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: closed (31 March 2017)

Special Issue Editors

Guest Editor
Dr. Sugam Sharma

Center for Survey Statistics and Methodology (CSSM), Iowa State University, USA
Website | E-Mail
Interests: data-driven science; Big Data; spatial data; GIS; smart home technology; health informatics; wireless sensor networks
Guest Editor
Dr. Pouria Amirian

1. Products and Innovation, Ordnance Survey of Great Britain, UK
2. The Global Health Network, University of Oxford, UK
Website | E-Mail
Interests: big data; spatio-temporal analytics; data science; spatial databases; distributed computing; GIS

Special Issue Information

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • big
  • data
  • analytics
  • visualization
  • science
  • NoSQL
  • cloud
  • IoT

Published Papers (1 paper)

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Research

Open AccessArticle Discovering and Understanding City Events with Big Data: The Case of Rome
Information 2017, 8(3), 74; doi:10.3390/info8030074
Received: 31 March 2017 / Revised: 21 June 2017 / Accepted: 25 June 2017 / Published: 27 June 2017
PDF Full-text (2866 KB) | HTML Full-text | XML Full-text
Abstract
The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in
[...] Read more.
The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists. Full article
(This article belongs to the Special Issue Big Data Analytics and Data-Driven Science)
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