Special Issue “Big Data Analytics and Data-Driven Science”
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section “Information Systems”.
Deadline for manuscript submissions: 31 March 2017
Guest Editor
Dr. Sugam Sharma
Iowa State University, USA
Dr. Pouria Amirian
University of Oxford, UK
Special Issue Information
Over the past 10 years, the accommodation of information technology in enterprises has transformed the traditional business into a new paradigm, called business informatics. The inception of informatics has offered very robust and hi-tech solutions for data and information analysis, collection, storage and organizational management, as well as product and service delivery to the customers. Recently, technological advancements, particularly in the form of Big Data, and business informatics have resulted in the storage of enormous amounts of valuable business data in various formats. Businesses are trying to analyze this data (Big Data analytics) to help them understand their operations and markets. This swift advent has turned traditional businesses into highly robust and smart businesses that promise to deliver intelligent and highly profitable solutions. Big Data analytics are assisting businesses to more accurately predict the occurrences of events and data-driven decision making; this ensures that customer needs are met for a sustainable period of time. Additionally, the intelligent analysis of customer-related data from complex structured and unstructured business (Big) data may be useful in developing potential insights on a product’s market, pricing policies and strategies, risk management, and product and service delivery. This Special Issue intends to address the current research challenges in business informatics and seeks articles discussing Big Data and analytics in businesses from various perspectives, such as design and development of new tools and techniques, comprehensive analytics, applications, intelligent decision making, and so forth.
Topics of interest include, but not limited to:
Architecture and framework design for Big Data pipeline
Algorithmic paradigms, models, and analysis of Big Data
Big Data analytics for Smart Cities and Internet of Things
Big Data analytics solutions for data-driven decision making
Big Data analytics and associated issues and challenges
Big Data analytics and Data Lake paradigms, architectures, and models
Big Data governance, security, privacy, and trust policies
Big Data and risk management
Big Data for enterprise, government, and society
Big Data implications in enterprise models and practices
Cloud computing and Big Data analytics models and paradigms
Analytics (Descriptive, Diagnostic, Predictive and Prescriptive) as a Service
Big Data and sensitive business applications
Big Data and next generation innovations in business models
Big Data and rich and interactive visual and media analytics
Big Data economics and econometrics
Big Data and industry standards
Big Data Analytics in batch, real-time, and batch-real-time modes
Role of social media in Big Data, its uncertainty and quality issues
Evolution of Big Data and its knowledge implications
Open-source ecosystem of Big Data technologies and their pros and cons
Customization of Hadoop ecosystem for spatio-temporal data analysis
Geospatial Big Data analytics, paradigms and challenges
Knowledge development, discovery and decision making from spatio-temporal Big Data
Innovative applications of Big Data in business informatics
Innocative applications of spatio-temporal analysis in Big Data environement