Continuous Spatial Query Processing:A Survey of Safe Region Based Techniques

In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region . A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements

Location- and keyword-based querying of geo-textual data: a survey

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityThis research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC

Spatial Queries for Indoor Location-based Services

Indoor Location-based Services (LBS) facilitate people in indoor scenarios such as airports, train stations, shopping malls, and office buildings. Indoor spatial queries are the foundation to support indoor LBSs. However, the existing techniques for indoor spatial queries are limited to support more advanced queries that consider semantic information, temporal variations, and crowd influence. This work studies indoor spatial queries for indoor LBSs. Some typical proposals for indoor spatial queries are compared theoretically and experimentally. Then, it studies three advanced indoor spatial queries, a) Indoor Keyword-aware Routing Query. b) Indoor Temporal-variation aware Routing Query. c) Indoor Crowd-aware Routing Query. A series of techniques are proposed to solve these problems.</p

Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D

Fog computing-based approximate spatial keyword queries with numeric attributes in IoV

Due to the popularity of on-board geographic devices, a large number of spatial-textual objects are generated in Internet of Vehicles (IoV). This development calls for Approximate Spatial Keyword Queries with numeric Attributes in IoV (ASKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering huge amounts of objects involved in the query processing, this paper comes up with the ideal of utilizing vehicles as fog-computing resource, and proposes the network structure called FCV, and based on which the fog-based Top-k ASKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning simultaneously, a two-level spatial-textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient Top-k ASKIV query processing algorithm is presented. Simulation results show that, our STAG-based approach is about 1.87x (17.1x, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.University of Derb

MOVING OBJECTS MANAGEMENT FOR LOCATION-BASED SERVICES

Ph.DDOCTOR OF PHILOSOPH

Towards Crowd-aware Indoor Path Planning (Extended Version)

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.Comment: The extension of a VLDB'21 paper "Towards Crowd-aware Indoor Path Planning