Context-Awareness in Location Based Services in the Big Data Era

2017 ◽  
pp. 85-127 ◽  
Author(s):  
Patrizia Grifoni ◽  
Arianna D’Ulizia ◽  
Fernando Ferri
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Location Based Services

scholarly journals Strategies to Implement Edge Computing in a P2P Pervasive Grid

2018 ◽  
Vol 11 (1) ◽  
pp. 1-15
Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Edge Computing ◽  
Computing Platform ◽  
Geophysical Application ◽  
Two Factors ◽  
Computing Platforms ◽  
Distributed Computing Platform ◽  
Big Data Computing ◽  
Analyze Data

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.

scholarly journals Context Awareness Technology Using Parallel Mining for Ambient Assisted Living System

2018 ◽  
Vol 7 (2.19) ◽  
pp. 52
Author(s):  
J Vivek ◽  
Gandla Maharnisha ◽  
Gandla Roopesh Kumar ◽  
Ch Karun Sagar ◽  
R Arunraj
Keyword(s):  
Health Care ◽  
Big Data ◽  
Knowledge Discovery ◽  
Assisted Living ◽  
Context Awareness ◽  
Health Care Services ◽  
Ambient Assisted Living ◽  
Living System ◽  
Health Condition ◽  
Parallel Mining

In  this  paper,  context  awareness  is  a  promising  technology  that  provides  health care services and a niche  area of big data paradigm. The   drift  in  Knowledge  Discovery  from  Data  refers  to  a  set  of  activities  designed  to refine and  extract  new knowledge from complex  datasets.  The   proposed  model  facilitates  a  parallel  mining  of  frequent item sets for Ambient Assisted Living (AAL) System [a.k.a. Health  Care [System]  of  big  data that  reside   inside  a  cloud  environment.  We  extend  a  knowledge  discovery framework for  processing  and  classifying  the  abnormal  conditions of patients having fluctuations in Blood Pressure (BP) and Heart Rate(HR) and storing  this data  sets  called  Big data  into Cloud to access from  anywhere   when  needed.   This   accessed data is used to compare the new data with it, which helps to know the patients health condition.  

A Stream Data Processing Framework for Location-Based Service Using NoSQL Technology

2015 ◽  
Vol 763 ◽  
pp. 159-163 ◽  
Author(s):  
Nan Ju Kim ◽  
Eui In Choi
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Streaming Data ◽  
Location Based Services ◽  
Location Based Service ◽  
Stream Data ◽  
Data Processing Method ◽  
Specific Device ◽  
Internet Development ◽  
Processing Framework

One of the most exciting changes in Location-Based Services is the incredible growth of internet, development of wearable devices, and advanced positioning technologies. In addition, the big data from those sources helps performing seamless LBS as a technology. The existing processing methods used to detect the location of a particular tag, or specific device are not enough for complex processing while collecting all of the streaming data at the same time using a variety of wireless communication system [10,11,12,13]. We can use big data processing method for processing all the streaming data in real time. In this paper, we propose a framework for improving performance of Seamless LBS using NoSQL technology.

scholarly journals Deep Intelligent Prediction Network: A Novel Deep Learning Based Prediction Model on Spatiotemporal Characteristics and Location Based Services for Big Data Driven Intelligent Transportation System

2019 ◽  
Vol 8 (11) ◽  
pp. 845-849
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Recommendation System ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Location Based Services ◽  
Traffic Data ◽  
Spatiotemporal Characteristics

