Cryptographic Spatio-temporal Predicates for Location-Based Services

2013 ◽  
Author(s):  
Yan Zhu ◽  
Changjun Hu ◽  
Di Ma ◽  
Jin Li
Keyword(s):  
Location Based Services ◽  
Spatio Temporal

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.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Building a Visual Analytics Tool for Location-Based Services

2016 ◽  
pp. 620-642 ◽  
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Information Management in Mobile Environments Using a Location-Aware Intelligent Agent System

2011 ◽  
pp. 1-17
Author(s):  
Amrish Vyas ◽  
Victoria Yoon
Keyword(s):  
Intelligent Agent ◽  
Location Based Services ◽  
Agent System ◽  
Content Dissemination ◽  
New Era ◽  
Location Aware ◽  
Spatio Temporal ◽  
Types Of Information ◽  
Push And Pull ◽  
User Location

Recent rise in the level of comfort and demand to access various types of information using mobile devices can be attributed to the advancements in wireless as well as Internet technologies. This demand leads us to the new era of mobile computing. Location-based services (LBS) are engendering new passion in mobile services utilizing users’ location information. Such spatio-temporal information processing entails the need for a dynamic middleware that accurately identifies changing user location and attaches dependent content in real-time without putting extra burden on users. Our work focuses on creating a distributed infrastructure suitable to support such scalable content dissemination. As a result this chapter offers a conceptual framework, location-aware intelligent agent system (LIA) in integration with publish/subscribe middleware to comprehensively address dynamic content dissemination and related issues. We discuss the operational form of our framework in terms of PUSH and PULL strategies.

scholarly journals Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI82-SI94
Author(s):  
Anh Tuan Truong
Keyword(s):  
Data Mining ◽  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Location Data ◽  
Related Information ◽  
Spatio Temporal ◽  
Location Privacy Protection

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

scholarly journals Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Bennati ◽  
Aleksandra Kovacevic
Keyword(s):  
Service Providers ◽  
Flow Analysis ◽  
Location Based Services ◽  
Location Based Service ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Imperfect Knowledge ◽  
Spatio Temporal ◽  
Definition Of ◽  
Privacy Risks

AbstractMobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target’s home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

HERMES

2016 ◽  
pp. 717-742
Author(s):  
Nikos Pelekis ◽  
Elias Frentzos ◽  
Nikos Giatrakos ◽  
Yannis Theodoridis
Keyword(s):  
Query Language ◽  
Expressive Power ◽  
Location Based Services ◽  
Temporal Semantics ◽  
Mobility Data ◽  
Object Relational ◽  
Data Definition ◽  
Spatio Temporal ◽  
Manipulation Language ◽  
Processing Techniques

This chapter presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this chapter HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

Building a Visual Analytics Tool for Location-Based Services

Big Data ◽  
2016 ◽  
pp. 615-637
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

HERMES

2015 ◽  
Vol 5 (2) ◽  
pp. 19-41 ◽  
Author(s):  
Nikos Pelekis ◽  
Elias Frentzos ◽  
Nikos Giatrakos ◽  
Yannis Theodoridis
Keyword(s):  
Query Language ◽  
Expressive Power ◽  
Location Based Services ◽  
Temporal Semantics ◽  
Mobility Data ◽  
Object Relational ◽  
Data Definition ◽  
Spatio Temporal ◽  
Manipulation Language ◽  
Processing Techniques

This paper presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this paper HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

Building a Visual Analytics Tool for Location-Based Services

2015 ◽  
pp. 150-180
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

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