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Manuscript deadline
31 July 2021

Cover image - Knowledge Management Research & Practice

Knowledge Management Research & Practice

Special Issue Editor(s)

Ali Kashif Bashir, Manchester Metropolitan University, UK
[email protected]

M. Omair Shafiq, Carleton University, Canada
[email protected]

Nawab Muhammad Faseeh Qureshi, Sungkyunkwan University, South Korea
[email protected]

Muhammad Shafiq, Guangzhou University, China
[email protected]

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Advances in Business Analytics and Knowledge Management

Researchers today are actively involved in business-analytics and knowledge management systems to address complex business problems efficiently. It is an interdisciplinary research area that covers various aspects of business analytics, knowledge management systems, quantitative methods, and operation management systems. In general, business analytics makes use of advanced computational models and mathematical algorithms to acquire reasonable knowledge and assist in the valuable business-related decision-making processes. When used appropriately, this methodology reduces business risks and offers significant advantages to business organizations. Thus, the integration of business analytics and knowledge management systems enhances essential components of the business processes with improved performance, customer satisfaction, revenue opportunities, and reduced management risks. However, achieving this objective is highly complex as the present-day digital applications are data-intensive in nature and may subject to grow more exponentially in the upcoming days.

Currently, the data required for business-analytics processes are acquired through various means of data sources such as user mobile phones, personal computers, social media applications, Internet of Things (IoT) devices, and many more. For any information system, it is necessary to transfer the data into valuable information through a series of complex practices such as data capturing, aggregation, analysis, and storage.  The processed data forms the key component of knowledge systems, and it is then used for business decision making processes and developing efficient business management strategies. However, with the growing demands of big data, this process is often tricky with conventional database systems, and it gives rise to the need for incorporating advanced knowledge management practices across the business analytics processes. The data is stored in such a way that each record should be put in storage with its description and its association with the other data contents. Through this process, unrelated information’s concerning the particular entity can be easily managed, and it assists in effective business-analytics processes. As stated earlier, the conventional methods of business analytics processes do not scale well with the growing revolution of big data applications. Hence, bringing in the advances of knowledge management towards business analytics can better solve these issues. Against this background, this special issue aims to explore various aspects of the knowledge management for business analytics to meet the demands of future generation big data applications.  List of topics for the special issue include, but not, limited to the following:

  • Advances in business-analytics with intelligent knowledge management paradigms
  • New trends in ontology and knowledge representation models for big data
  • Recent technologies for knowledge sharing
  • Cognitive knowledge and business analytics
  • Role of knowledge management in enterprise transformation
  • Frontiers in knowledge management for risk assessment in business applications
  • Advances in semantic integration using knowledge management
  • Knowledge management for business analytics in practice and case studies
  • Big data driven knowledge management technologies for business-analytics
  • Recent trends in tactical knowledge capture and dissemination methods
  • Advanced knowledge management and representation methodologies for business analytics

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Submission Instructions

Submission timeline:

  • Deadline of Submission: 31.07.2021
  • Author Notification: 30.09.2021
  • Deadline for Revised Papers: 31.12.2021
  • Final Acceptance: 01.03.2022

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