A Conceptual Data Model for Trajectory Data Mining

被引:0
|
作者
Bogorny, Vania [1 ]
Heuser, Carlos Alberto [2 ]
Alvares, Luis Otavio [2 ]
机构
[1] Univ Fed Santa Catarina, Dept Informat & Estatist, Campus Univ,CP 476, Florianopolis, SC, Brazil
[2] Univ Fed Rio Grande do Sul, Inst Informat, Porto Alegre, RS, Brazil
来源
关键词
conceptual model; data mining; trajectory data; trajectory patterns; KNOWLEDGE;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data mining has-become very popular in the last years, and it is well known that data Preprocessing is the most effort and time consuming step in the discovery process. In part, it is because database designers do not think about data mining during the conceptual design of a database, therefore data are not prepared for mining. This problem increases for spatio-temporal data generated by mobile devices, which involve both space and time. In this paper we propose a novel solution to reduce the gap between databases and data mining in the domain of trajectories of moving objects, aiming to reduce the effort for data preprocessing. We propose a general framework for modeling trajectory patterns during the conceptual design of a database. The proposed framework is a result of several works including different data mining case studies and experiments performed by the authors on trajectory data modeling and trajectory data mining It has been validated with a data mining query language implemented in PostGIS, that allows the user to create, instantiate and query trajectory data and trajectory patterns.
引用
收藏
页码:1 / +
页数:3
相关论文
共 50 条
  • [11] Model-driven data mining engineering: from solution-driven implementations to 'composable' conceptual data mining models
    Cuzzocrea, Alfredo
    Mazon, Jose-Norberto
    Trujillo, Juan
    Zubcoff, Jose
    INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT, 2011, 3 (03) : 217 - 251
  • [12] A Survey on Trajectory Data Mining: Techniques and Applications
    Feng, Zhenni
    Zhu, Yanmin
    IEEE ACCESS, 2016, 4 : 2056 - 2067
  • [13] Mining Moving Object, Trajectory and Traffic Data
    Han, Jiawei
    Li, Zhenhui
    Tang, Lu An
    DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PT II, PROCEEDINGS, 2010, 5982 : 485 - 486
  • [14] Trajectory data mining: A review of methods and applications
    Mazimpaka, Jean Damascene
    Timpf, Sabine
    JOURNAL OF SPATIAL INFORMATION SCIENCE, 2016, (13): : 61 - 99
  • [15] Trajectory Data Mining in Distributed Sensor Networks
    Qiao, Shaojie
    Jin, Huidong
    Gao, Yunjun
    Tang, Lu-An
    Xing, Huanlai
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
  • [16] Trajectory Mining Using Uncertain Sensor Data
    Muzammal, Muhammad
    Gohar, Moneeb
    Rahman, Arif Ur
    Qu, Qiang
    Ahmad, Awais
    Jeon, Gwanggil
    IEEE ACCESS, 2018, 6 : 4895 - 4903
  • [17] Mobility data mining: discovering movement patterns from trajectory data
    Giannotti, Fosca
    Nanni, Mirco
    Pedreschi, Dino
    Pinelli, Fabio
    Renso, Chiara
    Rinzivillo, Salvatore
    Trasarti, Roberto
    3RD ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON COMPUTATIONAL TRANSPORTATION SCIENCE 2010 (CTS'10), 2010, : 7 - 10
  • [18] Analysis of Trajectory Data in Support of Traffic Management: A Data Mining Approach
    Elragal, Ahmed
    Raslan, Hisham
    Advances in Data Mining: Applications and Theoretical Aspects, 2014, 8557 : 174 - 188
  • [19] Conceptual mining of large administrative health data
    Semenova, T
    Hegland, M
    Graco, W
    Williams, G
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2004, 3056 : 659 - 669
  • [20] ITERATE: A conceptual clustering algorithm for data mining
    Biswas, Gautam
    Weinberg, Jerry B.
    Fisher, Douglas H.
    IEEE Transactions on Systems, Man & Cybernetics Part C: Applications and Reviews, 1998, 28 (02): : 219 - 230