<i>Data Analytics: Hyped Up Aspirations or True Potential?</i>

Banerjee, Arindam; Bandyopadhyay, Tathagata; Acharya, Prachi T.

doi:10.1177/0256090920130401

Cited by 98 publications

(48 citation statements)

References 2 publications

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“…Structured, unstructured, or semi-structured data types are used in a variety of applications. Structured data, which includes only five percent of all existing data [20] is the tabular data found in relational databases and spreadsheets. The semi-structured data does not necessarily follow strict standards.…”

Section: It Has Complex Interrelationshipmentioning

confidence: 99%

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Scalable Security Analytics Framework Using NoSQL Database

Rahman¹,

Tomar²

2017

IJDTA

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Section: It Has Complex Interrelationshipmentioning

confidence: 99%

“…Even retailers are generating high volumes of data. For example, Wal-Mart processes more than one million transactions per hour [20]. Facebook processes 500TB each day and hundred hours of video are uploaded to YouTube every minute [19].…”

Section: It Has Complex Interrelationshipmentioning

confidence: 99%

Scalable Security Analytics Framework Using NoSQL Database

Rahman¹,

Tomar²

2017

IJDTA

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“…Finally, employing prescriptive analysis in order to clarify reason and result effects for business optimization would work fine when all the required dimensions were included. However, most of the databases are constrained on the number of dimensions (Banerjee, et al, 2013) and provide only partial insight into complex business problems.…”

Section: Issues and Challenges In Big Data And Its Analyticsmentioning

confidence: 99%

Can Big Data Analysis Make Cities Smarter? Emerging Issues and Stakeholder Implicationsin Smart Mobility

Gürsoy¹,

Yücelen²

2017

Eur. j. econ. bus. stud.

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This study deals with the challenges and bottlenecks with respect to the concept of smart cities which has largely been constructed on knowledge utilization issues and challenges. Despite the abundant existent literature in this field, the effective transformation of data into knowledge which can become a source of competitive advantage is still an ongoing debate, especially due to contemporary developments in big data analysis methods, approaches and strategies. As an emerging problem, the derivation of significant meaning from big data is among popular academic research fields, as well as being a crucial industrial and policy making engagement regarding value creating mechanisms in smart cities. Therefore in this study, limitations and challenges in translating big data into valuable knowledge in academia and industries are considered within the concept of smart mobility. In an attempt to propose researchers, business firms and governmental entities a collaborative approach, a perception about emerging issues is presented for clarifying some future constructs intersecting in relevant research and applied fields.

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“…In this paper the approach of benchmarking scientific output is defined as a fact-based consulting [5]. Modern commercial benchmarking usually refers to the process of identifying the best methods, practices, and processes of an organization to improve one's own organization [2] [5] [6].…”

Section: A Benchmarking Of Scientific Outputmentioning

confidence: 99%

Benchmarking of Scientific Research Clusters by Use of Text Mining Algorithms on Textual Artefacts

Schroeder

Tummel

Isenhardt

et al. 2016

2016 International Conference on Information Systems Engineering (ICISE)

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The development of text mining algorithms is far reaching and thus, new application areas arise, inter alia in the framework of applying modern information technologies (e.g. big data analytics) in order to answer social and economic (research) questions (digital humanities). In this paper existing algorithms to reveal results in the context of benchmarking scientific research clusters are combined. For conducting the analysis, two data corpora are gathered. The first data corpus contains abstracts of publications according to the used application case of the Cluster of Excellence Tailor-Made Fuels from Biomass 'TMFB' at RWTH Aachen University. The second data corpus -based on the analysis of the first data corpus -contains content-related abstracts of research belonging to the relevant scientific community. In the framework of processing the gathered data, information extraction, information retrieval and named entity recognition among others are utilized for analyzing the publications in order to derive useful findings. Furthermore association mining findings are introduced. As an excerpt of the results, the identification of thematic alignments and developments over time are presented for both corpora and are subsequently compared.

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Data Analytics: Hyped Up Aspirations or True Potential?

Cited by 98 publications

References 2 publications

Scalable Security Analytics Framework Using NoSQL Database

Scalable Security Analytics Framework Using NoSQL Database

Can Big Data Analysis Make Cities Smarter? Emerging Issues and Stakeholder Implicationsin Smart Mobility

Benchmarking of Scientific Research Clusters by Use of Text Mining Algorithms on Textual Artefacts

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