Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics

With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted...

Full description

Saved in:
Bibliographic Details
Main Authors: Rabia Hasan, Waseem Shehzad, Ejaz Ahmed, Hasan Ali Khattak, Ahmed S. AlGhamdi, Sultan S. Alshamrani
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Applied Bionics and Biomechanics
Online Access:http://dx.doi.org/10.1155/2021/9958647
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1850120538426441728
author Rabia Hasan
Waseem Shehzad
Ejaz Ahmed
Hasan Ali Khattak
Ahmed S. AlGhamdi
Sultan S. Alshamrani
author_facet Rabia Hasan
Waseem Shehzad
Ejaz Ahmed
Hasan Ali Khattak
Ahmed S. AlGhamdi
Sultan S. Alshamrani
author_sort Rabia Hasan
collection DOAJ
description With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache.
format Article
id doaj-art-602371fa17ec46d489845d3044090a6d
institution OA Journals
issn 1754-2103
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Applied Bionics and Biomechanics
spelling doaj-art-602371fa17ec46d489845d3044090a6d2025-08-20T02:35:21ZengWileyApplied Bionics and Biomechanics1754-21032021-01-01202110.1155/2021/9958647Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City AnalyticsRabia Hasan0Waseem Shehzad1Ejaz Ahmed2Hasan Ali Khattak3Ahmed S. AlGhamdi4Sultan S. Alshamrani5Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceSchool of Electrical Engineering and Computer Science (SEECS)Department of Computer EngineeringDepartment of Information TechnologyWith the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache.http://dx.doi.org/10.1155/2021/9958647
spellingShingle Rabia Hasan
Waseem Shehzad
Ejaz Ahmed
Hasan Ali Khattak
Ahmed S. AlGhamdi
Sultan S. Alshamrani
Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics
Applied Bionics and Biomechanics
title Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics
title_full Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics
title_fullStr Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics
title_full_unstemmed Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics
title_short Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics
title_sort location dependent query processing semantic cache for real time smart city analytics
url http://dx.doi.org/10.1155/2021/9958647
work_keys_str_mv AT rabiahasan locationdependentqueryprocessingsemanticcacheforrealtimesmartcityanalytics
AT waseemshehzad locationdependentqueryprocessingsemanticcacheforrealtimesmartcityanalytics
AT ejazahmed locationdependentqueryprocessingsemanticcacheforrealtimesmartcityanalytics
AT hasanalikhattak locationdependentqueryprocessingsemanticcacheforrealtimesmartcityanalytics
AT ahmedsalghamdi locationdependentqueryprocessingsemanticcacheforrealtimesmartcityanalytics
AT sultansalshamrani locationdependentqueryprocessingsemanticcacheforrealtimesmartcityanalytics