Critical analysis of Big Data challenges and analytical methods
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In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.About:
This article is published in Journal of Business Research.The article was published on 2017-01-01 and is currently open access. It has received 1267 citations till now. The article focuses on the topics: Analytics & Big data.read more
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Using Machine Learning for Identifying Ping Failure in Large Network Topology
TL;DR: This paper presents an ongoing work in exploring the use of machine learning algorithms for better diagnosis of network failure by using PING and analyzed 3 methods such Machine Learning (ML), Feature Selection with ML and hyperparameter tuning of ML.
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EROSO: Semantic Technologies Towards Thermal Comfort in Workplaces
TL;DR: EROSO (thERmal cOmfort SOlution), a framework that combines KDD processes and Semantic Technologies for ensuring thermal comfort in workplaces is presented and results show that Semantic technologies make the proposed solution more usable and extensible, as well as ensuring a thermal comfort situation throughout the working day.
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Big Data Analytics: principles, trends and tasks (a survey)
TL;DR: It is suggested that there exist three modes of large-scale usage of Big Data: 1) ‘intelligent information retrieval; 2) massive “intermediate” data processing (concentration, mining); 3) model inference from data; 4) knowledge discovery in data.
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H-tSNE: Hierarchical Nonlinear Dimensionality Reduction
TL;DR: This work extends the widely used t-Distributed Stochastic Neighbor Embedding technique to include hierarchical information and demonstrates its use with known or unknown class labels, and proposes a novel DR approach that can incorporate a known underlying hierarchy.
Book ChapterDOI
Big Data Analytics and Intelligence: A Perspective for Health Care
TL;DR: Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions.
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Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review
TL;DR: The extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research is evaluated.
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Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review
TL;DR: In this article, the authors evaluate the process of systematic review used in the medical sciences to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research and highlight the challenges in developing an appropriate methodology.
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Business intelligence and analytics: from big data to big impact
TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
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Critical questions for big data
danah boyd,Kate Crawford +1 more
TL;DR: The era of Big Data has begun as discussed by the authors, where diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people.
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Beyond the hype
Amir H. Gandomi,Murtaza Haider +1 more
TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.