Special Issue "Big-Data Driven Multi-Criteria Decision-Making"

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (28 February 2019)

Special Issue Editors

Guest Editor
Prof. AMM Sharif Ullah

Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, Kitami, Japan
Website | E-Mail
Interests: Design, Manufacturing, Artificial Intelligence, Engineering Education
Guest Editor
Dr. Md. Noor-E-Alam

Department of Mechanical and Industrial Engineering, Northeastern University, Boston 02115, MA, USA
Website | E-Mail
Phone: +1- 617-373-2275
Interests: big-data analytics; operations research; large-scale optimization; healthcare analytics and manufacturing systems

Special Issue Information

Dear Colleagues,

Governmental agencies, independent organizations, educational institutes, and business entities around the globe relentlessly carryout experimentations, surveys, and analyses on numerous issues. As a result, a huge amount of intellectual resources (data, information, and/or knowledge) has been accumulated and stored in numerical, textual, graphical, audio, and video forms. In general, when we link and access such intellectual resources through the Internet, we call it “big data.” Nowadays, we have big data for such sectors as agriculture, health, local government, climate, ecosystems, consumers, manufacturing, energy, marine, public safety, finance, and scientific research. The fact of the matter is that big data will serve as a source of decision-relevant information while making both formal and informal decisions in all sectors mentioned above and beyond. In this Special Issue, we invite our colleagues to publish original articles, reviews, and technical notes related to the following topics (but not limited to them):

  • Big data driven multi-criteria decision formulation
  • Big data driven sector-centric decision making
  • Big data driven decision computation (e.g., new aggregation function and alternative ranking process)
  • Influence of different forms (e.g., numerical, textual, and graphical) of big data in the decision making process
  • Multi-agent decision making using big data
  • Uncertainty management for big data driven decision making
  • New apps and tools for multi-criteria decision making using big data
  • Usages of big data in making decisions for Industry 4.0/Society 5.0
  • Integration of sector-wise big data for decision making
  • Artificial Intelligence and/or computational intelligence for big data driven decision making
  • Problems related to big data driven decision making

Dr. AMM Sharif Ullah
Dr. Md. Noor-E-Alam
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big-data
  • Sector-driven big-data
  • forms of big-data
  • uncertainty
  • artificial intelligence
  • formal decision making
  • informal decision making
  • decision-relevant information
  • granular information
  • multi-criteria decision making

Published Papers (2 papers)

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Research

Open AccessArticle
Intelligent Recommender System for Big Data Applications Based on the Random Neural Network
Big Data Cogn. Comput. 2019, 3(1), 15; https://doi.org/10.3390/bdcc3010015
Received: 26 December 2018 / Revised: 10 February 2019 / Accepted: 15 February 2019 / Published: 18 February 2019
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Abstract
Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data [...] Read more.
Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer. Full article
(This article belongs to the Special Issue Big-Data Driven Multi-Criteria Decision-Making)
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Open AccessArticle
Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA)
Big Data Cogn. Comput. 2018, 2(3), 27; https://doi.org/10.3390/bdcc2030027
Received: 29 July 2018 / Revised: 15 August 2018 / Accepted: 22 August 2018 / Published: 3 September 2018
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Abstract
Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an [...] Read more.
Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an analytic network process (ANP) and data envelopment analysis (DEA) approach to tackling productivity measurement and benchmarking problems in a manufacturing environment. The ANP was used to capture the interdependency between the criteria taking into consideration the ambiguity and vagueness. The nonparametric DEA approach was utilized to determine the input-oriented constant returns to scale (CRS) efficiency of different value-adding production units and to benchmark them. The proposed framework was implemented to benchmark the productivity of an apparel manufacturing company. By applying the model, industrial managers can gain benefits by identifying the possible contributing factors that play an important role in increasing the productivity of manufacturing organizations. Full article
(This article belongs to the Special Issue Big-Data Driven Multi-Criteria Decision-Making)
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