The concept of big Data for intelligent transportation system has been employed for traffic management on dealing with dynamic traffic environments. Big data analytics helps to cope with large amount of storage and computing resources required to use mass traffic data effectively. However these traditional solutions brings us unprecedented opportunities to manage transportation data but it is inefficient for building the next-generation intelligent transportation systems as Traffic data exploring in velocity and volume on various characteristics. In this article, a new deep intelligent prediction network has been introduced that is hierarchical and operates with spatiotemporal characteristics and location based service on utilizing the Sensor and GPS data of the vehicle in the real time. The proposed model employs deep learning architecture to predict potential road clusters for passengers. It is injected as recommendation system to passenger in terms of mobile apps and hardware equipment employment on the vehicle incorporating location based services models to seek available parking slots, traffic free roads and shortest path for reach destination and other services in the specified path etc. The underlying the traffic data is classified into clusters with extracting set of features on it. The deep behavioural network processes the traffic data in terms of spatiotemporal characteristics to generate the traffic forecasting information, vehicle detection, autonomous driving and driving behaviours. In addition, markov model is embedded to discover the hidden features .The experimental results demonstrates that proposed approaches achieves better results against state of art approaches on the performance measures named as precision, execution time, feasibility and efficiency.

scholarly journals Positioning Methods and the Use of Location and Activity Data in Forests

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 458 ◽  
Author(s):  
Robert F. Keefe ◽  
Ann M. Wempe ◽  
Ryer M. Becker ◽  
Eloise G. Zimbelman ◽  
Emily S. Nagler ◽  
...  
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Natural Resources ◽  
Data Science ◽  
Fire Management ◽  
Location Based Services ◽  
Future Research ◽  
Positioning Systems ◽  
Incident Command ◽  
Location Sharing

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

scholarly journals A survey on context awareness in big data analytics for business applications

2020 ◽  
Vol 62 (9) ◽  
pp. 3387-3415
Author(s):  
Loan Thi Ngoc Dinh ◽  
Gour Karmakar ◽  
Joarder Kamruzzaman
Keyword(s):  
Big Data ◽  
Context Awareness ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Applications

Use and validation of location-based services in urban research: An example with Dutch restaurants

Urban Studies ◽  
2018 ◽  
Vol 56 (5) ◽  
pp. 868-884 ◽  
Author(s):  
Daniel Arribas-Bel ◽  
Jessie Bakens
Keyword(s):  
Big Data ◽  
The Netherlands ◽  
Detailed Analysis ◽  
Urban Geography ◽  
Location Based Services ◽  
Urban Landscapes ◽  
Location Based Service ◽  
Economic Data ◽  
Urban Research ◽  
Urban Amenities

This article focuses on the use of big data for urban geography research. We collect data from the location-based service Foursquare in The Netherlands and employ it to obtain a rich catalogue of restaurant locations and other urban amenities, as well as a measure of their popularity among users. Because the Foursquare data can be combined with traditional sources of socio-economic data obtained from Statistics Netherlands, we can quantify, document and characterise some of the biases inherent in these new sources of data in the context of urban applications. A detailed analysis is given as to when this type of big data is useful and when it is misleading. Although the users of Foursquare are not representative of the whole population, we argue that this inherent bias can be exploited for research about the attractiveness of urban landscapes and consumer amenities in addition to the more traditional data on urban amenities.

scholarly journals Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19

2020 ◽  
Vol 17 (20) ◽  
pp. 7664 ◽  
Author(s):  
Hongbin He ◽  
Yonglin Shen ◽  
Changmin Jiang ◽  
Tianqi Li ◽  
Mingqiang Guo ◽  
...  
Keyword(s):  
Big Data ◽  
Health Risk ◽  
Health Risks ◽  
Driving Factors ◽  
Location Based Services ◽  
Population Exposure ◽  
Regional Environment ◽  
The Status ◽  
Pm2.5 Exposure ◽  
Pm2.5 Concentration

The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 exposure and health risk during COVID-19, and its driving factors have been analyzed using spatiotemporal big data, including Tencent location-based services (LBS) data, place of interest (POI), and PM2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM2.5 concentration firstly. Then, population exposure and health risks of PM2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM2.5 pollution, the relationship between PM2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM2.5 concentrations distributed with a U-shape, i.e., with a decrease followed by an increase from January to December. In terms of spatial distribution, the PM2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM2.5 in the west is significantly lower than that in the Middle East. Urban gross domestic product (GDP) and urban green area are negatively correlated with PM2.5; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM2.5 pollution. In terms of reducing the health risks and PM2.5 pollution, several pointed suggestions to improve the status has been proposed.

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