| 000 -LEADER |
|---|
| fixed length control field |
nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190326b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781681734552 |
| Terms of availability |
(pbk) |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
025.04 |
| Item number |
LIS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lissandrini, Matteo |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data exploration using example-based methods |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
S.l. : |
| Name of publisher, distributor, etc |
Morgan & Claypool Publisher, |
| Date of publication, distribution, etc |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 146 p. : |
| Other physical details |
ill. ; |
| Dimensions |
23.3 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
USD |
| Price amount |
64.95 |
| Unit of pricing |
00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Database searching |
|
|---|
| Topical term or geographic name as entry element |
Database management |
|
|---|
| Topical term or geographic name as entry element |
Programming by example |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Davide Mottin |
|
|---|
| Personal name |
Themis Palpanas |
|
|---|
| Personal name |
Yannis Velegrakis |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |