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Keyword Search in Databases (Synthesis Lectures on Data Management)

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It has become highly desirable to provide flexible ways for users to query/search information by integrating database (DB) and information retrieval (IR) techniques in the same platform. On one hand, the sophisticated DB facilities provided by a database management system assist users to query well-structured information using a query language based on database schemas.Such systems include conventional rdbmss (such as DB2, ORACLE, SQL-Server), which use sql to query relational databases (RDBs) andXML data management systems, which use XQuery to queryXML databases. On the other hand, IR techniques allow users to search unstructured information using keywords based on scoring and ranking, and they do not need users to understand any database schemas. The main research issues on DB/IR integration are discussed by Chaudhuri et al. [2005] and debated in a SIGMOD panel discussion [Amer-Yahia et al., 2005]. Several tutorials are also given on keyword search over RDBs and XML databases, including those by Amer-Yahia and Shanmugasundaram [2005]; Chaudhuri and Das [2009]; Chen et al. [2009].

The main purpose of this book is to survey the recent developments on keyword search over databases that focuses on finding structural information among objects in a database using a keyword query that is a set of keywords. Such structural information to be returned can be either trees or subgraphs representing how the objects, which contain the required keywords, are interconnected in an RDB or in an XML database. In this book, we call this structural keyword search or, simply, keyword search. The structural keyword search is completely different from finding documents that contain all the user-given keywords. The former focuses on the interconnected object structures, whereas the latter focuses on the object content. In a DB/IR context, for this book, we use keyword search and keyword query interchangeably.We introduce forms of answers, scoring/ranking functions, and approaches to process keyword queries.
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