scholarly journals Big Data and Personalisation for Non-Intrusive Smart Home Automation

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Suriya Priya R. Asaithambi ◽  
Sitalakshmi Venkatraman ◽  
Ramanathan Venkatraman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Smart Devices ◽  
Home Automation ◽  
Open Standard ◽  
Iot Devices ◽  
And Control

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

Sustainable Smart Home and Home Automation: Big Data Analytics Approach

2016 ◽  
Vol 10 (8) ◽  
pp. 177-198 ◽  
Author(s):  
Rita Yi Man Li ◽  
Herru Ching Yu Li ◽  
Cho Kei Mak ◽  
Tony Beiqi Tang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Home Automation

scholarly journals The Smart Triad : Big Data Analytics , Cloud Computing and Internet of Things to shape the Smart Home, Smart City, Smart Business & Smart Country

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3594-3600 ◽  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Smart Home ◽  
Service Providers ◽  
Irrigation System ◽  
Electronic Devices ◽  
Big Data Analytics ◽  
User Privacy

Big data analytics, cloud computing & internet of things are a smart triad which have started shaping our future towards smart home, city, business, country. Internet of things is a convergence of intelligent networks, electronic devices, and cloud computing. The source of big data at different connected electronic devices is stored on cloud server for analytics. Cloud provides the readymade infrastructure, remote processing power to consumers of internet of things. Cloud computing also gives device manufacturers and service providers access to ―advanced analytics and monitoring‖, ―communication between services and devices‖, ―user privacy and security‖. This paper, presents an overview of internet of things, role of cloud computing & big data analytics towards IoT. In this paper IoT enabled automatic irrigation system is proposed that saves data over ―ThingSpeak‖ database an IoT analytics platform through ESP8266 wifi module. This paper also summarizes the application areas and discusses the challenges of IoT.

scholarly journals Text Mining and Big Data Analytics for Retrospective Analysis of Clinical Texts from Outpatient Care

2015 ◽  
Vol 15 (4) ◽  
pp. 58-77 ◽  
Author(s):  
Svetla Boytcheva ◽  
Galia Angelova ◽  
Zhivko Angelov ◽  
Dimitar Tcharaktchiev
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Text Mining ◽  
Retrospective Analysis ◽  
Outpatient Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Free Text ◽  
Arterial Blood ◽  
And Control

Abstract This paper presents the results of an on-going research project for knowledge extraction from large corpora of clinical narratives in Bulgarian language, approximately 100 million of outpatient care notes. Entities with numerical values are mined in the free text and the extracted information is stored in a structured format. The Algorithms for retrospective analyses and big data analytics are applied for studying the treatment and evaluating the diabetes compensation and control of arterial blood pressure.

A smart home energy management system using IoT and big data analytics approach

2017 ◽  
Vol 63 (4) ◽  
pp. 426-434 ◽  
Author(s):  
A.R. Al-Ali ◽  
Imran A. Zualkernan ◽  
Mohammed Rashid ◽  
Ragini Gupta ◽  
Mazin Alikarar
Keyword(s):  
Big Data ◽  
Energy Management ◽  
Management System ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

scholarly journals Novel holistic architecture for analytical operation on sensory data relayed as cloud services

2020 ◽  
Vol 10 (4) ◽  
pp. 4322
Author(s):  
Manujakshi B. C ◽  
K. B. Ramesh
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Services ◽  
Sensor Nodes ◽  
Sensory Data ◽  
Energy Constrained ◽  
Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

scholarly journals Big Data Analytics and Processing Platform in Czech Republic Healthcare

2020 ◽  
Vol 10 (5) ◽  
pp. 1705
Author(s):  
Martin Štufi ◽  
Boris Bačić ◽  
Leonid Stoimenov
Keyword(s):  
Big Data ◽  
Czech Republic ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Cost Effective ◽  
The European Union ◽  
Production Environment ◽  
The Czech Republic ◽  
Data Mining Algorithms

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

Data Mining, Big Data, Data Analytics

Web Services ◽  
2019 ◽  
pp. 89-104
Author(s):  
Priya P. Panigrahi ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Smart Devices ◽  
Data Life Cycle ◽  
The Impact ◽  
Special Relevance ◽  
Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

Data Mining, Big Data, Data Analytics

2017 ◽  
pp. 91-111
Author(s):  
Priya P. Panigrahi ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Smart Devices ◽  
Data Life Cycle ◽  
The Impact ◽  
Special Relevance ◽  
Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

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