A bibliometric approach to tracking big data research trends

Kalantari, Ali; Kamsin, Amirrudin; Kamaruddin, Halim Shukri; Ebrahim, Nader Ale; Gani, Abdullah; Ebrahimi, Afshin; Shamshirband, Shahaboddin

doi:10.1186/s40537-017-0088-1

Cited by 93 publications

(68 citation statements)

References 76 publications

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“…Bibliometric is defined as "a set of methods to quantitatively analyse academic literature and scholarly communications" (Das, 2015). There are multiple papers that have used bibliometrics in the fields of social science (Farahmand, Mariani, Ghanbari Baghestan, Ebrahim, & Matinnia, 2018;Ingwersen, 2000;Kalantari et al, 2017;Riazi et al, 2019) and communication (Gonzalez & Guarinos, 2017) to measure scientific progress. Bibliometrics is an essential aspect of measuring academic and organization performance based on various indices, including the number of publications, number of citations, and average citations per year (Davidson et al, 2014;Farahmand et al, 2018;Etemadifard, Khaniki, Ghanbari Baghestan, & Mehrnoosh, 2018).…”

Section: Methodsmentioning

confidence: 99%

A Crisis in “Open Access”: Should Communication Scholarly Outputs Take 77 Years to Become Open Access?

et al. 2019

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This study diachronically investigates the trend of the “open access” in the Web of Science (WoS) category of “communication.” To evaluate the trend, data were collected from 184 categories of WoS from 1980 to 2017. A total of 87,997,893 documents were obtained, of which 95,304 (0.10%) were in the category of “communication.” In average, 4.24% of the documents in all 184 categories were open access. While in communication, it was 3.29%, which ranked communication 116 out of 184. An Open Access Index (OAI) was developed to predict the trend of open access in communication. Based on the OAI, communication needs 77 years to fully reach open access, which undeniably can be considered as “crisis in scientific publishing” in this field. Given this stunning information, it is the time for a global call for “open access” by communication scholars across the world. Future research should investigate whether the current business models of publications in communication scholarships are encouraging open access or pose unnecessary restrictions on knowledge development.

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Section: Methodsmentioning

confidence: 99%

A Crisis in “Open Access”: Should Communication Scholarly Outputs Take 77 Years to Become Open Access?

et al. 2019

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“…Much similarly to the DICE effort, the IBM Stream Processing Language (SPL) initiative [24] provides an implementation language specific to programming streams management (e.g., Storm jobs) and related reactive systems. In addition, there are several work close to OSTIA in terms of their foundations and type of support, e.g., works focusing on distilling and analysing big data topologies by-design [36], as also highlighted in recent research by Kalantari et al [25].…”

Section: Related Workmentioning

confidence: 99%

Verifying big data topologies by-design: a semi-automated approach

et al. 2019

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IntroductionBig data or data-intensive applications (DIAs) process large amounts of data for the purpose of gaining key business intelligence through complex analytics using machinelearning techniques [20,35]. These applications are receiving increased attention in the last years given their ability to yield competitive advantage by direct investigation of user needs and trends hidden in the enormous quantities of data produced daily by the average Internet user. According to Gartner [1] business intelligence and analytics applications will remain a top focus for Chief-Information Officers (CIOs) of most Fortune 500 companies until at least 2019-2021. However, the cost of ownership of the systems that process big data analytics are high due to infrastructure costs, steep learning curves for the different frameworks (such as Apache Storm [21], Apache Spark [2] or Apache Hadoop [3]) typically involved in design and development of big data applications and complexities in large-scale architectures.A key complexity of the above design and development activity lies in quickly and continuously refining the configuration parameters of the middleware and service platforms on top of which the DIA is running [12]. The process in question is especially complex as the number of middleware involved in DIAs design increases; the more middleware are involved the more parameters need co-evaluation (e.g., latency or beaconing times, caching policies, queue retention and more)-fine-tuning these "knobs" on so many Abstract Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Apache Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors. Such complex architectures make possible refactoring of the applications a difficult task for software architects, as applications might be very different with respect to the initial designs. As an aid to designers and developers, we developed OSTIA (Ordinary Static Topology Inference Analysis) that allows detecting the occurrence of common anti-patterns across big data architectures and exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case-studies. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Big data as defined by Kalantari, et al (2017) and Monnappa (2017) includes the "fourVs": volume, velocity, variety and veracity of data (text, audio, visually) that are shared, transferred and used for different purposes in different fields. These four Vs are what distinguish big data from traditional data.…”

Section: The Fourth Industrial Revolution Engineering Competencies Anmentioning

confidence: 99%

Approaches in Developing Undergraduate IT Engineering Curriculum for the Fourth Industrial Revolution in Malaysia and Vietnam

Lieu

Duc

Gleason

et al. 2018

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Curricula have important effects on students' professional skills. To prepare for the fourth industrial revolution (4IR), countries are recommended to implement new curricula to develop new competencies for the automation workforce. As Information technology (IT) lays the foundations for the core 4IR-relevant emerging technologies (big data, internet of things, artificial intelligence, etc.), countries have developed new IT engineering curricula to prepare engineers for the 4IR. By conducting a structured focused comparison for bachelor IT curricula containing core 4IR modules of 57 Vietnamese and 35 Malaysian universities, this research aims to find appropriate approaches in developing curricula to prepare 4IR competencies for IT engineering students for Malaysia, Vietnam and implications for other nations. The research found that, only new stand-alone curricula with a focus on 4IR competencies can prepare engineers for the 4IR. It then provides seven lessons for designing new 4IR engineering curriculum and for continuing studies on 4IR-ready curriculum.

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A bibliometric approach to tracking big data research trends

Cited by 93 publications

References 76 publications

A Crisis in “Open Access”: Should Communication Scholarly Outputs Take 77 Years to Become Open Access?

A Crisis in “Open Access”: Should Communication Scholarly Outputs Take 77 Years to Become Open Access?

Verifying big data topologies by-design: a semi-automated approach

Approaches in Developing Undergraduate IT Engineering Curriculum for the Fourth Industrial Revolution in Malaysia and Vietnam

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