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Author: Matteo Lissandrini
Publisher: Springer Nature
ISBN: 3031018664
Category : Computers
Languages : en
Pages : 146
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Book Description
Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when the task is particularly challenging like finding duplicate items, or simply when they are exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how that different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. The book presents also the challenges and the new frontiers of machine learning in online settings which recently attracted the attention of the database community. The lecture concludes with a vision for further research and applications in this area.
Author: Matteo Lissandrini
Publisher: Springer Nature
ISBN: 3031018664
Category : Computers
Languages : en
Pages : 146
Get Book Here
Book Description
Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when the task is particularly challenging like finding duplicate items, or simply when they are exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how that different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. The book presents also the challenges and the new frontiers of machine learning in online settings which recently attracted the attention of the database community. The lecture concludes with a vision for further research and applications in this area.
Author: C. J. Date
Publisher: Technics Publications
ISBN: 1634629841
Category : Computers
Languages : en
Pages : 243
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Book Description
Along with its companion volume (Database Dreaming Volume II), this book offers a collection of essays on the general topic of relational databases and relational database technology. Most of those essays, though not all, have been published before, but only in journals and magazines that are now hard to find or in books that are now out of print. Here’s a lightly edited excerpt from the preface (so this is the author speaking): I went back and reviewed all of those early essays, looking for ones that seemed worth reviving (or, rather, revising and reviving) at this time. Of course, some of them definitely weren’t! However, out of a total of around 130 original papers, I did find some 20 or so that seemed to me worth preserving and hadn’t already been incorporated in, or superseded by, more recent books of mine. So I tracked down the original versions of those 20 or so papers and set to work. When I was done, though, I found I had somewhere in excess of 600 pages on my hands—too much, in my view, for just one book, and so I split them across two separate volumes. Highlights of the present volume include a discussion of the difficulties involved in providing a relational interface to a nonrelational system; a tutorial on the quantifiers and what happens to them under three-valued logic; an examination of the effect of user defined types on optimization; some thoughts on normalization and database design tools; and caveats regarding certain important database operators, especially outer join and negation.
Author: Chong Ho Alex Yu
Publisher: CRC Press
ISBN: 100077807X
Category : Business & Economics
Languages : en
Pages : 291
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Book Description
This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Author: Roger Marjoribanks
Publisher: Springer Science & Business Media
ISBN: 3540743758
Category : Science
Languages : en
Pages : 243
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Book Description
This practical step-by-step guide describes the key geological field techniques needed by today's exploration geologists involved in the search for metallic deposits. The techniques described are fundamental to the collection, storage and presentation of geological data and their use to locate ore. This book explains the various tasks which the exploration geologist is asked to perform in the sequence in which they might be employed in an actual exploration project. Hints and tips are give. The steps are illustrated with numerous examples drawn from real projects on which the author has worked. The book emphasizes traditional skills and shows how they can be combined effectively with modern technological approaches.
Author: Mike Kahn
Publisher: Packt Publishing Ltd
ISBN: 1805123424
Category : Computers
Languages : en
Pages : 264
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Book Description
Leverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modeling Key Features Use mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud console Master optimization techniques for storage and query performance in BigQuery Engage with case studies on data exploration and preparation for advertising, transportation, and customer support data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges. The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem. The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems. By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.What you will learn Assess the quality of a dataset and learn best practices for data cleansing Prepare data for analysis, visualization, and machine learning Explore approaches to data visualization in BigQuery Apply acquired knowledge to real-life scenarios and design patterns Set up and organize BigQuery resources Use SQL and other tools to navigate datasets Implement best practices to query BigQuery datasets Gain proficiency in using data preparation tools, techniques, and strategies Who this book is for This book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.
