for Journals by Title or ISSN
for Articles by Keywords
help
Followed Journals
Journal you Follow: 0
 
Sign Up to follow journals, search in your chosen journals and, optionally, receive Email Alerts when new issues of your Followed Journals are published.
Already have an account? Sign In to see the journals you follow.
Similar Journals
Journal Cover
Journal of Data, Information and Management
Number of Followers: 0  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 2524-6356 - ISSN (Online) 2524-6364
Published by Springer-Verlag Homepage  [2574 journals]
  • Panacea of challenges in real-world application of big data analytics in
           healthcare sector
    • Abstract: Big data analytics is emerging ever since it has been introduced in the healthcare sector. It has given tools to gather, operate, assess, and associate large volumes of disparate, structured and unstructured data that are generated by present healthcare systems. Big data has been lately functional towards helping in the process of care delivery and disease exploration. Howbeit, due to some fundamental problems, the progress in care delivery and disease exploration is blocked. Fundamental problems such as data cleaning, capturing, security and privacy, storage, and how data is visualized hinder the expansion of big data in the healthcare sector. In this paper, we discuss these challenges, methods used to overcome these challenges, and results obtained. Based on the obtained results; the conclusion has been drawn to keep advancing in the healthcare sector.
      PubDate: 2019-10-15
       
  • Reducing data transfer in big-data workflows: the computation-flow
           delegated approach
    • Abstract: Existing orchestrated bioinformatics workflow execution approaches necessitate the transfer of datasets from biological data services to the analysis tool (computation) services of the workflow for various data analysis. This model of moving data to computation during workflow execution weakens the performance of the workflow especially when the orchestrated bioinformatics workflow has to handle big-data in it. Since the size of the analysis tools are much smaller than the datasets size in a workflow, in this paper, to minimize the dataflow and improve workflow performance, we propose a novel computation-flow delegated (CFD) approach. The CFD approach lets the tool services of the workflow to dynamically migrate analysis tools towards the datasets to perform computation on data side during workflow execution. We use a set of mobile agents to operate the CFD approach and present a mobile agent-based computation-flow delegation framework (MABCFD) to execute the workflow tasks. We implement the prototype of the MABCFD framework and analyze the performance of the CFD approach empirically by executing in isolation workflow patterns (sequence, fan-out and fan-in) common to bioinformatics applications. Performance analysis shows that the computation-driven CFD approach consistently outperforms the existing data-driven approaches across all patterns and scales favorably with data size.
      PubDate: 2019-10-09
       
  • Multiportfolio optimization with CVaR risk measure
    • Abstract: In the financial industry, the trading of multiple portfolios is usually aggregated and optimized simultaneously. When multiple portfolios are managed together, unique issues such as market impact costs must be dealt with properly. Conditional Value-at-Risk (CVaR) is a coherent risk measure with the computationally friendly feature of convexity. In this study, we propose the new combination of CVaR with the multiportfolio optimization (MPO) problem and develop optimization models with using CVaR to measure risks in MPO problems. A five-step scheme is presented for practical operations with considering the impact costs caused by aggregating the trading of multiple portfolios. The impact of CVaR on returns and utility in MPO environment is studied, and the comparisons with existing methods and sensitivity analysis are reported.
      PubDate: 2019-08-29
       
  • A risk-averse multi-item inventory problem with uncertain demand
    • Abstract: The observed values of demands in real-life inventory problems are sometimes imprecise due to the lack of information and historical data, thus a growing research is committed to study the properties of risk measures in fuzzy inventory optimization problems. In this paper, a risk-averse fuzzy optimization method is adopted for the multi-item inventory problem, in which the demands are described by common possibility distributions. Firstly, three classes of fuzzy inventory optimization models are built by combining the absolute semi-deviation with expected value operator and then model analysis is given for the min-max inventory models. To make the inventory problem tractable and computable, the equivalent forms of the proposed optimization models are discussed. Subsequently, several useful absolute semi-deviation formulas are presented under triangular, trapezoidal and Erlang possibility distributions. Finally, some numerical experiments are performed to highlight the modeling idea, and the computational results demonstrate the effectiveness of the solution method.
      PubDate: 2019-08-07
       
