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020 _a9783031018664
_9978-3-031-01866-4
024 7 _a10.1007/978-3-031-01866-4
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aLissandrini, Matteo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987707
245 1 0 _aData Exploration Using Example-Based Methods
_h[electronic resource] /
_cby Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIV, 146 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Management,
_x2153-5426
505 0 _aPreface -- Acknowledgments -- Introduction -- Relational Data -- Graph Data -- Textual Data -- Unifying Example-Based Approaches -- Online Learning -- The Road Ahead -- Conclusions -- Bibliography -- Authors' Biographies.
520 _aData 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 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_987709
650 2 4 _aData Structures and Information Theory.
_931923
700 1 _aMottin, Davide.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987711
700 1 _aPalpanas, Themis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987712
700 1 _aVelegrakis, Yannis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987713
710 2 _aSpringerLink (Online service)
_987715
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000935
776 0 8 _iPrinted edition:
_z9783031007385
776 0 8 _iPrinted edition:
_z9783031029943
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_987717
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01866-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c86138
_d86138