000 06518nam a22007451i 4500
001 8552738
003 IEEE
005 20200413152928.0
006 m eo d
007 cr cn |||m|||a
008 181128s2019 caua foab 000 0 eng d
020 _a9781681734569
_qebook
020 _z9781681734576
_qhardcover
020 _z9781681734552
_qpaperback
024 7 _a10.2200/S00881ED1V01Y201810DTM053
_2doi
035 _a(CaBNVSL)swl000408792
035 _a(OCoLC)1076493846
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA76.9.D3
_bL573 2019
082 0 4 _a005.7565
_223
100 1 _aLissandrini, Matteo,
_eauthor.
245 1 0 _aData exploration using example-based methods /
_cMatteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis.
264 1 _a[San Rafael, California] :
_bMorgan & Claypool,
_c2019.
300 _a1 PDF (xvii, 146 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on data management,
_x2153-5426 ;
_v# 53
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 125-143).
505 0 _a1. Introduction -- 1.1 Example-driven exploration -- 1.1.1 Problem formulation -- 1.1.2 Applications of example-based methods -- 1.2 Road map --
505 8 _aPart I. Example-based approaches --
505 8 _a2. Relational data -- 2.1 Preliminaries -- 2.2 Reverse engineering queries (REQ) -- 2.2.1 Exact reverse engineering -- 2.2.2 Approximate reverse engineering -- 2.3 Schema mapping -- 2.3.1 From schema mapping to examples -- 2.3.2 Example-driven schema mapping -- 2.4 Data cleaning -- 2.4.1 Entity matching -- 2.4.2 Interactive data repairing -- 2.5 Example-based data exploration systems -- 2.6 Summary --
505 8 _a3. Graph data -- 3.1 The graph data model -- 3.2 Search by example nodes -- 3.2.1 Connectivity and closeness -- 3.2.2 Clusters and node attributes -- 3.2.3 Similar entity search in information graphs -- 3.3 Reverse engineering queries on graphs -- 3.3.1 Learning path queries on graphs -- 3.3.2 Reverse engineering SPARQL queries -- 3.4 Search by example structures -- 3.4.1 Graph query via entity-tuples -- 3.4.2 Queries with example subgraphs -- 3.5 Summary --
505 8 _a4. Textual data -- 4.1 Documents as examples -- 4.1.1 Learning relevance from plain-text -- 4.1.2 Modeling networks of document -- 4.2 Semi-structured data as example -- 4.2.1 Relation extraction -- 4.2.2 Incomplete web tables -- 4.3 Summary --
505 8 _a5. Unifying example-based approaches -- 5.1 Data model conversion -- 5.2 Seeking relations -- 5.2.1 Implicit relation -- 5.2.2 Explicit relation -- 5.3 Entity extraction and matching --
505 8 _aPart II. Open research directions --
505 8 _a6. Online learning -- 6.1 Passive learning -- 6.1.1 First- and second-order learning -- 6.1.2 Regularization -- 6.1.3 MindReader -- 6.1.4 Multi-view learning -- 6.2 Active learning -- 6.2.1 Multi-armed bandits -- 6.2.2 Gaussian processes -- 6.3 Explore-by-example --
505 8 _a7. The road ahead -- 7.1 Supporting interactive explorations -- 7.1.1 Query processing -- 7.1.2 Automatic result analysis -- 7.2 Presenting answers and exploration alternatives -- 7.2.1 Results presentation -- 7.2.2 Generation of exploration alternatives -- 7.3 New challenges -- 7.3.1 Explore heterogeneous data -- 7.3.2 Personalized explorations -- 7.3.3 Exploration for everybody --
505 8 _a8. Conclusions -- Bibliography -- Authors' biographies.
506 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _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.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on November 28, 2018).
650 0 _aDatabase searching.
650 0 _aDatabase management.
650 0 _aProgramming by example (Computer science)
653 _asearch by example
653 _adata exploration
653 _ainformation retrieval
653 _adata management
700 1 _aMottin, Davide,
_eauthor.
700 1 _aPalpanas, Themis,
_eauthor.
700 1 _aVelegrakis, Yannis,
_eauthor.
776 0 8 _iPrint version:
_z9781681734552
_z9781681734576
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on data management ;
_v# 53.
_x2153-5426
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8552738
999 _c562334
_d562334