Название: Demystifying Graph Data Science: Graph algorithms, analytics methods, platforms, databases, and use casesАвтор: Pethuru Raj, Abhishek Kumar, Vicente García Díaz
Издательство: The Institution of Engineering and Technology
Год: 2022
Страниц: 415
Язык: английский
Формат: pdf (true), epub
Размер: 32.8 MB
With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive business innovations and disruptions to bring in real digital transformation. Data Science (DS) is proving to be the one-stop solution for simplifying the process of knowledge discovery and dissemination out of massive amounts of multi-structured data.
Supported by query languages, databases, algorithms, platforms, analytics methods and Machine and Deep Learning (ML and DL) algorithms, graphs are now emerging as a new data structure for optimally representing a variety of data and their intimate relationships.
Compared to traditional analytics methods, the connectedness of data points in graph analytics facilitates the identification of clusters of related data points based on levels of influence, association, interaction frequency and probability. Graph analytics is being empowered through a host of path-breaking analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book aims to explain the various aspects and importance of graph Data Science. The authors from both academia and industry cover algorithms, analytics methods, platforms and databases that are intrinsically capable of creating business value by intelligently leveraging connected data.
The pace of data analytics is gaining the much-needed speed and sagacity with the continuous contributions of product and tool vendors. Data science is the domain increasingly associated with data analytics. There are big, fast and streaming data analytics platforms, frameworks, accelerators, toolkits, etc. for making data analytics simpler, faster and affordable.
In the Big Data world, NoSQL and distributed SQL databases gained the market and mind shares fast. Graph databases are one of the prominent NoSQL databases. Data representation through graphs has laid down a stimulating foundation to visualize and realize a stream of fresh capabilities. On the other hand, the analytical competency is significantly improved through the faster maturity and stability of Artificial Intelligence (AI) algorithms [machine and deep learning (ML/DL)]. Thus, the classical and current data science paradigm is substantially advanced to have sophisticated abilities through the direct and distinct empowerment of AI algorithms. There is a twist now. Applying the AI-inspired data science methods on graph-structured data is being seen as a clear-cut gamechanger for the digital world. Extracting hidden patterns, useful associations, impending risks, future opportunities, and other useful and usable insights out of data heaps through data science platforms, frameworks, and engines is the new normal. Especially data science on graph data is acquiring special significance as there is a solid understanding that the blending of graphs and data science techniques can bring in a lot of noteworthy innovations and transformations.
This book will be a valuable reference for ICTs industry and academic researchers, scientists and engineers, and lecturers and advanced students in the fields of data analytics, data science, cloud/fog/edge architecture, internet of things, Artificial Intelligence, Machine and Deep Learning, and related fields of applications. It will also be of interest to analytics professionals in industry and IT operations teams.
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