Information Processing & Management is devoted to refereed reporting of:

1. Basic and applied research in information science, computer science, cognitive science and related areas that deals with: the generation, representation, organization, storage, retrieval, and use of information; the nature, manifestations, behavior, and effects of information and knowledge; communication and distribution of information and knowledge; and human information behavior.

2. Experimental and advanced processes, related to: information retrieval (IR); digital libraries; knowledge organization and distribution; digitized contents - text, image, sound and multimedia processing; and human-computer interfaces in information systems. Implementations in information retrieval systems and a variety of information systems, networks, and contexts. Related evaluation.

3. Management of information resources, services, systems and networks, and digital libraries. Related studies of the economics of information and the principles of information management.

The aim is to provide an international forum for advanced works and critical analysis in these interdependent and interdisciplinary areas. Invited are original papers and critical reviews of trends reporting on:
• Progress in the theory, principles, and procedures in information processing, particularly involving information retrieval; search engines; knowledge and distributed intelligence; information representation, classification, extraction, filtering and summarization; question answering; information navigation, browsing and visualization; and human-computer interaction in information systems.
• Research on the formal characteristics and properties of information and knowledge and the associated processes of communication among humans and between humans and machines. Includes studies of human information needs, seeking, searching, and use; and bibliometric and infometric studies of the structural and statistical properties of information artifacts.
• Modeling and evaluation of information systems performance, particularly of information retrieval systems, knowledge systems, and digital libraries. Studies of their effectiveness, efficiency, value, or impact.
• Studies in management and economics of information and information systems. Use of information for decision making and problem solving.
• Studies in information policies. Data and issues relevant to information policies on organizational, national, and international levels. Derivation and use of information indicators.

Special Issue on Cognitive-inspired Information Processing and Applications
Submission Date: 2021-06-30

 Aim and Scope:

Cognition is emerging as a new and promising methodology with the development of cognitive-inspired computing, cognitive-inspired interaction and systems, which has the potential to enable a large class of applications and has emerged with a great potential to change our life. However, recent advances on artificial intelligence (AI), fog computing, big data, and cognitive computational theory show that multidisciplinary cognitive-inspired computing still struggle with fundamental, long-standing problems, such as computational models and decision-making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology. How to enhance human cognitive performance with machine learning, common sense, natural language processing etc. are worth exploring.

The list of possible topics includes, but is not limited to:

The objective of this special issue is to bring together state-of-the-art research contributions that address these key aspects of cognitive-inspired computing and applications. Original papers describing completed and unpublished work not currently under review by any other journal/magazine/conference are solicited. Specific topics include, but not limited to, the following:

    Cognitive-inspired computing fundamentals
    Cognitive-inspired computing systems
    Cognitive-inspired computing with big data
    Cognitive-inspired intelligent interaction
    AI-assisted cognitive computing approaches
    Brain analysis for cognitive-inspired computing
    Internet of cognitive Things
    Cognitive environment, sensing and data
    Cognitive robots and agents
    Security issue in cognitive-inspired computing
    Test-bed, prototype implementation and applications

Submission Instructions:

Prospective authors should submit original manuscripts that have not appeared, nor are under consideration, in any other journal. Prospective authors are required to follow the Author’s Guide for manuscript submission to Information Processing & Management. To ensure that all manuscripts are correctly identified for inclusion in the special issue, it is important that authors select “VSI: CIPA” when they reach the “Article Type Selection / Issue selection” step in the submission process. Referees will consider originality, significance, technical soundness, clarity of exposition, and relevance to the special issue topics above.

Important Dates:

Manuscript Due: June 30, 2021
First Round of Reviews: August 30, 2021
Final Decision: October 30, 2021

Guest Editor (s):

Lead Guest Editor: Zheng Xu, Associate Professor, Shanghai University of Medicine and Health Sciences, China, zhengxu@shu.edu.cn
Neil Y. Yen, Associate Professor, University of Aizu, Fukushima, Japan, neilyyen@u-aizu.ac.jp
Vijayan Sugumaran, Professor Oakland University, USA, sugumara@oakland.edu

Special Issue on Leveraging Text and Social Analytics for Business Intelligence
Submission Date: 2021-07-30

In the current era of the modern computing world, innovations in business intelligence are most crucial to remain competitive and successful across the growing business trends. Consequently, businesses of all sizes (small and medium scale enterprises) have started investing in the next level of data analytics and business intelligence solutions. The efficient use of business intelligence methodologies extracts analyzes, and visualizes key performance indicators from the huge volume of enterprise big data. It reduces the cost and increases the speed of decision making with actionable intelligence. However, it is most critical for business organizations to make timely decisions due to numerous challenges. Some of the considerable concerns include ensuring data quality, absence of execution and training measures, unifying business intelligence among all user groups, targeting bigger needs with less human and computational resources, complex systems and software, huge infrastructure investment, and inability to deal with unstructured business data sources. Subsequently, these common business intelligence problems may sometimes make the business organization disgusted and may lose the value of their business. Though the use of business intelligence can be quite challenging initially, its potential benefits make it worth the investment.

Text analytics and social analytics are the two significant trends that greatly harness the power of business intelligence with improved business results and demonstrated business values. Text analytics enhances the business intelligence tools to identify patterns, trends, and actionable insights from the text to produce data-driven decisions for business applications. It leverages opportunities for business improvement and enables business services to adapt to client needs and expectations. Similarly, social analytics deals with collecting and interpreting statistical, digital data on how the users interact with an organization, specifically using online platforms. To the point, text and social analytics are the two primary forms of business intelligence that enable business organizations to forecast and respond to changing customer behaviors. It makes qualitative use of the business data and produces valuable outcomes through various techniques such as sentiment analysis, natural language processing, social network analytics, predictive modeling, and recommendation systems. Further, it is more determined that the appropriate use of social and text analytics can significantly empower business intelligence by actively solving its underlying challenges.

This special issue is intended to explore high-quality research articles on recent advances in text and social analytics for business intelligence applications.

Possible topics of interest include, but are not limited to:
- Recent advances in text and social analytics for business intelligence
- Data fusion using text analytics for business intelligence
- Conceptual designs and architectures of text and social analytics for business intelligence applications
- Dimensional modelling with text analytics for business applications
- Trends in social analytics for user behavior analysis and prediction
- Influence of natural language processing in business decision making
- Challenges in text and social analytics for business analytics with proactive solutions
- Advances in text and social analytics to deal with structured and unstructured business data
- Effective strategies of social analytics for social media marketing and innovations
- Text and social analytics applications for E-commerce applications
- Advanced computational analysis paradigms using text and social analytics for business intelligence