Special Issue "Big Data Analytics and Data-Driven Science"
Deadline for manuscript submissions: closed (31 March 2017)
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
Dr. Sugam Sharma
Dr. Pouria Amirian
Manuscript Submission Information
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