Management and Processing of Complex Data Structures


Book Description

This volume presents the proceedings of the third workshop on Information Systems and Artificial Intelligence, organized by the German Computer Science Society. The 11 invited contributions by well known researchers and developers working in the fields of databases and knowledge representation systems are centered around the topic of management and processing of complex data structures; they give a representative snapshot of the state-of-the-art in this fruitful interdisciplinary research area important for further progress in both, information systems and artificial intelligence. Most of the papers stress the demands for new or extended formalisms and their deductive capabilities, including an analysis of their formal properties for managing complex structures.







Processing and Managing Complex Data for Decision Support


Book Description

"This book provides an overall view of the emerging field of complex data processing, highlighting the similarities between the different data, issues and approaches"--Provided by publisher.




Data Stream Management


Book Description

This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management.




Data Structures with Go


Book Description

Welcome to "Data Structures with Go: A Comprehensive Guide," your gateway to mastering data structures using the Go programming language. In today’s fast-paced software development world, a solid grasp of data structures is essential for creating efficient, scalable, and high-performance applications. This book provides a thorough exploration of data structures through Go, a language known for its simplicity, performance, and robust concurrency support. Why This Book? Data structures are fundamental to computer science and software engineering. They determine how data is organized, stored, and manipulated, significantly impacting the performance and efficiency of algorithms. With Go’s growing popularity for its clean syntax and effective concurrency model, it is an excellent choice for learning and implementing data structures. This book leverages Go’s features to offer practical insights into data structures, making it a valuable resource for developers of all skill levels. What You Will Learn Fundamentals of Data Structures: The book starts with an introduction to data structures, highlighting their importance and role in software development. You’ll explore basic data types in Go and their applications in various data structures. Arrays and Slices: Delve into arrays and slices, foundational structures in Go. Learn how to declare, initialize, and manipulate them, and understand their performance implications and practical uses. Linked Lists: Explore singly and doubly linked lists, including their structures, operations, and Go implementations. Understand how linked lists compare to arrays and slices and their advantages and limitations. Stacks and Queues: Study these essential linear data structures. Learn about stack (LIFO) and queue (FIFO) operations and their implementations in Go. The chapter also covers variants like deques and priority queues. Trees: Understand hierarchical data structures such as binary trees, binary search trees (BST), AVL trees, and Red-Black trees. Learn about tree operations, traversal techniques, and their Go implementations. Graphs: Learn about graph representations, including adjacency matrices and adjacency lists, and explore directed and undirected graphs. This chapter also covers common algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS). Hashing: Discover hashing techniques, hash tables, and collision handling strategies. Implement hash tables in Go and understand their practical applications. Advanced Data Structures: Dive into specialized data structures such as heaps, tries, suffix trees, and Bloom filters. Learn about their implementations and use cases. Algorithms and Data Structures in Practice: Apply data structures to real-world problems. This chapter focuses on sorting and searching algorithms, optimization techniques, and performance profiling in Go. Real-World Applications: Explore how data structures are used in practical projects. Study case studies, best practices, and design patterns for implementing data structures in Go-based systems. Who Should Read This Book? This book caters to: Beginners: Those new to Go or data structures will find a clear, structured introduction. Intermediate Developers: Readers with some experience can deepen their knowledge and tackle advanced topics. Experienced Professionals: Those looking to explore Go or stay updated with modern practices will find valuable insights and practical examples. Learning Approach Emphasizing hands-on learning, the book includes practical examples, exercises, and real-world case studies to reinforce understanding and encourage experimentation. By working through these exercises, you will gain practical experience and a deeper grasp of data structures in Go. "Data Structures with Go: A Comprehensive Guide" is your key to mastering essential computer science principles and applying them effectively in modern applications. Dive in and discover how Go can enhance your skills in building robust, efficient, and scalable systems. Aditya




Complex Data Analytics with Formal Concept Analysis


Book Description

FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining. Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge. This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.




NBS Special Publication


Book Description




Handbook of Data Structures and Applications


Book Description

The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.




Real-Time & Stream Data Management


Book Description

While traditional databases excel at complex queries over historical data, they are inherently pull-based and therefore ill-equipped to push new information to clients. Systems for data stream management and processing, on the other hand, are natively pushoriented and thus facilitate reactive behavior. However, they do not retain data indefinitely and are therefore not able to answer historical queries. The book provides an overview over the different (push-based) mechanisms for data retrieval in each system class and the semantic differences between them. It also provides a comprehensive overview over the current state of the art in real-time databases. It sfirst includes an in-depth system survey of today's real-time databases: Firebase, Meteor, RethinkDB, Parse, Baqend, and others. Second, the high-level classification scheme illustrated above provides a gentle introduction into the system space of data management: Abstracting from the extreme system diversity in this field, it helps readers build a mental model of the available options.