Search-Based Applications. Gregory Grefenstette
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10.4 SBA Platforms: Other Vendors
10.5 SBA Vendors: COTS Applications
11.1 When Are SBAs Used?
11.2 How Are SBAs Used?
12 Anatomy of a Search Based Application
12.1 SBAs for Structured Data
12.1.1 Data Collection
12.1.2 Data Processing
12.1.3 Data Updates
12.1.4 Data Retrieval & Analysis
12.2 SBAs for Unstructured Content
12.2.1 Data Collection
12.2.2 Data Processing
12.2.3 Data Updates
12.2.4 Data Retrieval & Analysis
12.3 SBAs for Hybrid Content
13.2 A Track & Trace Solution
13.3 Existing Drawbacks
13.4 Opting for a Search Based Application
13.5 First prototypes
13.6 Deployment
13.7 Future
14.1 Background
14.2 The Urbanizer Solution
14.3 How Urbanizer Works
14.4 What’s Next
15 Case Study: National Postal Agency
15.1 Customer Service SBA
15.1.1 Background
15.1.2 Deployment
15.2 Operational Business Intelligence (OBI) SBA
15.2.1 Background
15.2.2 Deployment
15.3 Sales Information SBA for Telemarketing
15.3.1 Background
15.3.2 Deployment
16.1 The Influence of the Deep Web
16.1.1 Surfacing Structured Data
16.1.2 Opening Access to Multimedia Content
16.2 The Influence of the Semantic Web
16.3 The Influence of the Mobile Web
16.3.1 Mission-Based IR
16.3.2 Innovation in Visualization
16.4 And Continuing Database/Search Convergence
Acknowledgments
We would like to thank Gary Marchionini and Diane Cerra for inviting us to participate in this timely and important lecture series, with a special thank you to Diane for her assistance and patience in guiding us through the publication process. We would also like to thank Morgan & Claypool’s reviewers, including Susan Feldman, Stephen Arnold and John Tait, for their thoughtful suggestions and comments on our manuscript. Ms. Feldman and Mr. Arnold are constant sources of insight for all of us working in search and information access-related disciplines, and we welcome Mr. Tait’s remarks based on his long IR research experience at the University of Sunderland and his more recent efforts at advancing research in IR for patents and other large scale collections at the Information Retrieval Facility.
In addition, we are grateful to our colleagues and managers at Exalead for allowing us time to work on this lecture, and for providing valuable feedback on our draft manuscript, especially Olivier Astier, Stéphane Donzé and David Thoumas. We would also like to thank our partners and customers. They are the source of the examples provided in this book, and they have played a pioneering role in expanding the boundaries of applied search technologies, in general, and search-based applications, in particular.
Finally, we would like to thank our families. Their love sustains us in all we do, and we dedicate this book to them.
Gregory Grefenstette and Laura Wilber
December 2010
Glossary
Glossary
| ACID | Constraints on a database for achieving Atomicity, Consistency, Isolation and Durability |
| Agility | The ease with which a computer application can be altered, improved, or extended |
| API | Application Programming Interface, specifies how to call a computer program, what arguments to use, and what you can expect as output |
| Application layer | Part of the Open System Interconnection model, in which an application interacts with a human user, or another application |
| Atomicity | The idea that a database transaction either succeeds or fails in its entirety |
| Availability | The percentage of time that data can be read or used. |
| Batch | A computer task that is programmed to run at a certain time (usually at night) with no human intervention |
| B2C | Business to Customer; B2C websites offer goods or services directly to users |
| B+ tree | A block-oriented data structure for efficient insertion and removal of data nodes |
| BI | Business Intelligence, views on data that aid users with business planning and decision making |
| BigTable | An internal data storage system used by Google, handles multidimensional key-value pairs |
| BSON | Binary JSON |