Data exploration using example-based methods:
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. Expl...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool
[2019]
|
| Schriftenreihe: | Synthesis lectures on data management
#53 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on November 28, 2018) |
| Beschreibung: | 1 Online-Resource (xvii, 146 Seiten) Illustrationen |
| ISBN: | 9781681734569 |
| DOI: | 10.2200/S00881ED1V01Y201810DTM053 |
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| spelling | Lissandrini, Matteo Verfasser (DE-588)1186639741 aut Data exploration using example-based methods Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis [San Rafael, California] Morgan & Claypool [2019] © 2019 1 Online-Resource (xvii, 146 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on data management #53 Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on November 28, 2018) 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 Database searching Database management Programming by example (Computer science) Explorative Datenanalyse (DE-588)4128896-8 gnd rswk-swf Explorative Datenanalyse (DE-588)4128896-8 s DE-604 Mottin, Davide Verfasser (DE-588)1186639709 aut Palpanas, Themis Verfasser (DE-588)1207415944 aut Velegrakis, Yannis Verfasser (DE-588)1207416169 aut Erscheint auch als Druck-Ausgabe, paperback 978-1-68173-455-2 Erscheint auch als Druck-Ausgabe, hardcover 978-1-6817-3457-6 Synthesis lectures on data management #53 (DE-604)BV036731811 53 https://doi.org/10.2200/S00881ED1V01Y201810DTM053 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Lissandrini, Matteo Mottin, Davide Palpanas, Themis Velegrakis, Yannis Data exploration using example-based methods Synthesis lectures on data management Database searching Database management Programming by example (Computer science) Explorative Datenanalyse (DE-588)4128896-8 gnd |
| subject_GND | (DE-588)4128896-8 |
| title | Data exploration using example-based methods |
| title_auth | Data exploration using example-based methods |
| title_exact_search | Data exploration using example-based methods |
| title_full | Data exploration using example-based methods Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis |
| title_fullStr | Data exploration using example-based methods Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis |
| title_full_unstemmed | Data exploration using example-based methods Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis |
| title_short | Data exploration using example-based methods |
| title_sort | data exploration using example based methods |
| topic | Database searching Database management Programming by example (Computer science) Explorative Datenanalyse (DE-588)4128896-8 gnd |
| topic_facet | Database searching Database management Programming by example (Computer science) Explorative Datenanalyse |
| url | https://doi.org/10.2200/S00881ED1V01Y201810DTM053 |
| volume_link | (DE-604)BV036731811 |
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