Extensible analysis tool for trajectory pattern mining

Vanya Deasy Safrina, Saiful Akbar
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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.
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用于轨迹模式挖掘的可扩展分析工具
随着技术的发展,运动目标数据采集的能力也在不断提高。通过卫星和GPS等技术,可以很容易地生成各种移动物体的移动性。有了这些设施,关于运动目标数据的研究在过去的几十年里不断增加,例如关于轨迹模式挖掘的研究。轨迹模式挖掘是运动对象数据挖掘中的一个领域,其重点是从运动对象数据生成的空间轨迹数据中寻找模式。目标系统是一个分析工具,可以运行与轨迹模式挖掘相关的各种算法来挖掘运动物体的轨迹。此外,还提供了用户界面,便于对采矿结果进行交互式勘探和分析。该工具开发的主要目的是生成一个可扩展的工具,以便与轨迹模式挖掘相关的新算法可以添加到该工具中。这种能力被认为是重要的,因为对相关主题的研究仍在迅速增长。通过分析各种轨迹模式挖掘算法的一般过程,得到了工具的可扩展性。然后利用模板方法模式将分析结果转换为设计,以确保可扩展性方面本身。在此基础上,成功构建了一个实现多种轨迹模式挖掘算法的分析工具。该工具是可扩展的,因此可以通过遵循几个规则和步骤来实现来自三个挖掘类别的新算法,即轨迹预处理,移动模式挖掘和轨迹聚类,同时最大限度地减少对现有系统功能的影响。
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