Calls for Papers (special): Journal of Database Management (JDM)

Special Issue On: Age of Information in Business Intelligence

Submission Due Date

Guest Editors
Dr. Gunasekaran Manogaran [Leading Guest Editor]
Big Data Scientist, University of California, Davis, USA
Dr. Hassan Qudrat-Ullah [Co-Guest Editor]
Professor of Decision Sciences,
School of Administrative Studies, York University, Toronto, Canada
Dr. Qin Xin [Co –Guest Editor]
Full Professor of Computer Science, Faculty of Science and Technology,
University of the Faroe Islands, Faroe Islands. Denmark

In recent times, a large amount of data is generated from every organization sector, whether it is the medical, education, retails, banking, manufacturing, or government sector. Modern business organizations also produce large amounts of accounting data every day. This data is valuable and essential for business organizations, and efficient processing of this information can spike the success and performance of the organization. These generated need to be captured, processed, and analyzed effectively to enhance decision-making at the business and organizational levels. Besides, many social networking websites and machine-generated IoT sensors generated millions of data every day that need to be processed efficiently for a healthy societal environment. With improved decision-making. Since the data from all public, private, social, and government organizations are increasing, timely extraction of this relevant data information to facilitate effective decision-making is crucial. Analysis and processing of this information enable improved decision-making, which can further help to formulate and implement an effective business strategy to increase organizational performance. To facilitate accurate and precise decision-making, most organizations applied descriptive analysis that utilizes data insight using predictive and prescriptive analysis. However, this approach cannot analyze the data maturely, which affects the business significantly and processing of data information in such a business environment needs specific techniques and methodologies that empower valuable knowledge for the professional organization. Business Intelligence (BI) can efficiently address this issue and spike the success and performance of the organization with intelligent decision-making skills.

Business intelligence (BI) is a canopy term for the applications, technological skills, and data processing techniques associated with the fast data collection, integration, data accessing, and data analysis to enable the effective decision-making process. Today’s business world is highly crowded and competitive, demanding innovative database management techniques to accurately analyze data and simultaneously solve data integration and administration issues. However, the current business intelligence application faces the challenges of data analytics. This is because most organizations implement business intelligence and data analytics separately to manage performance, reducing the efficacy level of business intelligence application, particularly in articulating tactics and policies for the performance improvement of the overall organization. So, an emergent need to integrate advanced information technologies and data analytical approaches with BI implementation to address this challenge and make BI implementation to manage this dynamic business environment. Business intelligence II (BI II) emerges as a promising solution that addresses existing BI implementation challenges. BI II is an updated version of BI that includes various IT technologies, including data mining, text mining, big data analytics, machine learning algorithms, and artificial intelligence techniques. In addition to technical processes, business organizations also have administrative and managerial procedures for resource allocation, real-time tracking of assets and human resources, supply chain management, logistic, and distribution. So, there is a need for optimizing the administrative and managerial process. To address this challenge, human capital capabilities such as knowledge management (KM) and intellectual capital (IC) are integrated into business intelligence II that make decision-making more efficient.
Further research studies are still needed to link up human capital capabilities with business intelligence and require new BI II architecture to process the data in this age of information. This special issue contributes to the research article that explores methods, algorithms, and techniques related to information technologies, database management, knowledge management, and intellectual capital. We invite all the potential scholars and academician to submit their research work that include business intelligence II tactics, algorithms and methodology with architecture, result simulation, and future work direction.

