Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective

Purpose The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field. Design/methodology/approach To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals. Findings The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data. Research limitations/implications This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research. Originality/value To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

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RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

Statistika, učet i audit ◽

10.51579/1563-2415.2021-4.16 ◽

2021 ◽

Vol 83 (4) ◽

pp. 100-111

Author(s):

Ahmad Anwar Zainuddin ◽

Keyword(s):

Big Data ◽

Internet Of Things ◽

Data Analytics ◽

Big Data Analytics ◽

Physical World ◽

Future Directions ◽

Wide Range ◽

Time Utilization ◽

Relationship Of ◽

The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

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Computational Systems Biology Perspective on Tuberculosis in Big Data Era

Web Services ◽

10.4018/978-1-5225-7501-6.ch115 ◽

2019 ◽

pp. 2230-2254

Author(s):

Amandeep Kaur Kahlon ◽

Ashok Sharma

Keyword(s):

Big Data ◽

Systems Biology ◽

Data Management ◽

High Throughput ◽

Predictive Models ◽

System Biology ◽

Prediction Models ◽

Computational Systems Biology ◽

Future Directions ◽

Computational Systems

The major concern in this chapter is to understand the need of system biology in prediction models in studying tuberculosis infection in the big data era. The overall complexity of biological phenomenon, such as biochemical, biophysical, and other molecular processes, within pathogen as well as their interaction with host is studied through system biology approaches. First, consideration is given to the necessity of prediction models integrating system biology approaches and later on for their replacement and refinement using high throughput data. Various ongoing projects, consortium, databases, and research groups involved in tuberculosis eradication are also discussed. This chapter provides a brief account of TB predictive models and their importance in system biology to study tuberculosis and host-pathogen interactions. This chapter also addresses big data resources and applications, data management, limitations, challenges, solutions, and future directions.

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Biomedical informatics meets data science: current state and future directions for interaction

JAMIA Open ◽

10.1093/jamiaopen/ooy032 ◽

2018 ◽

Vol 1 (2) ◽

pp. 136-141 ◽

Cited By ~ 2

Author(s):

Philip R O Payne ◽

Elmer V Bernstam ◽

Justin B Starren

Keyword(s):

Big Data ◽

Data Science ◽

Clinical Decision Making ◽

Social Issues ◽

Biomedical Informatics ◽

Scientific Discovery ◽

Topic Area ◽

Future Directions ◽

Current State ◽

Challenges And Opportunities

Abstract There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.

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The Criminal Law and Law Enforcement Implications of Big Data

Annual Review of Law and Social Science ◽

10.1146/annurev-lawsocsci-101317-030839 ◽

2018 ◽

Vol 14 (1) ◽

pp. 293-308 ◽

Cited By ~ 3

Author(s):

Sarah Brayne

Keyword(s):

Big Data ◽

Law Enforcement ◽

Data Collection ◽

Data Analytics ◽

Big Data Analytics ◽

Police Departments ◽

Law Enforcement Agencies ◽

Future Directions ◽

Legal Questions ◽

Legal Responses

Law enforcement agencies increasingly use big data analytics in their daily operations. This review outlines how police departments leverage big data and new surveillant technologies in patrol and investigations. It distinguishes between directed surveillance—which involves the surveillance of individuals and places under suspicion—and dragnet surveillance—which involves suspicionless, unparticularized data collection. Law enforcement's adoption of big data analytics far outpaces legal responses to the new surveillant landscape. Therefore, this review highlights open legal questions about data collection, suspicion requirements, and police discretion. It concludes by offering suggestions for future directions for researchers and practitioners.

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