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Data Exploration Using Example Based by Lissandrini Matteo (17 results)
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Data Exploration Using Example-Based Methods (Synthesis Lectures on Data Management)
Language: English
Published by Morgan & Claypool Publishers, 2018
ISBN 10: 1681734559 ISBN 13: 9781681734552
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Data Exploration Using Example-Based Methods (Synthesis Lectures on Data Management)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Data Exploration Using Example-based Methods
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Data Exploration Using Example-based Met
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Data Exploration Using Example-based Methods
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Taschenbuch. Condition: Neu. Data Exploration Using Example-Based Methods | Matteo Lissandrini (u. a.) | Taschenbuch | Synthesis Lectures on Data Management | xiv | Englisch | 2018 | Springer | EAN 9783031007385 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Data Exploration Using Example-Based Methods
Language: English
Published by Springer International Publishing Nov 2018, 2018
ISBN 10: 3031007387 ISBN 13: 9783031007385
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 164 pp. Englisch.
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Data Exploration Using Example-Based Methods
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2018
ISBN 10: 3031007387 ISBN 13: 9783031007385
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 th.
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Data Exploration Using Example-Based Methods
Language: English
Published by Springer, Springer Nov 2018, 2018
ISBN 10: 3031007387 ISBN 13: 9783031007385
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 164 pp. Englisch.