Recommended Topics
• Efficient ERP system management using business intelligence II
• Business intelligence II techniques to improve decision-making in Business organization
• Extraction of valuable insight from healthcare data using BI II
• IT and IC integrated business intelligence II for analyzing and processing data information
• Towards the integration of business intelligence II tools for educational data mining
• Text mining approaches for social-media data with business intelligence II tactics
• Big data analytic based BI II approach for business data analytics
• Human intellectual based BI II for administrative and managerial process management
• BI II umbrella framework for business process management
• Information management system with intellectual capital under BI II framework
• BI II based organizational model for effective decision-making
• Research towards businesses intelligence II: Age of information
• Challenges and research opportunities in business intelligence II environment

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on [Age of Information in Business Intelligence] on or before 11/18/2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Gunasekaran Manogaran
Guest Editors
Journal of Database Management [JDM]
E-mails: [;]

Special Issue On: Energy-Efficient Machine Learning and Big Data Analytics for Data-Intensive Applications

Submission Due Date

Guest Editors
Dr. Keping Yu (Lead Guest Editor)
Waseda University, Tokyo, Japan
Dr. Wei Wang
Sun Yat-sen University, Shenzhen, China
Dr. Moayad Aloqaily
xAnalytics Inc.,
Ottawa, ON, Canada

In recent times, ranging from hospitality to manufacturing to energy applications, every industry has started adopting sensor-assisted intelligent solutions. As modern technologies such as the Internet of Things (IoT), cloud computing, fog computing, edge computing, and artificial intelligence (AI) continue to evolve, the industrial sector has started developing more and more tools and applications for their business use cases. Achieving energy efficiency remains a major concern here. This is especially true when it comes to data-intensive applications. Industrial applications such as smart energy, smart mobility, smart health, military, and defense generate a massive amount of data every second and it has to be processed with utmost efficiency and accuracy for better decision making and operation. As a result, business organizations have started looking for innovative solutions to reduce cost and energy consumption.
Energy-efficient data analytics with machine learning and big data opens up a new door to identify the sphere of inefficiency and implement energy-saving initiatives across complex data-intensive intelligent applications. The appropriate use of big data and machine learning technologies helps to address various critical issues associated with the businesses, such as quality, productivity, and efficiency. The data collected from the sensors provide us the required information to assess the current situation of the system and take relevant actions. Big data and machine learning algorithms help make predictions and inform the end-users/business professionals to make adjustments based on the situation. However, the more the amount of data, the better insights can be acquired to make predictions. Here, the major constraint is to reduce energy consumption without compromising the level of productivity and efficiency measures. Collecting qualitative data and using appropriate machine learning and big data analytics models are ways to enhance energy efficiency across data-intensive applications. This creates the necessity to normalize the machine learning and big data paradigms in terms of energy efficiency. If this idea is implemented successfully, we can easily connect to all the critical devices, people, and machines to capture valuable insights hidden inside the data and make immediate solutions to solve complex real-world problems. Furthermore, research in this background will significantly benefit various sectors such as finance, retail, government, healthcare, oil and gas, transportation, etc.

This special issue entitled “Energy-Efficient Machine Learning and Big Data Analytics for Data-Intensive Applications” brings together researchers and data scientists to discuss various energy-efficiency challenges in harnessing the complete potential of machine learning and big data models in analyzing data-intensive applications. Research works that focus on this background are most welcomed for submission.

Recommended Topics
• Effective ways through which big data and machine learning change the energy efficiency paradigm across complex applications
• Scalable big data analytics with efficient machine learning algorithms
• Distributed machine learning for complex intelligent systems in the context of energy efficiency
• Energy efficient predictive analytics with big data
• Trends in in-memory computing for big data analytics and machine learning
• Advances in bio-inspired and neuromorphic computing
• Emerging computing methodologies and algorithms for energy-efficient big data and machine learning
• Reconfigurable hardware computing in the aspect of energy efficient big data and machine learning models for data-intensive applications
• Metadata management and energy efficiency computing
• Machine learning and big data assisted data visualization methods for data intensive applications
• Scalable and/or descriptive analytics algorithms
• Big data frameworks and architectures in the view of energy efficiency

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Energy-Efficient Machine Learning and Big Data Analytics for Data-Intensive Applications on or before1/25/20222. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Keping Yu
Guest Editors

Journal of Database Management (JDM)