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Big data and the internet of things
01 Apr 2015-Management Intercultural (Romanian Foundation for Business Intelligence)-Iss: 33, pp 211-215
TL;DR: The IoT is defined, looking at Big Data, based on unprecedented connectivity among objects and to collect massive amounts of data, Internet of Things (IoT) is ready to provide significant business benefits.
Abstract: Nowadays, technology is on an evolution wave both in terms of software (complexes and complete business package software solutions) and hardware (increasing processing power for mobile devices, large consume of Internet world wide). The speed with which humans interact with the Internet, use social media and interconnect their devices with other devices is rapidly growing. This desire to stay connected is translated as an exponential growth of volumes of data. Therefore all data that is generated represents the main engine for innovation both in terms of business and technology. Based on unprecedented connectivity among objects and to collect massive amounts of data, Internet of Things (IoT) is ready to provide significant business benefits. Organizations are interested to adopt IoT as a business strategy and they must be prepared to address a number of technical and administrative challenges. The purpose of this article is to define the IoT , looking at Big Data.
Citations
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TL;DR: This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems workshop, respectively, by presenting a big data analytics framework that depicts a process view of the components needed for big data Analytics in organizations.
Abstract: This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.
112 citations
Cites background from "Big data and the internet of things..."
...For example, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia is developing technology to increase crop yield by performing sensor-based monitoring of plants, soil, and environmental conditions at high resolution (Zaslavsky, 2014)....
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...Practitioner interviews and literature revealed an increasing interest in the “Internet of things” and the associated brontobytes (1000 yottabytes or 10^27 bytes) of data (Zaslavsky, 2014; Grover & John, 2015)....
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TL;DR: This paper outlines some fundamental standardization activities for IoT and big Data approaches for real-time processing are outlined and tools for analytics are addressed.
Abstract: IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today's value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction between "everything", IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytic tools are able to extract "sense" from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset. The era of IoT relies heavily on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental standardization activities. Big Data approaches for real-time processing are outlined and tools for analytics are addressed. As consequence, IoT is a (fast) evolutionary process whose success in penetrating all dimensions of life heavily depends on close cooperation between standardization organizations, open source communities and IT experts.
23 citations
Cites methods from "Big data and the internet of things..."
...From the IoT perspective, Big Data is a subset of the IoT technology where Big Data software addresses data handling and IoT takes responsibility for sensors, devices, and data delivery (Dull 2015)....
[...]
01 Jan 2013
TL;DR: This report contains the definition of the architecture for the BETaaS platform, including all the components in the platform and the main external interfaces, in order to clarify how interactions will happen among the components, and gives some initial hints about the potential deployment of the components.
Abstract: This report contains the definition of the architecture for the BETaaS platform, including all the components in the platform and the main external interfaces, in order to clarify how interactions will happen among the components. It also gives some initial hints about the potential deployment of the components. This architecture will be used as the base of the implementation and updated in M16, according to testing and validation results. BETaaS D3.1.1 BETaaS Architecture Version 1.1 Page: 2 of 42 Document History Vers. Issue Date Total pages Modified pages Modifications implemented Author Organisation 0.1 25/03/2013 12 ToC, introduction and section 3.1, initial architecture in section 4 Fco. Javier Nieto ATOS 0.2 20/05/2013 14 6 Section 5.4, updates to section 4 and 3 Fco. Javier Nieto, Sergio García ATOS 0.3 20/06/2013 22 9 Content for section 3.7, 4.2, 5.6, 3.4, 5.5, 3.6, 5.7, 6 Fco. Javier Nieto, Sergio García, Izaskun Mendia, Luca Cucchi, Alessandro Mamelli, Davide Sommacampagna ATOS, TECN, INTECS, HP 0.4 26/06/2013 30 15 Updates in BigData, QoSM, Things Adaptor (sections 3 and 5). Updates to the architecture (section 4) Fco. Javier