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Books in Data structures

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1-10 of 13 results in All results

Data Science in the Medical Field

  • 1st Edition
  • September 27, 2024
  • Seifedine Kadry + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 0 2 8 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 0 2 9 - 4
Data Science in the Medical Field focuses on the potential tools that can be used in data science to identify signs of illness at extremely early stages. Today's rapid advancements in data science provide the potential to influence and improve fundamental services in the healthcare sector, including many important applications across the healthcare spectrum. As every human body produces two terabytes of data each day, including brain activity, stress level, heart rate, blood sugar level, and many other data points, this book discusses how data science can help clinicians and researchers handle the massive volume of data to better track patient health.

Synthetic Data and Generative AI

  • 1st Edition
  • January 9, 2024
  • Vincent Granville
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 8 5 7 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 8 5 6 - 9
Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

Data Analytics for Social Microblogging Platforms

  • 1st Edition
  • November 4, 2022
  • Soumi Dutta + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 1 7 8 5 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 7 2 3 0 - 7
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Data Simplification

  • 1st Edition
  • March 9, 2016
  • Jules J. Berman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 7 8 1 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 8 5 4 - 3
Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

Data Mapping for Data Warehouse Design

  • 1st Edition
  • December 8, 2015
  • Qamar Shahbaz
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 5 1 8 5 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 5 3 3 5 - 5
Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.

Repurposing Legacy Data

  • 1st Edition
  • March 13, 2015
  • Jules J. Berman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 8 8 2 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 9 1 5 - 2
Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data—data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

Principles of Artificial Intelligence

  • 1st Edition
  • June 28, 2014
  • Nils J. Nilsson
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 5 8 6 - 2
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

Architecture and Patterns for IT Service Management, Resource Planning, and Governance

  • 2nd Edition
  • September 23, 2011
  • Charles T. Betz
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 3 8 5 0 1 7 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 3 8 5 0 1 8 - 8
Information technology supports efficient operations, enterprise integration, and seamless value delivery, yet itself is too often inefficient, un-integrated, and of unclear value. This completely rewritten version of the bestselling Architecture and Patterns for IT Service Management, Resource Planning and Governance retains the original (and still unique) approach: apply the discipline of enterprise architecture to the business of large scale IT management itself. Author Charles Betz applies his deep practitioner experience to a critical reading of ITIL 2011, COBIT version 4, the CMMI suite, the IT portfolio management literature, and the Agile/Lean IT convergence, and derives a value stream analysis, IT semantic model, and enabling systems architecture (covering current topics such as CMDB/CMS, Service Catalog, and IT Portfolio Management). Using the concept of design patterns, the book then presents dozens of visual models documenting challenging problems in integrating IT management, showing how process, data, and IT management systems must work together to enable IT and its business partners. The edition retains the fundamental discipline of traceable process, data, and system analysis that has made the first edition a favored desk reference for IT process analysts around the world. This best seller is a must read for anyone charged with enterprise architecture, IT planning, or IT governance and management.

Pricing, Risk, and Performance Measurement in Practice

  • 1st Edition
  • October 22, 2009
  • Wolfgang Schwerdt + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 9 2 3 0 4 - 8
How can managers increase their ability to calculate price and risk data for financial instruments while decreasing their dependence on a myriad of specific instrument variants? Wolfgang Schwerdt and Marcelle von Wendland created a simple and consistent way to handle and process large amounts of complex financial data. By means of a practical framework, their approach analyzes market and credit risk exposure of financial instruments and portfolios and calculates risk adjusted performance measures. Its emphasis on standardization yields significant improvements in speed and accuracy.Schwerdt and von Wendland's focus on practical implementation directly addresses limitations imposed by the complex and costly processing time required for advanced risk management models and pricing hundreds of thousands of securities each day. Their many examples and programming codes demonstrate how to use standards to build financial instruments, how to price them, and how to measure the risk and performance of the portfolios that include them.

Foundations of Multidimensional and Metric Data Structures

  • 1st Edition
  • August 8, 2006
  • Hanan Samet
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 3 6 9 4 4 6 - 1
Foundations of Multidimensional and Metric Data Structures provides a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets. The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional data structures and algorithms; and implementation details for the most useful data structures. Each section includes a large number of exercises and solutions to self-test and confirm the reader's understanding and suggest future directions. The book is an excellent and valuable reference tool for professionals in many areas, including computer graphics, databases, geographic information systems (GIS), game programming, image processing, pattern recognition, solid modeling, similarity retrieval, and VLSI design.