  • Editorial
    • PubDate: 2019-05-01
       
  • The application of big data analytics in optimizing logistics: a
           developmental perspective review
    • Abstract: This paper adopts a developmental perspective to review articles and reports published in the past decade on the application of big data to the optimization of logistics. First, the evolution and features of both logistics and big data are reviewed using the systematic review method. This is followed by discussions on the implementation of big data in logistics and the optimization outcomes. The paper summarizes the four main effects of the adoption of big data in logistics: informatization; operation efficiency; service quality; and the promotion of technical upgrading.
      PubDate: 2019-05-01
       
  • A Bibliometrics analysis on big data research (2009–2018)
    • Abstract: At present, the concepts, technologies and methods of big data are constantly spreading to all areas of the society. The arrival of big data has brought about tremendous changes in many areas of the people’s social life, and at the same time, it has caused profound changes in the development of society. This paper uses the bibliometric analysis and the visual analysis methods to systematically study and analyze the big data publications included in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) databases. On the one hand, it analyzes the most influential countries, journals, research institutions. On the other hand, the co-occurrence of author keywords of the publications are investigated, and the current research hotspots and future development trends are explored. The research in this paper is helpful for relevant scholars to understand the development status and trends in this field.
      PubDate: 2019-05-01
       
  • A hesitant fuzzy multi-criteria group decision making method for college
           applicants’ learning potential evaluation
    • Abstract: The evaluation of applicants’ learning potential is important to college admission process. This paper develops a multi-criteria decision making method for a comprehensive evaluation of high school graduates’ learning potential in college, in which both entrance examination marks and expert remarks are considered in the indicator system. Experts’ opinions towards indicator importance are expressed by hesitant fuzzy numbers. By using hesitant fuzzy linguistic judgments, the flexibility of expressions is increased. A minimized divergence and hesitant degree model is established to calculate the experts’ weights. Then to determine indicators’ weights, a weighted average operator is applied. To aggregate the final evaluation results, a TOPSIS method is adopted. The proposed methodology is applied to evaluate students’ learning potential in one of the top universities in China. Ten years’ real enrollment data is collected in Business and Economic School. Based on the evaluation results, some suggestions are given for the admission process.
      PubDate: 2019-05-01
       
  • Retail supply chain management: a review of theories and practices
    • Abstract: Retail business has been rapidly evolving in the past decades with the boom of internet, mobile technologies and most importantly e-commerce. Supply chain management, as a core part of retail business, has also gone through significant changes with new business scenarios and more advanced technologies in both algorithm design and computation power. In this review, we focus on several core components of supply chain management, i.e. vendor management, demand forecasting, inventory management and order fulfillment. We will discuss the key innovations from both academia and industry and highlight the current trend and future challenges.
      PubDate: 2019-05-01
       
  • A survey of type-2 fuzzy aggregation and application for multiple criteria
           decision making
    • Abstract: Type-2 fuzzy sets (T2FSs) exhibit evident merits when it comes to representing the complex and high uncertainty, which has been encountered in fuzzy optimization and multiple criteria decision making (MCDM) problems. Since T2FSs theory was proposed by Zadeh in 1975, then a series of theories and methods were investigated by more scholars. Type-2 fuzzy aggregation operators, contributing to the fundamental information fusion theory, have been paid more attention and applied to different areas during the last two decades. In this paper, a survey of type-2 fuzzy aggregation and application for MCDM is carried out. We first review some basic knowledge including definitions, operations, type reduction and ranking methods of T2FSs. Then the definitions and properties of some main aggregation operators are introduced. Furthermore, some application categories under type-2 fuzzy environment are given. Finally, we identify some existing shortcomings and point at future research directions on this topic.
      PubDate: 2019-05-01
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 35.173.234.140
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-