scholarly journals Preserving Transactional Data

2017 ◽  
Vol 11 (2) ◽  
pp. 126-137
Author(s):  
Sara Day Thomson
Keyword(s):  
Big Data ◽  
Administrative Data ◽  
Academic Research ◽  
Database Systems ◽  
Use Cases ◽  
Ethical Challenges ◽  
Or Education ◽  
Ethical Frameworks ◽  
Transactional Data

This paper is an adaptation of a longer report commissioned by the UK Data Service. The longer report contributes to on-going support for the Big Data Network – a programme funded by the Economic and Social Research Council (ESRC). The longer report can be found at doi:10.7207/twr16-02. This paper discusses requirements for preserving transactional data and the accompanying challenges facing the companies and institutions who aim to re-use these data for analysis or research. It presents a range of use cases – examples of transactional data – in order to describe the characteristics and difficulties of these ‘big’ data for long-term access. Based on the overarching trends discerned in these use cases, the paper will define the challenges facing the preservation of these data early in the curation lifecycle. It will point to potential solutions within current legal and ethical frameworks, but will focus on positioning the problem of re-using these data from a preservation perspective. In some contexts, these data could be fiscal in nature, deriving from business ‘transactions’. This paper, however, considers transactional data more broadly, addressing any data generated through interactions with a database system. Administrative data, for instance, is one important form of transactional data collected primarily for operational purposes, not for research. Examples of administrative data include information collected by government departments and other organisations when delivering a service (e.g. tax, health, or education) and can entail significant legal and ethical challenges for re-use. Transactional data, whether created by interactions between government database systems and citizens or by automatic sensors or machines, hold potential for future developments in academic research and consumer analytics. Re-use of reliable transactional data in research has the power to improve services and investments by organisations in many different sectors. Ultimately, however, these data will only lead to new discoveries and insights if they are effectively curated and preserved to ensure appropriate reproducibility. This paper explores challenges to this undertaking and approaches to ensuring long-term access. 

scholarly journals The Longitudinal IntermediaPlus (2014–2016): A Case Study in Structuring Unstructured Big Data

2021 ◽  
Vol 6 (1) ◽  
pp. 1-16
Author(s):  
Inga Brentel ◽  
Kristi Winters
Keyword(s):  
Big Data ◽  
Academic Research ◽  
The Novel ◽  
Cross Media ◽  
Novel Structure ◽  
Metadata File ◽  
Data Source ◽  
Media Channels ◽  
Research Challenge

Abstract This article details the novel structure developed to handle, harmonize and document big data for reuse and long-term preservation. ‘The Longitudinal IntermediaPlus (2014–2016)’ big data dataset is uniquely rich: it covers an array of German online media extendable to cross-media channels and user information. The metadata file for this dataset, and its documentation, were recently deposited as its own MySQL database called charmstana_sample_14-16.sql (https://data.gesis.org/sharing/#!Detail/10.7802/2030) (cs16) and is suitable for generating descriptive statistics. Analogous to the ‘Data View’ in spss, the charmstana_analysis (ca) contains the dataset’s numerical values. Both the cs16 and ca MySQL files are needed to conduct analysis on the full database. The research challenge was to process large-scaled datasets into one longitudinal, big-data data source suitable for academic research, and according to fair principles. The authors review four methodological recommendations that can serve as a framework for solving big-data structuring challenges, using the harmonization software CharmStats.

Big Data Analytics Demystified

Big Data ◽  
2016 ◽  
pp. 1859-1894
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Use Cases ◽  
Sensor Data ◽  
New Paradigm ◽  
Time Data ◽  
Real Time Data ◽  
Major Application

This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.

Big Data Analytics Demystified

2014 ◽  
pp. 38-73 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Use Cases ◽  
Sensor Data ◽  
New Paradigm ◽  
Time Data ◽  
Real Time Data ◽  
Major Application

This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool

Pandemic Disease, Public Health, and Ethics

2019 ◽  
pp. 796-811 ◽  
Author(s):  
Maxwell Smith ◽  
Ross Upshur
Keyword(s):  
Public Health ◽  
Ethical Issues ◽  
Ethical Challenges ◽  
Pandemic Preparedness ◽  
Scarce Resources ◽  
Ethical Frameworks ◽  
Health Measures ◽  
Care Workers ◽  
The Face ◽  
Conducting Research

Infectious disease pandemics raise significant and novel ethical challenges to the organization and practice of public health. This chapter provides an overview of the salient ethical issues involved in preparing for and responding to pandemic disease, including those arising from deploying restrictive public health measures to contain and curb the spread of disease (e.g., isolation and quarantine), setting priorities for the allocation of scarce resources, health care workers’ duty to care in the face of heightened risk of infection, conducting research during pandemics, and the global governance of preventing and responding to pandemic disease. It also outlines ethical guidance from prominent ethical frameworks that have been developed to address these ethical issues and concludes by discussing some pressing challenges that must be addressed if ethical reflection is to make a meaningful difference in pandemic preparedness and response.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

Procurement Analytics: Enabling Data Architectures in the Age of Big Data

2020 ◽  
Author(s):  
Michael Goul ◽  
T. S. Raghu ◽  
Ziru Li
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Research Literature ◽  
Structured Interviews ◽  
Data Space ◽  
Prescriptive Analytics ◽  
Data Architecture ◽  
The Future ◽  
High Level ◽  
Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

The Ethics of AI in Biomedical Research, Patient Care, and Public Health

2020 ◽  
pp. 702-718 ◽  
Author(s):  
Alessandro Blasimme ◽  
Effy Vayena
Keyword(s):  
Public Health ◽  
Patient Care ◽  
Biomedical Research ◽  
Disease Surveillance ◽  
Ethical Issues ◽  
Ethical Review ◽  
Ethical Challenges ◽  
Healthcare Provision ◽  
Social Scientists ◽  
Ethical Frameworks

This chapter explores ethical issues raised by the use of artificial intelligence (AI) in the domain of biomedical research, healthcare provision, and public health. The litany of ethical challenges that AI in medicine raises cannot be addressed sufficiently by current regulatory and ethical frameworks. The chapter then advances the systemic oversight approach as a governance blueprint, which is based on six principles offering guidance as to the desirable features of oversight structures and processes in the domain of data-intense biomedicine: adaptivity, flexibility, inclusiveness, reflexivity, responsiveness, and monitoring (AFIRRM). In the research domain, ethical review committees will have to incorporate reflexive assessment of the scientific and social merits of AI-driven research and, as a consequence, will have to open their ranks to new professional figures such as social scientists. In the domain of patient care, clinical validation is a crucial issue. Hospitals could equip themselves with “clinical AI oversight bodies” charged with the task of advising clinical administrators. Meanwhile, in the public health sphere, the new level of granularity enabled by AI in disease surveillance or health promotion will have to be negotiated at the level of targeted communities.

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