Reduction Scheme for Sensor-Data Transmission on a Big Data Streaming Platform

2018 ◽  
Author(s):  
Yi-Wei Huang ◽  
Sheng-Tzong Cheng
Keyword(s):  
Big Data ◽  
Data Transmission ◽  
Sensor Data ◽  
Data Streaming ◽  
Reduction Scheme

On the application of Big Data in future large-scale intelligent Smart City installations

2014 ◽  
Vol 10 (2) ◽  
pp. 168-182 ◽  
Author(s):  
Sylva Girtelschmid ◽  
Matthias Steinbauer ◽  
Vikash Kumar ◽  
Anna Fensel ◽  
Gabriele Kotsis
Keyword(s):  
Big Data ◽  
Real World ◽  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Sensor Data ◽  
Data Streaming ◽  
Content Type ◽  
Semantic Models ◽  
Intelligent Filtering

Purpose – The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale. Design/methodology/approach – The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine. Findings – By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem. Research limitations/implications – In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work. Originality/value – The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

scholarly journals Visualizing Effects of COVID-19 Social Isolation with Residential Activity Big Data Sensor Data

2020 ◽  
Author(s):  
Anuradha Rajkumar ◽  
Bruce Wallace ◽  
Laura Ault ◽  
Julien Lariviere-Chartier ◽  
Frank Knoefel ◽  
...  
Keyword(s):  
Big Data ◽  
Social Isolation ◽  
Sensor Data

SDN-based and multitenant-aware resource provisioning mechanism for cloud-based big data streaming

2019 ◽  
Vol 126 ◽  
pp. 133-149 ◽  
Author(s):  
Cleverton Vicentini ◽  
Altair Santin ◽  
Eduardo Viegas ◽  
Vilmar Abreu
Keyword(s):  
Big Data ◽  
Resource Provisioning ◽  
Data Streaming

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

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