A State-of-the-Art in Spatio-Temporal Data Warehousing, OLAP and Mining

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community, although not much work has been done to provide moving object databases with OLAP capabilities. In the first part of this paper we survey the SOLAP literature. We then address the problem of trajectory analysis, and review recent efforts regarding trajectory data warehousing and mining. We also provide an in-depth comparative study between two proposals: the GeoPKDD project (that makes use of the Hermes system), and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina. Finally, we discuss future directions in the field, including SOLAP analysis over raster data.

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A Survey of Spatio-Temporal Data Warehousing

Business Information Systems ◽

10.4018/978-1-61520-969-9.ch057 ◽

2010 ◽

pp. 949-977

Author(s):

Leticia Gómez ◽

Bart Kuijpers ◽

Bart Moelans ◽

Alejandro Vaisman

Keyword(s):

Moving Objects ◽

Route Planning ◽

Geographic Information ◽

Moving Object ◽

Attribute Data ◽

Object Databases ◽

Spatio Temporal ◽

Eu Project ◽

The University ◽

Moving Object Databases

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.

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Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

Sustainability ◽

10.3390/su13020690 ◽

2021 ◽

Vol 13 (2) ◽

pp. 690

Author(s):

Tao Wu ◽

Huiqing Shen ◽

Jianxin Qin ◽

Longgang Xiang

Keyword(s):

Moving Objects ◽

Major Effect ◽

Location Based Services ◽

Trajectory Data ◽

The Road ◽

Contextual Features ◽

Novel Approach ◽

Gps Trajectories ◽

Time Distance ◽

Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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Spatio-Temporal Queries for Moving Objects Data Warehousing

International Journal of Database Management Systems ◽

10.5121/ijdms.2013.5301 ◽

2013 ◽

Vol 5 (3) ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Leila Esheiba ◽

Hoda M.O.Mokhtar ◽

Mohamed El-Sharkawi

Keyword(s):

Moving Objects ◽

Data Warehousing ◽

Spatio Temporal

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Mining Partners in Trajectories

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2020010102 ◽

2020 ◽

Vol 16 (1) ◽

pp. 22-38

Author(s):

Diego Vilela Monteiro ◽

Rafael Duarte Coelho dos Santos ◽

Karine Reis Ferreira

Keyword(s):

Moving Objects ◽

R Package ◽

Moving Object ◽

Spatiotemporal Data ◽

Data Preparation ◽

Trajectory Data ◽

Gps Satellites ◽

Object Trajectories ◽

Moving Object Trajectory ◽

Moving Object Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

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Moving Beyond Traditional Decision Support Systems

International Journal of Information Systems and Social Change ◽

10.4018/ijissc.2017040104 ◽

2017 ◽

Vol 8 (2) ◽

pp. 74-87

Author(s):

Noura Azaiez ◽

Jalel Akaichi ◽

Jeffrey Hsu

Keyword(s):

Decision Making ◽

Decision Support ◽

Moving Objects ◽

Temporal Data ◽

Trajectory Data ◽

Reduced Costs ◽

Spatio Temporal ◽

Traditional Approaches ◽

Dairy Milk ◽

Made In

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

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APDS: A framework for discovering movement pattern from trajectory database

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719888164 ◽

2019 ◽

Vol 15 (11) ◽

pp. 155014771988816

Author(s):

Guan Yuan ◽

Zhongqiu Wang ◽

Zhixiao Wang ◽

Fukai Zhang ◽

Li Yuan ◽

...

Keyword(s):

Clustering Algorithm ◽

Moving Objects ◽

Pattern Mining ◽

Pattern Discovery ◽

Movement Pattern ◽

Moving Object ◽

Periodic Pattern ◽

Trajectory Data ◽

Discovery System ◽

Spatiotemporal Data Mining

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

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Identifying Human Mobility via Trajectory Embeddings

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/234 ◽

2017 ◽

Cited By ~ 39

Author(s):

Qiang Gao ◽

Fan Zhou ◽

Kunpeng Zhang ◽

Goce Trajcevski ◽

Xucheng Luo ◽

...

Keyword(s):

Human Mobility ◽

Route Planning ◽

Classification Problem ◽

Personalized Recommendation ◽

Mobility Patterns ◽

Classification Problems ◽

Trajectory Data ◽

Trajectory Classification ◽

Spatio Temporal ◽

Real World Datasets

Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.

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Continuously Moving Objects Framework to Monitor Result Changes of Spatio Temporal Queries using Data Stream Management System

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v8i2.3387 ◽

2013 ◽

Vol 8 (2) ◽

pp. 796-801

Author(s):

Brijesh Panday ◽

Vinod Kumar Yadav ◽

Shreya Paul

Keyword(s):

Management System ◽

Data Stream ◽

Moving Objects ◽

Moving Object ◽

Data Stream Management ◽

Stream Management ◽

Aggregate Function ◽

Safe Limit ◽

Data Stream Management System ◽

Spatio Temporal

In this paper the concept of safe limit in the framework for continuously moving objects to monitor result changes of spatio temporal queries has been proposed. In this framework the movement of the moving object is monitored with the help of user defined aggregate function over spatio temporal reference in data stream management system. The expected movement of the moving object is plotted on the graph and the spatio temporal queries are answered on the basis of that, until and unless the difference between the expected movement and actual movement is more than the safe limit. This makes the framework more efficient than the previously given framework. The safe limit can be any range with respect to space and time of a moving object which varies according to different parameters such as size of the object, velocity with which it is moving and etc.

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Mobility Data Warehouses

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040170 ◽

2019 ◽

Vol 8 (4) ◽

pp. 170 ◽

Cited By ~ 3

Author(s):

Alejandro Vaisman ◽

Esteban Zimányi

Keyword(s):

Spatial Data ◽

Moving Objects ◽

Moving Object ◽

Data Warehouses ◽

Mobility Analysis ◽

Trajectory Data ◽

Data Types ◽

Seamless Integration ◽

Moving Object Database ◽

Mobility Data

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.

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