Scalable Spatial Query Processing for Location-Aware Mobile Services

Park, Kwangjin; Song, Mei; Kong, Ki-Sik; Hwang, Chong-Sun; Chung, Kwang-Sik; Jung, Soonyoung

doi:10.1007/11596356_71

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…2013 -Parallel spatial mashup [240] Quadtree construction using GPGPU [242] Activity trajectories [253] Spatial keyword based queries [28] Moving objects indices [178] [5] -2014 -Large scale parallel query processing [122] Skew resistant spatial join [170] Data partitioning framework [214] Data access methods for spatial query processing [152] -Trajectories based indexing [90] 2015 -Bitmap indexing [184] Polygon overlay processing [168] Spatial Hadoop [44] Keyword query processing [27,56] [ 220,254] Reverse nearest neighbor queries [22] Predictive index [70] Scalable spatial search [153,238] 2016 Concurrent Quadtree [258] Data management on distributed and parallel platforms [236] In memory data management sing locationSpark [202] Spatial query based virtual reality analysis platforms [219] Keyword aware kNN query [252] In-memory kNN query [17] -2017 Concurrent spatial operations [36] Big spatial data processing framework [6,65] selectivity estimation [21] Parallel map projection [203] Spatial index for cloud [86] Cumulative sum algorithm [127] kNN search with road network constraints [182] Neural network based algorithm for spatial predictions [223] Indexing trajectories [232] Constellation query processing [91] Speculative real time concurrency algorithm…”

Section: Concurrency Controlmentioning

confidence: 99%

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Indexing of real time geospatial data by IoT enabled devices: Opportunities, challenges and design considerations

Chaudhry

Yousaf

Khan

2020

AIS

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We are moving towards 'smart' world in which industries, such as healthcare, smart cities, transportation, and agriculture have started using IoT (Internet of Things). These applications involve huge number of sensors and devices that generate high volume of real time data. To perform useful analytics on this data, location and spatial awareness characteristics of devices need to be considered. Wide range of location-based services and sensors in GIS have to manage moving objects that change their position with respect to time. These applications generate voluminous amount of real time geospatial data that demands an effective query processing mechanism to minimize the response time of a query. Indexing is one of the traditional ways to minimize the response time of a query by pruning the search space. In this paper, we performed a detailed survey of the literature regarding the indexing of real time geospatial data generated by IoT enabled devices. Some major challenges relevant to indexing of moving objects are highlighted. Various important index design considerations are also discussed. The goal is to help researchers in understanding the principles, methods, and challenges in the indexing of real time geospatial data. This will also aid in identifying the future research opportunities.

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Section: Concurrency Controlmentioning

confidence: 99%

“…Distributed indices involving client/server model have several limitations such as compromise on privacy, server dependency, and insufficient support for moving objects. In [153], an indexing algorithm is proposed for moving objects in a broadcast environment. It processes queries efficiently by using grid cells.…”

Section: Pure Moving Objects Indexingmentioning

confidence: 99%

Indexing of real time geospatial data by IoT enabled devices: Opportunities, challenges and design considerations

Chaudhry

Yousaf

Khan

2020

AIS

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“…LBS include services to identify the location of a person or object, such as the nearest point of interest (POI) or the whereabouts of a friend or employee. Typical LBS applications include road navigation and vehicle tracking services [1][2][3][4]. As LBS have become more numerous and diverse, user privacy violations have become more commonplace.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Location-Based System for Continuous Spatial Queries

Song

Park

2016

Mobile Information Systems

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K-anonymization generated a cloaked region (CR) that was K-anonymous; that is, the query issuer was indistinguishable from K-1 other users (nearest neighbors) within the CR. This reduced the probability of the query issuer’s location being exposed to untrusted parties (1/K). However, location cloaking is vulnerable to query tracking attacks, wherein the adversary can infer the query issuer by comparing the two regions in continuous LBS queries. This paper proposes a novel location cloaking method to resist this attack. The target systems of the proposed method are road networks where the mobile clients’ trajectories are fixed (the road network is preknown and fixed, instead of the trajectories), such as subways, railways, and highways. The proposed method, called adaptive-fixed K-anonymization (A-KF), takes this issue into account and generates smaller CRs without compromising the privacy of the query issuer’s location. Our results show that the proposed A-KF method outperforms previous location cloaking methods.

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“…In such systems, the antenna-equipped server cyclically broadcasts a set of selected spatial objects, thus forming a broadcast cycle on a wireless channel to generate a broadcast stream, and mobile clients tune in to the channel to retrieve the desired data. To maximize battery life, mobile devices need to stay in doze mode whenever possible and switch into active mode only when necessary [10][11][12][13]. Interleaving spatial indexes with spatial objects could help mobile devices predict the arrival times of spatial objects and their related indexes.…”

Section: Introductionmentioning

confidence: 99%

Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing

Shen

Chen

Tang

2016

IJGI

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Abstract:In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings.

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Scalable Spatial Query Processing for Location-Aware Mobile Services

Cited by 3 publications

References 9 publications

Indexing of real time geospatial data by IoT enabled devices: Opportunities, challenges and design considerations

Indexing of real time geospatial data by IoT enabled devices: Opportunities, challenges and design considerations

A Privacy-Preserving Location-Based System for Continuous Spatial Queries

Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing

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