Development Methodologies For Big Data Analytics Systems
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Development Methodologies for Big Data Analytics Systems
This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches – 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data – and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.
Big Data Analytics: Systems, Algorithms, Applications
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Marketing and Big Data Analytics in Tourism and Events
In the digital age, the tourism industry faces the challenge of effectively marketing destinations amidst a sea of competition and information. Marketing Information Systems (MkIS) and Big Data Analytics (BDA) hold immense potential. Yet, many organizations need help harnessing their power efficiently. Marketing and Big Data Analytics in Tourism and Events offer a comprehensive solution, deep-dive into integrating MkIS and BDA as a strategic approach to revolutionizing tourism marketing. The book aims to bridge the gap between theory and practice by examining the complexities and nuances of MkIS and BDA in promoting tourist destinations. It provides actionable insights and practical strategies for leveraging these technologies effectively. Readers will understand how AI-driven MkIS and BDA can enhance marketing campaigns, improve customer experiences, and drive business growth in the tourism sector.