Abstract
It is my great pleasure and honor to assume the role of Editor-in-Chief of the Big Data journal (BDJ). From its inception 6 years ago, a highly esteemed BDJ editorial team has been devoted to publishing quality articles addressing challenges related to organizing, storing, disseminating, protecting, manipulating, and making sense of rapidly growing structured and unstructured data.
Within a relatively short period of time under the great leadership of the previous Editor-in-Chief, Prof. Vasant Dhar, BDJ has received an impact factor reflecting the importance of the journal within the field. Vasant has provided a clear engaging vision, along with extensive time and effort, to build strong foundations for a reputable journal covering a wide range of important big data topics. He also helped me a lot in the transition of Editor-in-Chief responsibilities, for which I thank him from the bottom of my heart.
Continuing the path of excellence established by Vasant and the editorial team is my main objective as the next Editor-in-Chief for BDJ. In my new role, I will dedicate my time to further develop the publishing reputation of the BDJ. Our goal is to provide a quick response to authors of submitted articles and rigorous peer reviews. We plan to increase BDJ visibility and to enlarge its scope by working closely with organizers of relevant conferences and workshops to include additional topics of big data science and foundations, big data infrastructure, big data management, big data retrieval, advanced analytics for big data, big data security, privacy and trust, and a variety of high-impact big data applications. We will solicit high-quality reviews, short communications, letters, and original research articles addressing multiple aspects of the big data field, including development of novel and effective methods for handling volume, velocity, variety, value, and veracity of big data. In addition, we will publish articles describing systems that integrate and deploy existing tools to make new discoveries in challenging applications. We will also welcome proposals for development of special issues on emerging big data topics.
Toward this objective, this BDJ issue includes an interview, a survey, and five research articles discussing a range of topics related to effective integration, analysis, and visualization of streaming data.
An interview with Bart Baesens on his book, “Principles of Database Management,” is aimed to highlight a new practical guide for storing, managing, and analyzing big and small data. In this interview, one of the authors describes the scope and intended target audience of the book and provides links to supplemental materials, including YouTube lectures and PowerPoint slides.
A survey article on “How heterogeneity affects the design of Hadoop MapReduce schedulers” by Vaibhav Pandey and Poonam Saini provides a comparative analysis of various Hadoop scheduling schemes classified based on heterogeneity factors.
In “A data snapshot approach for making real-time predictions in basketball,” Varol Onur Kayhan and Alison Watkins propose a practical approach to computing live probabilities of winning for home and away teams during a basketball game based on the characteristics of play.
Populations of automated user accounts in online social networks, called bots, are studied by Andrej Duh, Marjan Slak Rupnik, and Dean Korosak in an article entitled “Collective Behaviour of Social Bots is Encoded in Theory Temporal Twitter Activity.” Using and analyzing Twitter data related to the UK EU referendum, they found that collective behavior in a population of bots is statistically different from populations of humans, although both groups exhibit properties similar to those found in social and biological systems placed near a critical point.
An infrastructure to support distributed storage and fast analysis of big heterogeneous documents is described by Pablo Basanta-Val and Luis Sanchez-Fernandez in an article entitled “Big-BOE: Fusing Spanish official gazette with big-data technology.”
A flexible and interactive online framework to visualize news data over time is proposed by Emilio Carrizosa, Vases Guerrero, Daniel Hards, and Dolores Romero Morales in an article entitled “On building online visualization maps for news data streams by means of mathematical optimization.”
Finally, in “Deep learning method for DoS attack detection based on Restricted Boltzmann Machine,” Yadigar Imamverdiyev and Fargana Abdullayeva describe a real-time deep neural networks-based method for accurate and adaptive detection of malicious activities in a computer network.
I hope that the articles published in this issue will inspire further interest in transforming the world through big data research. I also invite you to submit your potentially transformative research for BDJ consideration.