Nieto, Sergio García, Alessandro Mamelli, Davide Sommacampagna, Nikolaos Zonidis ATOS, HP, CONV 0.5 27/06/2013 32 13 Updates in 5.1, 5.2, 5.3, 5.5, 5.6 and 5.7 Fco. Javier Nieto, Sergio García, Belén Martínez, Luca Cucchi ATOS, TECN, INTECS 0.6 28/06/2013 36 10 Updates of diagrams and addition information about interfaces in section 5 Fco. Javier Nieto, Sergio García, Belén Martínez, Nikolaos Zonidis, Alessandro Mamelli, Davide Sommacampagna, Nikolaos Zonidis ATOS, TECN, CONV, HP 1.0 29/06/2013 41 10 Updates in section 1, section 3 and section 6 Fco. Javier Nieto ATOS 1.1 30/06/2013 41 3 Minor updates in several sections Fco. Javier Nieto, Izaskun Mendia, Belén Martínez ATOS, TECN BETaaS D3.1.1 BETaaS Architecture Version 1.1 Page: 3 of 42 Executive Summary According to the BETaaS conceptual model and the definition of the basic and extended capabilities, BETaaS must cover several functionalities at different levels: adaptation, TaaS and service. In this document, we have analysed the required capabilities, extracting a list of functionalities to be provided by BETaaS platforms. These functionalities have been described and have been mapped with the capabilities at the different layers. Based on these functionalities, a high level architecture has been defined in such a way it can cover all the functionalities and, at the same time, it does not require to perform isolated implementations for each layer. All those components identified have been detailed with concrete designs, in order to facilitate their implementation and, moreover, their external interfaces have been identified, in order to facilitate the definition of the interactions in the API focused document. Finally, deployment options have been described, in order to provide a clear idea on how to configure BETaaS gateways. BETaaS D3.1.1 BETaaS Architecture Version 1.1 Page: 4 of 42 Table of
19 citations
TL;DR: The aim of this article is to provide an overview of the architectures published so far, serving as a starting point for future research.
Abstract: Big Data and IoT have made huge strides in detection technologies, resulting in "smart" devices consisting of sensors, and massive data processing. So far, there is no common strategy for designing Big Data architectures containing IoT, since they depend on the context of the problem to be solved. But in recent years, various architectures have been proposed that serve as examples for future research in this area. The aim of this article is to provide an overview of the architectures published so far, serving as a starting point for future research. The methodology used is that of systematic mapping.
12 citations
Cites background from "Big data and the internet of things..."
...Según Shah (2016), el volume exponencial de datos adquiridos requieren de una poderosa infraestructura para soportar no sólo el almacenamiento y consulta, sino también la extracción desde distintos puntos de vista de estos datos [4]....
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...En consecuencia, los datos resultantes de estos dispositivos crecerán de manera exponencial, generando nuevas oportunidades de negocio, así como nuevos desafíos para la gestión y su procesamiento [4]....
[...]
14 Sep 2016
TL;DR: The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms.
Abstract: Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.
12 citations
Cites background from "Big data and the internet of things..."
...There is exponential growth in the number of unique, sensor-rich and cyber-enabled devices processing data and communicating with other devices and computers around the world [7]....
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...At the curb [7], a vehicle stops in front of you and a green light on the door indicates it is yours....
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References
More filters
TL;DR: This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems workshop, respectively, by presenting a big data analytics framework that depicts a process view of the components needed for big data Analytics in organizations.
Abstract: This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.
112 citations
TL;DR: This paper outlines some fundamental standardization activities for IoT and big Data approaches for real-time processing are outlined and tools for analytics are addressed.
Abstract: IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today's value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction between "everything", IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytic tools are able to extract "sense" from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset. The era of IoT relies heavily on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental standardization activities. Big Data approaches for real-time processing are outlined and tools for analytics are addressed. As consequence, IoT is a (fast) evolutionary process whose success in penetrating all dimensions of life heavily depends on close cooperation between standardization organizations, open source communities and IT experts.