Author: C. J. Date
Publisher: Apress
ISBN: 1484255402
Category : Computers
Languages : en
Pages : 449
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Book Description
Create database designs that scale, meet business requirements, and inherently work toward keeping your data structured and usable in the face of changing business models and software systems. This book is about database design theory. Design theory is the scientific foundation for database design, just as the relational model is the scientific foundation for database technology in general. Databases lie at the heart of so much of what we do in the computing world that negative impacts of poor design can be extraordinarily widespread. This second edition includes greatly expanded coverage of exotic and little understood normal forms such as: essential tuple normal form (ETNF), redundancy free normal form (RFNF), superkey normal form (SKNF), sixth normal form (6NF), and domain key normal form (DKNF). Also included are new appendixes, including one that provides an in-depth look into the crucial notion of data consistency. Sequencing of topics has been improved, and many explanations and examples have been rewritten and clarified based upon the author’s teaching of the content in instructor-led courses. This book aims to be different from other books on design by bridging the gap between the theory of design and the practice of design. The book explains theory in a way that practitioners should be able to understand, and it explains why that theory is of considerable practical importance. Reading this book provides you with an important theoretical grounding on which to do the practical work of database design. Reading the book also helps you in going to and understanding the more academic texts as you build your base of knowledge and expertise. Anyone with a professional interest in database design can benefit from using this book as a stepping-stone toward a more rigorous design approach and more lasting database models. What You Will LearnUnderstand what design theory is and is notBe aware of the two different goals of normalizationKnow which normal forms are truly significant Apply design theory in practice Be familiar with techniques for dealing with redundancy Understand what consistency is and why it is crucially important Who This Book Is For Those having a professional interest in database design, including data and database administrators; educators and students specializing in database matters; information modelers and database designers; DBMS designers, implementers, and other database vendor personnel; and database consultants. The book is product independent.
Author: Niranjan C. Nanda
Publisher: Springer Nature
ISBN: 3030753018
Category : Science
Languages : en
Pages : 307
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Book Description
This book is meant for geoscientists and engineers who are beginners, and introduces them to the field of seismic data interpretation and evaluation. The exquisite seismic illustrations and real case examples interspersed in the text help the readers appreciate the interpretation of seismic data in a simple way, and at the same time, emphasize the multidisciplinary, integrated practical approach to data evaluation. A concerted effort has been made for the readers to realize that mindless interpretation of seismic data using sophisticated software packages, without having a grasp on the elementary principles of geology and geophysics, and coupled with their over-reliance on workstations to provide solutions can have appalling results all too very often.
Author: Dianne Finch-Claydon
Publisher: Taylor & Francis
ISBN: 1317435354
Category : Language Arts & Disciplines
Languages : en
Pages : 198
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Book Description
This book offers an engaging and accessible introduction to data visualization for communicators, covering everything from data collection and analysis to the creation of effective data visuals. Straying from the typical "how to visualize data" genre often written for technical audiences, Big Data in Small Slices offers those new to data gathering and visualization the opportunity to better understand data itself. Using the concept of the "data backstory," each chapter features discussions with experts, from marine scientists to pediatricians and city government officials, who produce datasets in their daily work. The reader is guided through the process of designing effective visualizations based on their data, delving into how datasets are produced and vetted, and how to assess their weaknesses and strengths, ultimately offering readers the knowledge needed to produce their own effective data visuals. This book is an invaluable resource for anyone interested in data visualization and storytelling, from journalism and communications students to public relations professionals. A detailed accompanying website features additional material for readers, including links to all the original datasets used in the text, at www.bigdatainsmallslices.com
Author: M. Smelror
Publisher: Geological Society of London Special Publications
ISBN: 1786205734
Category : Science
Languages : en
Pages : 346
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Book Description
Raw materials have been essential in the development of all human societies through history and moving into a greener, more carbon-lean future we become increasingly reliant on access to a growing number of raw materials. Minerals for new technologies improving the quality of our lives and the environment are the building blocks of the new Green Stone Age. This Special Publication presents ongoing research and mapping programmes focusing on minerals needed for the transformation to greener societies. In addition to new exploration models and shared geological information on the different prospective currently mined areas, the notion of criticality in different countries is discussed and examples of ongoing national and cross-country research and mapping programmes are presented. In addition to the resource/reserve and technical-economic aspects, the social and environmental dimensions are also a focus in some of the contributions, as holistic approaches to the exploration and exploitation of critical minerals and materials are needed to fulfil the green transition and goals for the Green Stone Age.
Author:
Publisher:
ISBN:
Category : Data mining
Languages : en
Pages : 136
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Book Description
