scholarly journals Time resolved surface photovoltage measurements using a big data capture approach to KPFM

2018 ◽  
Vol 29 (44) ◽  
pp. 445703 ◽  
Author(s):  
Liam Collins ◽  
Mahshid Ahmadi ◽  
Jiajun Qin ◽  
Yongtao Liu ◽  
Olga S Ovchinnikova ◽  
...  
Keyword(s):  
Big Data ◽  
Data Capture ◽  
Surface Photovoltage ◽  
Time Resolved

Big Data Analytics

2019 ◽  
pp. 67-81
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Big Data ◽  
Open Problem ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Capture ◽  
Data Sets ◽  
Full Potential ◽  
Processing Application ◽  
Nosql Databases ◽  
The World

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

The New “ABC” of ICTs (Analytics + Big Data + Cloud Computing)

Big Data ◽  
2016 ◽  
pp. 2165-2198
Author(s):  
José Carlos Cavalcanti
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Data Capture ◽  
Unstructured Data ◽  
Network Computing ◽  
Time Data ◽  
Trade Off ◽  
Information And Communication

Analytics (discover and communication of patterns, with significance, in data) of Big Data (basically characterized by large structured and unstructured data volumes, from a variety of sources, at high velocity - i.e., real-time data capture, storage, and analysis), through the use of Cloud Computing (a model of network computing) is becoming the new “ABC” of information and communication technologies (ICTs), with important effects for the generation of new firms and for the restructuring of those ones already established. However, as this chapter argues, successful application of these new ABC technologies and tools depends on two interrelated policy aspects: 1) the use of a proper model which could help one to approach the structure and dynamics of the firm, and, 2) how the complex trade-off between information technology (IT) and communication technology (CT) costs is handled within, between and beyond firms, organizations and institutions.

Improving Healthcare with Data-Driven Track-and-Trace Systems

2015 ◽  
pp. 65-82 ◽  
Author(s):  
Eldar Sultanow ◽  
Alina M. Chircu
Keyword(s):  
Quality Assurance ◽  
Big Data ◽  
Value Added ◽  
High Volume ◽  
Data Capture ◽  
Data Driven ◽  
Healthcare Organizations ◽  
Efficient Management ◽  
Internal Operations ◽  
Pharmaceutical Supply Chains

This chapter illustrates the potential of data-driven track-and-trace technology for improving healthcare through efficient management of internal operations and better delivery of services to patients. Track-and-trace can help healthcare organizations meet government regulations, reduce cost, provide value-added services, and monitor and protect patients, equipment, and materials. Two real-world examples of commercially available track-and-trace systems based on RFID and sensors are discussed: a system for counterfeiting prevention and quality assurance in pharmaceutical supply chains and a monitoring system. The system-generated data (such as location, temperature, movement, etc.) about tracked entities (such as medication, patients, or staff) is “big data” (i.e. data with high volume, variety, velocity, and veracity). The chapter discusses the challenges related to data capture, storage, retrieval, and ultimately analysis in support of organizational objectives (such as lowering costs, increasing security, improving patient outcomes, etc.).

The New “ABC” of ICTs (Analytics + Big Data + Cloud Computing)

2016 ◽  
pp. 152-186
Author(s):  
José Carlos Cavalcanti
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Data Capture ◽  
Unstructured Data ◽  
Network Computing ◽  
Time Data ◽  
Trade Off ◽  
Information And Communication

Analytics (discover and communication of patterns, with significance, in data) of Big Data (basically characterized by large structured and unstructured data volumes, from a variety of sources, at high velocity - i.e., real-time data capture, storage, and analysis), through the use of Cloud Computing (a model of network computing) is becoming the new “ABC” of information and communication technologies (ICTs), with important effects for the generation of new firms and for the restructuring of those ones already established. However, as this chapter argues, successful application of these new ABC technologies and tools depends on two interrelated policy aspects: 1) the use of a proper model which could help one to approach the structure and dynamics of the firm, and, 2) how the complex trade-off between information technology (IT) and communication technology (CT) costs is handled within, between and beyond firms, organizations and institutions.

scholarly journals Big Data Revisited: A Rejoinder

2015 ◽  
Vol 30 (1) ◽  
pp. 70-74 ◽  
Author(s):  
Jannis Kallinikos ◽  
Ioanna D Constantiou
Keyword(s):  
Big Data ◽  
Final Section ◽  
Current Data ◽  
Data Capture ◽  
Sensor Data ◽  
Data Generation ◽  
Social Data ◽  
Use Of Data ◽  
Key Issues ◽  
Communicative Processes

We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper's commentators. We initially deal with the issue of social data and the role it plays in the current data revolution. The massive involvement of lay publics as instrumented by social media breaks with the strong expert cultures that have underlain the production and use of data in modern organizations. It also sets apart the interactive and communicative processes by which social data is produced from sensor data and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data.

Toward a Conceptualization of Big Data Value Chain

2019 ◽  
pp. 76-92 ◽  
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

Improving Healthcare with Data-Driven Track-and-Trace Systems

Big Data ◽  
2016 ◽  
pp. 1229-1246
Author(s):  
Eldar Sultanow ◽  
Alina M. Chircu
Keyword(s):  
Quality Assurance ◽  
Big Data ◽  
Value Added ◽  
High Volume ◽  
Data Capture ◽  
Data Driven ◽  
Healthcare Organizations ◽  
Efficient Management ◽  
Internal Operations ◽  
Pharmaceutical Supply Chains

This chapter illustrates the potential of data-driven track-and-trace technology for improving healthcare through efficient management of internal operations and better delivery of services to patients. Track-and-trace can help healthcare organizations meet government regulations, reduce cost, provide value-added services, and monitor and protect patients, equipment, and materials. Two real-world examples of commercially available track-and-trace systems based on RFID and sensors are discussed: a system for counterfeiting prevention and quality assurance in pharmaceutical supply chains and a monitoring system. The system-generated data (such as location, temperature, movement, etc.) about tracked entities (such as medication, patients, or staff) is “big data” (i.e. data with high volume, variety, velocity, and veracity). The chapter discusses the challenges related to data capture, storage, retrieval, and ultimately analysis in support of organizational objectives (such as lowering costs, increasing security, improving patient outcomes, etc.).

Big Data in Shipbuilding

2014 ◽  
Author(s):  
Patrick L. David ◽  
Patrick D. Roberts
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Ad Hoc ◽  
Data Capture ◽  
Sources Of Information ◽  
Rapid Convergence ◽  
Business Decisions ◽  
Business Decision ◽  
Existing Data

Recent strides in data analytics have uncovered interesting and actionable correlations across many different industries. Organizations are finding opportunities for making more intelligent business decisions by enhancing data with new insights and sources of information. In many cases these insights are gleaned through deeper analytics of existing data. The relatively large amount of information generated through the shipbuilding enterprise, coupled with rapidly advancing methods for optimizing data capture, points to a rapid convergence on exploiting data analytics for enhanced business decision making. An ad-hoc working group was formed consisting of multiple US shipyards with broad representation across the NSRP to investigate opportunities to leverage modern data analytics.

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