Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 125-143).

1. Introduction -- 1.1 Example-driven exploration -- 1.1.1 Problem formulation -- 1.1.2 Applications of example-based methods -- 1.2 Road map --

Part I. Example-based approaches --

2. 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 --

3. 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 --

4. 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 --

5. 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 --

Part II. Open research directions --

6. 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 --

7. 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 --

8. Conclusions -- Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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.

Also available in print.

Title from PDF title page (viewed on November 28, 2018).