A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

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A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

Sensors ◽

10.3390/s140712990 ◽

2014 ◽

Vol 14 (7) ◽

pp. 12990-13005 ◽

Cited By ~ 22

Author(s):

Shengnan Ke ◽

Jun Gong ◽

Songnian Li ◽

Qing Zhu ◽

Xintao Liu ◽

...

Keyword(s):

Temporal Data ◽

Data Indexing ◽

Indexing Method ◽

Spatio Temporal

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Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

Sustainability ◽

10.3390/su12229662 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9662 ◽

Cited By ~ 1

Author(s):

Disheng Yi ◽

Yusi Liu ◽

Jiahui Qin ◽

Jing Zhang

Keyword(s):

Traffic Management ◽

Spatial Clustering ◽

Spatial Interaction ◽

The Other ◽

Temporal Data ◽

Trajectory Data ◽

Data Field ◽

Spatio Temporal ◽

The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

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HD-Tree: An Efficient High-Dimensional Virtual Index Structure Using a Half Decomposition Strategy

Algorithms ◽

10.3390/a13120338 ◽

2020 ◽

Vol 13 (12) ◽

pp. 338

Author(s):

Ting Huang ◽

Zhengping Weng ◽

Gang Liu ◽

Zhenwen He

Keyword(s):

Hash Table ◽

Index Structure ◽

High Dimensional ◽

Sequential Search ◽

Storage Space ◽

Hash Tables ◽

Time Performance ◽

Indexing Method ◽

Point Data ◽

Better Than

To manage multidimensional point data more efficiently, this paper presents an improvement, called HD-tree, of a previous indexing method, called D-tree. Both structures combine quadtree-like partitioning (using integer shift operations without storing internal nodes, but only leaves) and hash tables (for searching for the nodes stored). However, the HD-tree follows a brand-new decomposition strategy, which is called half decomposition strategy. This improvement avoids the generation of nodes containing only a small amount of data and the sequential search of the hash table, so that it can save storage space while having faster I/O and better time performance when building the tree and querying data. The results demonstrate convincingly that the time and space performance of HD-tree is better than that of D-tree regardless of uniform or uneven data, which are less affected by data distribution.

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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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Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

Algorithms ◽

10.3390/a13080182 ◽

2020 ◽

Vol 13 (8) ◽

pp. 182

Author(s):

Elias Dritsas ◽

Andreas Kanavos ◽

Maria Trigka ◽

Gerasimos Vonitsanos ◽

Spyros Sioutas ◽

...

Keyword(s):

Big Data ◽

Spatial Data ◽

Privacy Preservation ◽

Nearest Neighbor ◽

Data Representation ◽

Privacy Preserving ◽

Temporal Data ◽

K Nearest Neighbor ◽

Trajectory Data ◽

Spatio Temporal

Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.

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Enhancing the quality of geometries of interest (GOIs) extracted from GPS trajectory data using spatio-temporal data aggregation and outlier detection

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-016-0426-8 ◽

2016 ◽

Vol 9 (1) ◽

pp. 173-186 ◽

Cited By ~ 4

Author(s):

Seyed Morteza Mousavi ◽

Aaron Harwood ◽

Shanika Karunasekera ◽

Mojtaba Maghrebi

Keyword(s):

Outlier Detection ◽

Data Aggregation ◽

Temporal Data ◽

Trajectory Data ◽

Gps Trajectory Data ◽

Gps Trajectory ◽

Spatio Temporal

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MIDET: A Method for Indexing Traffic Events

10.5753/sbbd.2021.17879 ◽

2021 ◽

Author(s):

Mariana M. G. Duarte ◽

Marcos V. Pontarolo ◽

Rebeca Schroeder ◽

Carmem S. Hara

Keyword(s):

Traffic Control ◽

Web Applications ◽

Grid Cell ◽

Real Data ◽

Index Structure ◽

Temporal Data ◽

Bitmap Index ◽

Traffic Lights ◽

Spatio Temporal ◽

Control Traffic

Traffic events announcements such as jams and road closures are continuously reported by mobile and Web applications. This collection of spatio-temporal data is an important source of information for urban planning, and can be used to orchestrate a number of actions to mprove the mobility, such as traffic control, traffic lights synchronization and preventive maintenance. Such analysis usually involves computation of spatial relationships among data, and may involve location of landmarks, roads and different types of events. In this paper, we propose a Method for Indexing Traffic Events (MIDET) for querying spatio-temporal data, whose location can be represented as a point or collection of points. MIDET is based on a fixed-grid space-oriented partitioning. In order to tackle the data skew, each grid cell is associated with a set of blocks containing event records. Moreover, a bitmap index is used for filtering out blocks without retrieving the actual data. MIDET provides the following benefits: adoption of a simple bulk loading process to manage dynamic insertion streams, and in-memory spatial joins. We conducted an experimental study using real data obtained from Waze. MIDET’s query performance was compared with Postgis, which adopts an R-tree index structure.

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DTNH Indexing Method: Past Present and Future Data Prediction for Spatio-Temporal Data

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2017.0630.48 ◽

2017 ◽

Vol 10 (3) ◽

pp. 426-434

Author(s):

John Ayeelyan ◽

◽

Sugumarn Muthukumarasamy ◽

Rengan Rajesh ◽

◽

...

Keyword(s):

Temporal Data ◽

Data Prediction ◽

Indexing Method ◽

Future Data ◽

Spatio Temporal

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A Survey on Big Data for Trajectory Analytics

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020088 ◽

2020 ◽

Vol 9 (2) ◽

pp. 88

Author(s):

Damião Ribeiro de Almeida ◽

Cláudio de Souza Baptista ◽

Fabio Gomes de Andrade ◽

Amilcar Soares

Keyword(s):

Big Data ◽

Data Analysis ◽

Moving Objects ◽

Research Field ◽

Temporal Data ◽

Trajectory Data ◽

Management Technology ◽

Spatial Big Data ◽

Analytical Processing ◽

Spatio Temporal

Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

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