LIBRARY FOR SUPPORTING MOVING POINT ANALYSIS
<p align="justify">Moving point objects are spatio-temporal data which have spatio (space) and temporal (time) attribute. As technological advancements in internet of things and GPS, quantity of moving point data also significantly increase. Those data contain numerous amount of know...
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Main Author: | |
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Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/28726 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | <p align="justify">Moving point objects are spatio-temporal data which have spatio (space) and temporal (time) attribute. As technological advancements in internet of things and GPS, quantity of moving point data also significantly increase. Those data contain numerous amount of knowledge useful for domain expert. Furthermore, there is no integrated library that can do data gathering, operation, and mining of moving point data. Therefore, this final project has objective to create analytical libray for moving point data. <br />
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First, to develop this library, various initial analysis need to be done i.e. data representation, functions in the library, extensibiliy of library, and usabiliy of the library. In data representation analysis, the model created must be able to represent spatio, temporal, and descritive attribute of most moving point data. In functional analysis, several functions were required to do trajectory preprocessing, clustering, and outlier detection. Last, in library analysis, library structure were created which have extensible and easy-to-use functionality. <br />
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After analysis phase, implementation phase is done using Python programming language with design from analysis phase. Last, there are four testing done to prove the functional and nonfunctional requirement of the library. The first one is functional testing using black box method. The second test for the extensibilty functionality of the library. The third is the correctness testing of the implemented algorithm. Lastly, case study testing is used to test the usage of library in real life problem. In conclusion, this library for supporting moving point data have been implemented and can done various kind of trajectory mining like clustering and outlier detection. This library also have extensibilty function so new algorithm can be implemented easily.<p align="justify"> |
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