scholarly journals A Survey on Big Data for Trajectory Analytics

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.

Moving Beyond Traditional Decision Support Systems

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.

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

Algorithms ◽  
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.

scholarly journals Profiling for Confidence: Debugging Relationships among Urban Spatio-Temporal Datasets

2020 ◽  
Author(s):  
Laís M. A. Rocha ◽  
Mirella M. Moro ◽  
Juliana Freire
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Temporal Data ◽  
Temporal Relationships ◽  
Quality Issues ◽  
Spatio Temporal

We aim to help users identify potential issues in spatio-temporal data and thus gain trust in the results they derive from such data -- a crucial benefit in the era of data science and big data. We propose a framework for profiling spatio-temporal relationships that automatically identifies data slices that deviate from what is expected, which can be further analyzed for quality issues and/or potential effects on analysis' results. We describe the profiling methodology and present cases studies using real urban datasets, then emphasizing the need for spatio-temporal profiling to build trust on data analysis' results.  

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

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.

A basis of spatial big data analysis with map-matching system

2017 ◽  
Vol 20 (3) ◽  
pp. 2177-2192 ◽  
Author(s):  
Wonhee Cho ◽  
Eunmi Choi
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Map Matching ◽  
Spatial Big Data

Data Analysis through Visualization

2021 ◽  
pp. 107-132
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
University Of California ◽  
Science Class ◽  
Temporal Data ◽  
Santa Cruz ◽  
State Action ◽  
Spatio Temporal ◽  
Visualization Systems ◽  
Visualization Techniques

This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

2019 ◽  
Vol 13 (01) ◽  
pp. 111-133
Author(s):  
Romita Banerjee ◽  
Karima Elgarroussi ◽  
Sujing Wang ◽  
Akhil Talari ◽  
Yongli Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Negative Emotion ◽  
Time Interval ◽  
Temporal Data ◽  
Analysis Framework ◽  
Spatial Clusters ◽  
Change Analysis ◽  
Spatio Temporal ◽  
Temporal Data Analysis ◽  
Very High

Twitter is one of the most popular social media platforms used by millions of users daily to post their opinions and emotions. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework, K2, for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework that can be used to understand the emotional evolution of a specific section of population. The input for our framework is the location and time of where and when the tweets were posted and an emotion assessment score in the range [Formula: see text], with [Formula: see text] representing a very high positive emotion and [Formula: see text] representing a very high negative emotion. Our framework first segments the input dataset into a number of batches with each batch representing a specific time interval. This time interval can be a week, a month or a day. By generalizing existing kernel density estimation techniques in the next step, we transform each batch into a continuous function that takes positive and negative values. We have used contouring algorithms to find the contiguous regions with highly positive and highly negative emotions belonging to each member of the batch. Finally, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particular, using this framework, unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurs. We also propose animation techniques to facilitate spatio-temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in the month of June 2014.

scholarly journals A Microservice-Based Big Data Analysis Platform for Online Educational Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Modular Design ◽  
Science Research ◽  
Big Data Analysis ◽  
Research Field ◽  
Traditional Work ◽  
Educational Applications ◽  
Analysis Platform

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.

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