Research Branch, Tehran, Iran

3 Assistant Professor, Faculty of Industrial Engineering, Khajeh Nassir-Al-Deen Toosi (K. N. Toosi) University, Tehran, Iran.

Abstract

Today, the effective role of information technology in intelligent transportation systems is
not limited to the use of IT approaches to set up and implement these systems, but also to
collect, manage and analyze information from it, so achieving the goals of ITS without
studying the data obtained It is impossible. Data derived from the use of intelligent
transport systems has three prominent features. First of all, huge amounts of data are
generated due to the use of a variety of sensors, cameras, global positioning systems
(GPS), spatial information management systems (GIS), and moreover, the problem of
storage, analysis and applications of these data presents serious challenges. Second, data is
produced and changed at an unprecedented pace. Encountering a variety of data streams
and rapid response to other challenge variations is considered. The use of information and
communication technology in intelligent transport infrastructure hardware hard ware and
the ability to store data types transmitted from this equipment has led. Massive database of
information on the activities of this equipment. Transmitted data from cameras contains
useful information that, if analyzed, can be used for prediction, control and management.
The analysis of these bulky data is not possible by manual methods; data mining provides
the ability to extract knowledge from the mass of information by using automated
solutions. This paper presents an effective model for extracting knowledge from plaquecamera data. Implementing this model is a valuable knowledge of the behavior of the
drivers of the public passenger transport fleet.

Keywords

References

 
- ندیمی شهرکی، م.ح.، تاکی، م.، حبیب‌الله‌ی، ف.، (1393)، "داده‌کاوی مفاهیم و کاربردها"، نجف‌آباد، نشر دانشگاه آزاد.
-"تحلیل وضعیت تردد اتوبوس­ها در آزاد راه تهران – قم با استفاده از سامانه های هوشمند ثبت تخلف"، (1395)­، مرکز مدیریت راههای کشور.
 
- Anqiang Huang, Lingling Zhang, Zhengxiang Zhu and YongShi, (2009), ”Data Mining Integrated with Domain Knowledge”, in Cutting-Edge Research Topics on Multiple Criteria Decision Making, Communications in Computer and InformationScience, vol. 35, Springer Berlin Heidelberg, pp.184.
 
- Harrison A., and Van Hoek, R., (2002),”Logistics Management and Strategy”, International Logistic: A Supply Chain Approach Financial Times Prentice Hall, pp. 3- 45.
 
- Crespo Marquez, A., (2005), “Applications and Case studies, Modeling Critical failures maintenance: a case study for mining”, Journal of Quality in Maintenance Engineering, Vol. 11 Issue 4,
pp.301 – 317.
 
- Cao Zhang, Huang and Gang Zong, Y. (2010), “Study on the Application of Knowledge Discovery in Databases to Decision Making of Railway Traffic Safety in China”.
 
-Chang-Yi Chen, Tien-Yin Chou, Ching-Yun Mu, Bing-Jean Lee, Magesh and Hsien Chao, (2011),  “Using Data Mining Techniques on Fleet Management System”, Esri International User Conference.
 
-Daniel T. Larose, (2005), “Discovering Knowledge Data, 2nd ed. Publisher JohnWiley& Sons”, 2005,
pp. 4-7.
 
-Ding Pan,” A Formal framework for Data Mining Process Model”. IEEE PACIIA 2009 in press.
 
-Kohavi R., “Data Mining and Visualization”, In: Sixth Annual Symposium on Frontiers of Engineering, National Academy Press, pp.31-40.
 
-Lily Sun, Cleopa John Mushi, (2010), “Case-based analysis in user requirements modeling for knowledge construction”,  Journal of Information and Software Technology, Vol. 52, Issue 7, July.
 
-Liu Xu, (2007), “Guojun Mao.An Algorithm to Approximately Mine Frequent Closed Itemsets from Data Streams”. ActaElectronica Sinica.
 
-Longbing Cao, Dan Luo and Chengqi Zhang, (2007), “Knowledge action ability:satisfying technical and business Interestingness.”IEEE Trans.International Journal Business Intelligence and Data Mining, Vol.2, No.4.
 
-Longbing Cao, (2008), “Domain Driven Data Mining: Challenges and Prospects.”Journal on Knowledge and Data Engineering. Vol. 1, No. 1  January.
 
-Sudhir Kumar Barai, (2003),“Data Mining Applications in Transportation Engineering”, Transport, Vol. XVIII, No. 5, pp. 216-223.
 
-WenQun Wang, Haibo and Magaret, (2005),“Vehicle Breakdown DurationModeling.”Journal of Transportation and Statistics. Vol. 8, Number 1.
 
-William R. King, Peter V. Marks, Jr., and Scott McCoy, (2002),“The Most Important Issues in Knowledge Management.” Communications of the ACM, Vol. 45, No 49, September.

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