{"title":"Extensible analysis tool for trajectory pattern mining","authors":"Vanya Deasy Safrina, Saiful Akbar","doi":"10.1109/ICODSE.2017.8285859","DOIUrl":null,"url":null,"abstract":"The capabilities of moving object data collection have been increasing parallel with the development pace of technologies. The mobility of various moving objects can be easily generated via technologies, such as satellite and GPS. With such facilities, studies about moving object data have been increasing these past few decades, for instance, studies about trajectory pattern mining. Trajectory pattern mining is a field in moving object data mining that focuses on finding patterns from the spatial trajectory data generated from moving object data. The purposed system is an analysis tool that can run various algorithms related to trajectory pattern mining to mine trajectory of moving objects. In addition, the user interface is provided to facilitate interactive exploration and analysis of mining results. The main purpose of this tool development is to produce an extensible tool so that a new algorithm related to trajectory pattern mining can be added to the tool. This ability is considered important because the study on related topics is still growing rapidly. Extensibility of the tool is obtained by analyzing the general process from various trajectory pattern mining algorithms. The results of the analysis are then transformed into designs by utilizing the template method pattern to ensure the extensibility aspect itself. From this study, an analysis tool that implements various algorithms trajectory pattern mining is successfully built. The tool is extensible so that new algorithms from three mining categories, i.e. trajectory preprocessing, moving together pattern mining, and trajectory clustering, can be implemented into the tool by following several rules and steps while minimizing impact on existing system functions.","PeriodicalId":366005,"journal":{"name":"2017 International Conference on Data and Software Engineering (ICoDSE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Data and Software Engineering (ICoDSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICODSE.2017.8285859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The capabilities of moving object data collection have been increasing parallel with the development pace of technologies. The mobility of various moving objects can be easily generated via technologies, such as satellite and GPS. With such facilities, studies about moving object data have been increasing these past few decades, for instance, studies about trajectory pattern mining. Trajectory pattern mining is a field in moving object data mining that focuses on finding patterns from the spatial trajectory data generated from moving object data. The purposed system is an analysis tool that can run various algorithms related to trajectory pattern mining to mine trajectory of moving objects. In addition, the user interface is provided to facilitate interactive exploration and analysis of mining results. The main purpose of this tool development is to produce an extensible tool so that a new algorithm related to trajectory pattern mining can be added to the tool. This ability is considered important because the study on related topics is still growing rapidly. Extensibility of the tool is obtained by analyzing the general process from various trajectory pattern mining algorithms. The results of the analysis are then transformed into designs by utilizing the template method pattern to ensure the extensibility aspect itself. From this study, an analysis tool that implements various algorithms trajectory pattern mining is successfully built. The tool is extensible so that new algorithms from three mining categories, i.e. trajectory preprocessing, moving together pattern mining, and trajectory clustering, can be implemented into the tool by following several rules and steps while minimizing impact on existing system functions.