TAILIEUCHUNG - Keyword Search in Databases- P25

Keyword Search in Databases- P25:Conceptually, a database can be viewed as a data graph GD(V ,E), where V represents a set of objects, and E represents a set of connections between objects. In this book, we concentrate on two kinds of databases, a relational database (RDB) and an XML database. In an RDB, an object is a tuple that consists of many attribute values where some attribute values are strings or full-text; there is a connection between two objects if there exists at least one reference from one to the other | . KEYWORD SEARCH ACROSS DATABASES 119 Foreign Key Join Finder Offline Multiple Databases i Y Index Builder C N-Evaluatio C N-Generatio - Online Figure The architecture of Kite where Pd wi Wj D is the set of tuple pairs defined as Pd wi Wj D t t t e D t e D t contains Wi t contains Wj t and t can be joined in a sequence of length d in D . Nd D is the total number of tuple pairs t t in D such that t and t can be joined in a sequence of length d. Nd wi Wj D is the total number of tuple pairs t t in D such that t contains Wi t contains Wj t and t can be joined in a sequence of length d in D. Nd Wi Wj D Pd Wi Wj D . The final score Given the node and edge scores for the keyword query Q c K the score of database D e D is defined as score D Q score D Wi score D Wj score D Wi Wj Wi eQ Wj eQ i j The databases with the top- scores computed this way are chosen to answer query Q. ANSWERING KEYWORD QUERIES ACROSS DATABASES Given the set of multiple databases to be evaluated a distributed keyword query finds a set of MTJNTs such that the tuples in each MTJNT may come from a different database. In Kite Sayyadian et al. 2007 a framework to answer such a distributed keyword query is developed Figure . We discuss the main components below. Foreign Key Join Finder The foreign key join finder discovers the foreign key reference between tuples from different databases. For each pair of tables U and V in different databases there are 4 steps to find the foreign key references from tuples in U to tuples in V. 1. Finding keys in table U. In this step a set of key attributes are discovered to be joined in table V. The algorithms developed in TANE Huhtala et al. 1999 are adopted. 2. Finding joinable attributes in table V. For the set of keys in U found in the first step a set of attributes are found in table V that can be joined with these keys. The algorithm Bellman Dasu et al. 2002 is used for this purpose. 3. Generating foreign key join candidates. In this

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