Large Scale Spatial Data Management On Modern Parallel And Distributed Platforms

Large-Scale Spatial Data Management on Modern Parallel and Distributed Platforms

Model and Data Engineering

This book constitutes the refereed proceedings of the 10th International Conference on Model and Data Engineering, MEDI 2021, held in Tallinn, Estonia, in June 2021. The 16 full papers and 8 short papers presented in this book were carefully reviewed and selected from 47 submissions. Additionally, the volume includes 3 abstracts of invited talks. The papers cover broad research areas on both theoretical, systems and practical aspects. Some papers include mining complex databases, concurrent systems, machine learning, swarm optimization, query processing, semantic web, graph databases, formal methods, model-driven engineering, blockchain, cyber physical systems, IoT applications, and smart systems. Due to the Corona pandemic the conference was held virtually.

Building Scalable Data-Intensive Applications

Building Scalable Data-Intensive Applications explores the vast landscape of digital data from social networks, blogs, business, science, and engineering. This book delves into data-intensive computing, which is essential for understanding and processing massive amounts of data. Utilizing the latest software, algorithms, and hardware, data-intensive applications deliver timely and meaningful insights, addressing the challenges posed by exponentially growing data complexity. We provide a comprehensive reference for computing professionals and researchers, covering the field's scope, key challenges, and state-of-the-art approaches required for future data-intensive problems. Our chapters include general principles and methods for designing and managing systems that analyze vast datasets, particularly those stored in the cloud. Additionally, we explore practical applications in cybersecurity and bioinformatics to illustrate these principles in action. Building Scalable Data-Intensive Applications is an invaluable resource for anyone looking to navigate and harness the power of data-intensive computing.