Data Science and Big Data Analytics

Complete Summary of the Book

Publication and Editorial Information

Title: Data Science and Big Data Analytics
Subtitle / Conference: Proceedings of ACM–WIR 2018
Editors: Durgesh Kumar Mishra, Xin-She Yang, and Aynur Unal
Publisher: Springer Nature Singapore Pte Ltd.
Series: Lecture Notes on Data Engineering and Communications Technologies, Volume 16
Year of Publication: 2019
ISBN: 978-981-10-7640-4 (Print) / 978-981-10-7641-1 (eBook)
Resale Rights: Big Data Consult (www.bigdataconsult.fr)

Enhanced Overview

Data Science and Big Data Analytics (ACM–WIR 2018) is an edited volume comprising a selection of high-quality research papers presented at the ACM International Workshop on Internet and Research (WIR 2018). Published as part of Springer’s Lecture Notes on Data Engineering and Communications Technologies series, this book explores the latest methodologies, architectures, and applications shaping the fields of data science, artificial intelligence, and large-scale data systems.

The publication reflects the rapid expansion of the data-driven economy, emphasizing the transformation of information into strategic insights that fuel decision-making, innovation, and industrial competitiveness. It provides a multidisciplinary perspective bridging computer science, engineering, healthcare, economics, and social systems.

Main Themes and Contributions

1. Big Data Technologies and Infrastructure

The volume presents architectures and frameworks that enable distributed, scalable, and fault-tolerant data systems. It discusses modern paradigms for managing massive datasets across heterogeneous environments, addressing challenges such as interoperability, latency reduction, and efficient storage.

2. Machine Learning and Predictive Analytics

A key section focuses on advanced analytical methods integrating machine learning, data mining, and statistical modeling.
Highlighted contributions include:

• Novel Outlier Detection through combined clustering and classification techniques.

• Unsupervised Learning Models for detecting plant diseases and environmental anomalies.

• Automated Workload Optimization leveraging predictive algorithms to enhance computational efficiency.

3. Healthcare and Bioinformatics

The book dedicates a strong segment to Big Data in healthcare, illustrating how predictive analytics and IoT-based monitoring systems are revolutionizing patient care.
Case studies include:

• Predictive models for epileptic seizure and cervical cancer detection.

• Real-time data collection via IoT-based healthcare frameworks.

• Evaluation of Big Data’s contribution to public health infrastructure in India.

4. Networking, Cloud, and Communication Systems

Research papers in this area address the optimization of data flow and network reliability, featuring:

• Congestion control algorithms for Mobile Ad Hoc Networks (MANETs).

• Traffic sharing protocols for internet scalability.

• Quality of Service (QoS) management in cloud computing environments.

5. Data Security and Privacy

Given the sensitivity of data-intensive systems, several papers focus on secure computation and privacy preservation:

• Anonymization techniques for electronic health records (EHRs).

• Homomorphic encryption for ensuring confidentiality during computation.
  These studies highlight the tension between data utility and user privacy, proposing balanced frameworks for ethical data governance.

6. Industrial and Real-Time Applications

The final section bridges theory and practice through industry-oriented applications, demonstrating how data analytics supports lean manufacturing, process optimization, and real-time operational intelligence.

Scholarly and Practical Relevance

This volume is particularly valuable for:

• Researchers and graduate students in data science, AI, and communication systems.

• Engineers and practitioners developing Big Data infrastructures or AI-driven decision systems.

• Policy makers and industry leaders exploring data-based innovation strategies.

It stands out by integrating academic rigor with applied research outcomes, offering a panoramic view of how data analytics reshapes modern industries and societies.

Summary Conclusion

Data Science and Big Data Analytics (ACM–WIR 2018) encapsulates the state of the art in data-centric research. Through its exploration of analytics, machine learning, and system design, the book underscores how data has become a transformative force across disciplines.
It not only showcases technological progress but also fosters a holistic understanding of how intelligent data systems can enhance efficiency, resilience, and decision intelligence in the digital era.