Ecological views of big data: Perspectives and issues

Shin, Dong‐Hee; Choi, Min Jae

doi:10.1016/j.tele.2014.09.006

Cited by 127 publications

(51 citation statements)

References 9 publications

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“…The goal of industrial CPS is to find the customers' demand and provide personalized product and real-time PLCM, so it is necessary to collect and analyze these data [34]. The most challenging aspect is to explore the large volumes of data and extract useful information or knowledge for future actions [35].…”

Section: Industrial Big Datamentioning

confidence: 99%

Cloud-assisted industrial cyber-physical systems: An insight

Yue

Cai

Yan

et al. 2015

Microprocessors and Microsystems

133

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Section: Industrial Big Datamentioning

confidence: 99%

Cloud-assisted industrial cyber-physical systems: An insight

Yue

Cai

Yan

et al. 2015

Microprocessors and Microsystems

133

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“…Chen et al 2014, Davenport 2013, Dhawan et al 2014, Jagadish et al 2014. As a technology revolution itself, it is fair to say that big data research often shows a bias toward 1 Hadoop is an open source software framework for distributed storage and distributed processing of large data sets (Shin 2015, Shin andChoi, 2015). In this paper, social dynamics refer to the users' understanding, commitment, and perceived value of big data, within a given context.…”

Section: Figure 1: Use Of Methodological Lens For the Development Of mentioning

confidence: 99%

Development of a Theoretical Framework to Investigate Alignment of Big Data in Healthcare through a Social Representation Lens

Weerasinghe

Pauleen

Scahill

et al. 2018

AJIS

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The aim of this paper is to develop a theoretical framework grounded in the literature, which can be used to explore the influence of big data on business-IT alignment in the healthcare context. Increasingly the availability of information systems in healthcare delivery and service management results in massive amounts of complex data that have the 3V characteristics of big data (i.e. volume, variety, velocity). Use of big-healthcare-data has been identified as bringing significant benefits to the healthcare sector from improved decision making through to population health analysis. Although the technical dynamics around big data such as analytics and infrastructure requirements are extensively researched, less attention has been given to social dynamics such as peoples' experience, understanding and perceived usefulness of this data. To address this gap, the paper uses social representation theory as a methodological lens to develop a theoretical framework to study the social dynamics around big data and its use in the healthcare context. The selected case for this development is the New Zealand healthcare sector and an approach using multi-level macro, meso, and micro analysis is taken. Use of social representation theory as a methodological lens to develop a theoretical framework is a novel approach. Such a theoretical framework will be useful as a foundation for carrying out on-going empirical research on big data to understand its influence on business-IT alignment in the healthcare context.

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“…Demchenko et al [18] and Shin and Choi [25] studied the big data ecosystems, with some key differences. The latter take a broader approach in proposing the key elements, also considering the social aspects, in addition to the technological aspects presented by the former.…”

Section: Data Ecosystem Characteristics and Elementsmentioning

confidence: 99%

“…(2) Searching, finding, assessing, and viewing data and the associated licenses [17] (3) Cleansing, analyzing, enriching, combining, linking, and visualizing data (4) Interpreting and discussing the data and providing feedback to stakeholders and the data providers (2) Innovators as a combination of technology, business, and the government [25] (3) Users, civil society, and business…”

Section: Ecosystem Type Element or Component Sourcementioning

confidence: 99%

A Data Ecosystem for Data-Driven Thermal Energy Transition: Reflection on Current Practice and Suggestions for Re-Design

et al. 2020

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The transition towards low-carbon thermal energy systems requires solid information provision to support both public and private decision-making, which is future proof and optimal in the context of the system dependencies. We adopt a data ecosystem approach to answer the following research question: How can a data ecosystem be analyzed and developed to enable the data-driven support of the local thermal energy transition, by capturing both social and technical aspects of the urban thermal energy system? A case study research design of the Netherlands, with an embedded case of the city of Utrecht therein, was used, including data collection involving 21 expert interviews representing a diversity of stakeholders, and qualitative data analysis using NVivo version 10. The data ecosystem includes the necessary elements, roles, and context for decision makers in a local heat transition and captures the social as well as technical aspects of an urban thermal energy system. Assessment of the data ecosystem pertaining to thermal heat transition in the city of Utrecht shows that it is still in its infancy phase, with challenges, barriers, and shortcomings in all its key elements. We present suggestions for the (re-)design of an inclusive and holistic data ecosystem that addresses the current shortcomings.

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Ecological views of big data: Perspectives and issues

Cited by 127 publications

References 9 publications

Cloud-assisted industrial cyber-physical systems: An insight

Cloud-assisted industrial cyber-physical systems: An insight

Development of a Theoretical Framework to Investigate Alignment of Big Data in Healthcare through a Social Representation Lens

A Data Ecosystem for Data-Driven Thermal Energy Transition: Reflection on Current Practice and Suggestions for Re-Design

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