Mining frequent trajectory patterns of WIP in Internet of Things-based spatial-temporal database. Issue 12 (2nd December 2017)

• Record Type: Journal Article
• Authors: Cai, Haoshu; Guo, Yu; Yang, Wen-An; Lu, Kun
• Is Part Of: International journal of computer integrated manufacturing. Volume 30:Issue 12(2017)
• Page Start: 1253
• Page End: 1271
• Publication Date: 2017-12-02
• Subjects: Internet of Things -- manufacturing system -- frequent trajectory -- data mining -- work-in-process; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2017.1307522
• Languages: English
• ISSNs: 0951-192X

Abstract

Abstract : The application of Internet of Things technologies has led to a data-rich manufacturing environment by connecting manufacturing objects as a collaborative community. However, advanced analytics approach is comparatively inadequate for work-in-process (WIP) trajectory data. On the other hand, although the topic of mining frequent trajectory patterns has raised a great deal of attention, it mainly focuses on the fields of vehicle traffic management and users' behaviours. When applied in manufacturing shop floor, the extracted knowledge is physical trajectory patterns and lacks manufacturing significance. This paper manages to obtain logical knowledge with manufacturing significance from WIP trajectory data. In this paper, a data model is introduced to map physical trajectories of WIP into logical space, in order to capture logical features of manufacturing system. Moreover, an algorithm named PMP is proposed to extract logical trajectory patterns. Several experiments are conducted to examine the performance. The results prove the efficiency and feasibility of the proposed method.