An Overview of Moving Object Trajectory Compression Algorithms

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.

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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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A Clustering-Based Approach for Personalized Privacy Preserving Publication of Moving Object Trajectory Data

Network and System Security - Lecture Notes in Computer Science ◽

10.1007/978-3-642-34601-9_12 ◽

2012 ◽

pp. 149-165 ◽

Cited By ~ 2

Author(s):

Samaneh Mahdavifar ◽

Mahdi Abadi ◽

Mohsen Kahani ◽

Hassan Mahdikhani

Keyword(s):

Privacy Preserving ◽

Moving Object ◽

Trajectory Data ◽

Moving Object Trajectory

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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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Extracting Interesting Regions and Trips from Taxi Trajectory Data

Modern Applied Science ◽

10.5539/mas.v13n2p258 ◽

2019 ◽

Vol 13 (2) ◽

pp. 258

Author(s):

Ammar M. Huneiti ◽

Omar Y. Adwan

Keyword(s):

Moving Objects ◽

Self Organizing Map ◽

Trajectory Data ◽

Useful Knowledge ◽

Processing Step ◽

Edge Location ◽

Gps Devices ◽

Research Initiatives ◽

Spatial Trajectories ◽

The City

The increasing availability of cutting-edge location-acquisition technologies such as GPS devices, has led to the generation of huge datasets of spatial trajectories. These trajectories store important information regarding the movement of people, vehicles, robots, animals, users of social networks, etc. Many research initiatives have applied data mining techniques in order to extract useful knowledge from this data.  An important, and yet complicated, pre-processing step in mining patterns from trajectory data, is the identification of the Regions of Interest (RoI) that have been collectively navigated by a set of trajectories. The RoI’s are being manually and subjectively pre-defined by a group of experts as popular regions, regardless of the actual behaviour of the moving objects. This research emphasizes the usefulness of applying an unsupervised machine learning technique, namely Self Organizing Map (SOM), in order to identify the RoI’s associated with a trajectory dataset depending on the moving objects’ behaviour. The research experiments were conducted using 180 thousand of the trajectories generated by 442 taxis running in the city of Porto, in Portugal, and they demonstrate the ability of SOM in identifying the RoI’s and interesting taxi trips within the city.

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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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QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

Algorithms ◽

10.3390/a14090265 ◽

2021 ◽

Vol 14 (9) ◽

pp. 265

Author(s):

Irya Wisnubhadra ◽

Safiza Kamal Baharin ◽

Nurul A. Emran ◽

Djoko Budiyanto Setyohadi

Keyword(s):

Semantic Web ◽

Spatial Data ◽

Moving Objects ◽

Open Data ◽

Moving Object ◽

Online Analytical Processing ◽

Multidimensional Data ◽

Trajectory Data ◽

Advanced Analysis ◽

Analytical Processing

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

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

Data Mining ◽

10.4018/978-1-4666-2455-9.ch104 ◽

2013 ◽

pp. 2021-2056

Author(s):

Leticia Gómez ◽

Bart Kuijpers ◽

Bart Moelans ◽

Alejandro Vaisman

Keyword(s):

Moving Objects ◽

Data Warehousing ◽

Route Planning ◽

Geographic Information ◽

Moving Object ◽

Trajectory Data ◽

Attribute Data ◽

Object Databases ◽

Spatio Temporal ◽

The University

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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Design of intelligent acquisition system for moving object trajectory data under cloud computing

Journal of Intelligent Systems ◽

10.1515/jisys-2020-0152 ◽

2021 ◽

Vol 30 (1) ◽

pp. 763-773

Author(s):

Yang Zhang ◽

Abhinav Asthana ◽

Sudeep Asthana ◽

Shaweta Khanna ◽

Ioan-Cosmin Mihai

Keyword(s):

Cloud Computing ◽

Clustering Algorithm ◽

Programming Model ◽

Hot Spot ◽

Moving Object ◽

Trajectory Clustering ◽

Trajectory Data ◽

Spot Area ◽

Time Period ◽

Moving Object Trajectory

Abstract In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based DBSCAN algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.

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A new approach for predicting the future position of a moving object: Hurricanes case study

10.21203/rs.3.rs-193248/v1 ◽

2021 ◽

Author(s):

wided oueslati ◽

sonia tahri ◽

hela limam ◽

jalel akaichi

Keyword(s):

Moving Objects ◽

Moving Object ◽

Second Phase ◽

Mobility Patterns ◽

Trajectory Data ◽

New Approach ◽

Natural Catastrophes ◽

The Future ◽

Future Position

Abstract Nowadays, huge amounts of tracking data related to moving objects are being generated and collected in suitable repositories thanks to GPS devices, RFIDsensors, satellites and wireless communication technologies. Tracked moving objects could be pedestrians, cars, vessels, planes, animals, natural disasters. Those letters generate trajectory data that contain a great deal of knowledge. For this reason, these trajectory data sets need an urgent and an effective analysis process and constitute a rich source for inferring mobility patterns. Predicting the future position of a given moving object is one of the important tasks we can find in the knowledge discovery process. In fact, being able to predict a moving object’s future position related to natural phenomena, would allow decision makers to take strategic decisions in order to help the humanity, and prevent or avoid the propagation of natural catastrophes. The aim of this paper is to propose a new approach to predict the future position of a moving object, especially a moving region based on mobility patterns. To achieve this aim, we experiment our approach on a real case study related to hurricanes as moving regions. The proposed approach is composed of three phases. The first phase allows generating object mobility patterns. In the second phase, spatio-tempoal mobility rules are extracted from the generated patterns. In the third and last phase, hurricane future position prediction is accomplished by using the extracted rules.

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