Understandable Big Data: A survey

Emani, Cheikh Kacfah; Cullot, Nadine; Nicolle, Christophe

doi:10.1016/j.cosrev.2015.05.002

Cited by 237 publications

(47 citation statements)

References 49 publications

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“…The authors drew on the literature on big data, social capital, and ENGOs for this research which, in turn, made theoretical contributions to enrich these three research fields. First, existing literature on big data primarily takes the perspective of technology, and focuses on big data analytics and application, such as state-of-the-art analytical methods [15,16,[65][66][67][68][69][70][71][72][73][74]. However, the authors viewed big data as a cultural and socio-technical phenomenon and conducted a scholarly inquiry into the social impacts of environmental big data, thereby contributing to discussions of the use of big data in society and its implications for management research.…”

Section: Discussionmentioning

confidence: 99%

“…Velocity refers to the frequency or the speed of data generation and delivery, and indicates that data are created and processed rapidly and in a timely manner [72]. Variety concerns the various forms of data, namely, structured data (data from relational databases) and unstructured data that cannot be processed using traditional analytical tools, such as text, webpage, images, video, and audio data [73,74]. IPE's environmental map database is described and understood in terms of the defining attributes of the 3Vs, as illustrated in Table 3.…”

Section: Method: Design Of Multiple-case Studiesmentioning

confidence: 99%

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Environmental Activism and Big Data: Building Green Social Capital in China

Zhang

2020

Sustainability

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Section: Discussionmentioning

confidence: 99%

Section: Method: Design Of Multiple-case Studiesmentioning

confidence: 99%

Environmental Activism and Big Data: Building Green Social Capital in China

Zhang

2020

Sustainability

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“…Watson (2014), McAfee and Brynjolfsson (2012), and Russom (2011) also characterise big data using these 3V's. Two additional V's -value and veracity -have also been identified, extending the characteristics of big data to 5V's (Kacfah Emani et al 2015, Saporito 2013, Sathi 2012. Based on the 5V characteristics Kacfah Emani et al (2015, p72) state "dealing effectively with big data requires one to create value against the volume, variety and veracity of data while it is still in motion (velocity), not just after it is at rest".…”

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

confidence: 99%

“…However traditional analytic capabilities are not sufficient to process big data. Much more advanced infrastructure and analytical techniques are needed to glean insight from data that is high in volume, variety and velocity (Kacfah Emani et al 2015).…”

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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“…There is a variety-data often does not have a fixed structure that is sufficiently ordered for immediate processing. Velocity refers to the dynamic creation of streams of data that may need to be processed in real time (Emani et al 2015). The major challenge arising from big data is the huge volume of data that leads to the difficulties in data storage, transmission, and processing.…”

Section: Introductionmentioning

confidence: 99%

A heuristics based global navigation satellite system data reduction algorithm integrated with map-matching

Dong

Hicks

2019

Ann Oper Res

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The transmission and storage of global navigation satellite system (GNSS) data places very high demands on mobile networks and centralised data processing systems. GNSS applications including community based navigation and fleet management require GNSS data to be transmitted from a vehicle to a centralised system and then processed by a map-matching algorithm to determine the location of a vehicle within a road segment. Various data compression techniques have been developed to reduce the volume of data transmitted. There is also an independent literature relating to map-matching algorithms. However, no previous research has integrated data compression with a map-matching algorithm that accepts compressed data as an input without the need for decompression. This paper develops a novel GNSS data reduction algorithm with deterministic error bounds, which was seamless integrated with a specifically designed map-matching algorithm. The approach significantly reduces the volume of GNSS data communicated and improves the performance of the map-matching algorithm. The data compression extracts critical points in the trajectory and velocity-time curve of a vehicle. During the process of selecting critical points, the error of restoring vehicle trajectories and velocity-time curves are used as parameters to control the number of critical points selected. By setting different error bound values prior to the execution of the algorithm, the accuracy and volume of reduced data is controlled precisely. The compressed GNSS data, particularly the critical points selected from the vehicle's trajectory is directly input to the map-matching algorithm without the need for decompression. An experiment indicated that the data reduction algorithm is very effective in reducing data volume. This research will be useful in many fields including community driven navigation and fleet management.

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Understandable Big Data: A survey

Cited by 237 publications

References 49 publications

Environmental Activism and Big Data: Building Green Social Capital in China

Environmental Activism and Big Data: Building Green Social Capital in China

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

A heuristics based global navigation satellite system data reduction algorithm integrated with map-matching

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