Taking the Pulse of Financial Activities with Online Graph Processing

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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HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming

2017 21st International Conference Information Visualisation (IV) ◽

10.1109/iv.2017.45 ◽

2017 ◽

Cited By ~ 3

Author(s):

Alexandre Perrot ◽

Romain Bourqui ◽

Nicolas Hanusse ◽

David Auber

Keyword(s):

Big Data ◽

High Throughput ◽

Low Latency

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Computational Systems Biology Perspective on Tuberculosis in Big Data Era

Big Data Analytics in Bioinformatics and Healthcare - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-6611-5.ch011 ◽

2015 ◽

pp. 240-264 ◽

Cited By ~ 1

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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Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis

Applied Energy ◽

10.1016/j.apenergy.2020.116429 ◽

2021 ◽

Vol 285 ◽

pp. 116429

Author(s):

Wen-Long Shang ◽

Jinyu Chen ◽

Huibo Bi ◽

Yi Sui ◽

Yanyan Chen ◽

...

Keyword(s):

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Environmental Benefits ◽

User Behaviors ◽

Bike Sharing

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Accelerating organic solar cell material's discovery: high-throughput screening and big data

Energy & Environmental Science ◽

10.1039/d1ee00559f ◽

2021 ◽

Author(s):

Xabier Rodríguez-Martínez ◽

Enrique Pascual-San-José ◽

Mariano Campoy-Quiles

Keyword(s):

Machine Learning ◽

Big Data ◽

High Throughput ◽

Organic Solar Cells ◽

High Throughput Screening ◽

Organic Solar Cell ◽

State Of The Art ◽

Review Article ◽

Machine Learning Algorithms ◽

Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

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A bibliographic study on big data: concepts, trends and challenges

Business Process Management Journal ◽

10.1108/bpmj-10-2015-0149 ◽

2017 ◽

Vol 23 (3) ◽

pp. 555-573 ◽

Cited By ~ 25

Author(s):

Deepa Mishra ◽

Zongwei Luo ◽

Shan Jiang ◽

Thanos Papadopoulos ◽

Rameshwar Dubey

Keyword(s):

Big Data ◽

Citation Analysis ◽

Design Methodology ◽

Future Directions ◽

Content Type ◽

Current Trends ◽

Bibliographic Study

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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