Data Exploration Using Example-Based Methods (Record no. 86138)

000 -LEADER
fixed length control field 04100nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-3-031-01866-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730165151.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2019 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031018664
-- 978-3-031-01866-4
082 04 - CLASSIFICATION NUMBER
Call Number 004.6
100 1# - AUTHOR NAME
Author Lissandrini, Matteo.
245 10 - TITLE STATEMENT
Title Data Exploration Using Example-Based Methods
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2019.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIV, 146 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Data Management,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- Acknowledgments -- Introduction -- Relational Data -- Graph Data -- Textual Data -- Unifying Example-Based Approaches -- Online Learning -- The Road Ahead -- Conclusions -- Bibliography -- Authors' Biographies.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Mottin, Davide.
700 1# - AUTHOR 2
Author 2 Palpanas, Themis.
700 1# - AUTHOR 2
Author 2 Velegrakis, Yannis.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01866-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2019.
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-- text
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer networks .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data structures (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information theory.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Communication Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Structures and Information Theory.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2153-5426
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-- ZDB-2-SXSC

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