23 citations
01 Jan 2013
TL;DR: This report contains the definition of the architecture for the BETaaS platform, including all the components in the platform and the main external interfaces, in order to clarify how interactions will happen among the components, and gives some initial hints about the potential deployment of the components.
Abstract: This report contains the definition of the architecture for the BETaaS platform, including all the components in the platform and the main external interfaces, in order to clarify how interactions will happen among the components. It also gives some initial hints about the potential deployment of the components. This architecture will be used as the base of the implementation and updated in M16, according to testing and validation results. BETaaS D3.1.1 BETaaS Architecture Version 1.1 Page: 2 of 42 Document History Vers. Issue Date Total pages Modified pages Modifications implemented Author Organisation 0.1 25/03/2013 12 ToC, introduction and section 3.1, initial architecture in section 4 Fco. Javier Nieto ATOS 0.2 20/05/2013 14 6 Section 5.4, updates to section 4 and 3 Fco. Javier Nieto, Sergio García ATOS 0.3 20/06/2013 22 9 Content for section 3.7, 4.2, 5.6, 3.4, 5.5, 3.6, 5.7, 6 Fco. Javier Nieto, Sergio García, Izaskun Mendia, Luca Cucchi, Alessandro Mamelli, Davide Sommacampagna ATOS, TECN, INTECS, HP 0.4 26/06/2013 30 15 Updates in BigData, QoSM, Things Adaptor (sections 3 and 5). Updates to the architecture (section 4) Fco. Javier Nieto, Sergio García, Alessandro Mamelli, Davide Sommacampagna, Nikolaos Zonidis ATOS, HP, CONV 0.5 27/06/2013 32 13 Updates in 5.1, 5.2, 5.3, 5.5, 5.6 and 5.7 Fco. Javier Nieto, Sergio García, Belén Martínez, Luca Cucchi ATOS, TECN, INTECS 0.6 28/06/2013 36 10 Updates of diagrams and addition information about interfaces in section 5 Fco. Javier Nieto, Sergio García, Belén Martínez, Nikolaos Zonidis, Alessandro Mamelli, Davide Sommacampagna, Nikolaos Zonidis ATOS, TECN, CONV, HP 1.0 29/06/2013 41 10 Updates in section 1, section 3 and section 6 Fco. Javier Nieto ATOS 1.1 30/06/2013 41 3 Minor updates in several sections Fco. Javier Nieto, Izaskun Mendia, Belén Martínez ATOS, TECN BETaaS D3.1.1 BETaaS Architecture Version 1.1 Page: 3 of 42 Executive Summary According to the BETaaS conceptual model and the definition of the basic and extended capabilities, BETaaS must cover several functionalities at different levels: adaptation, TaaS and service. In this document, we have analysed the required capabilities, extracting a list of functionalities to be provided by BETaaS platforms. These functionalities have been described and have been mapped with the capabilities at the different layers. Based on these functionalities, a high level architecture has been defined in such a way it can cover all the functionalities and, at the same time, it does not require to perform isolated implementations for each layer. All those components identified have been detailed with concrete designs, in order to facilitate their implementation and, moreover, their external interfaces have been identified, in order to facilitate the definition of the interactions in the API focused document. Finally, deployment options have been described, in order to provide a clear idea on how to configure BETaaS gateways. BETaaS D3.1.1 BETaaS Architecture Version 1.1 Page: 4 of 42 Table of
19 citations
TL;DR: The aim of this article is to provide an overview of the architectures published so far, serving as a starting point for future research.
Abstract: Big Data and IoT have made huge strides in detection technologies, resulting in "smart" devices consisting of sensors, and massive data processing. So far, there is no common strategy for designing Big Data architectures containing IoT, since they depend on the context of the problem to be solved. But in recent years, various architectures have been proposed that serve as examples for future research in this area. The aim of this article is to provide an overview of the architectures published so far, serving as a starting point for future research. The methodology used is that of systematic mapping.
12 citations
14 Sep 2016
TL;DR: The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms.
Abstract: Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.
12 citations