Understanding Schema Mapping Process for Better Data Integration
Learn about the Schema Mapping process that enables the extraction and translation of data from local sources into a global database, addressing key issues such as mapping creation and maintenance. Explore the steps involved in algorithm implementation and see an example showcasing source and target relations.
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Presentation Transcript
Schema Creation Purvish Oza Presentation id#18
Introduction Algorithm 1 Output Algorithm 2 Agenda
Introduction Schema mapping is all about how we obtain global database from local. Schema mapping is used to explicitly extract data from the sources, and translate them into data warehouse schema for populating. Two main issues : Mapping creation and Mapping Maintenance Mapping creation is, explicit query mapping from local database to global. Mapping maintenance is correction of mapping inconsistencies.
Mapping Creation Mapping creation starts with source LCS(Local Conceptual Database) , the target GCS(Global Conceptual Database) and a set of schema matches and a set of queries , when executed, will create GCS data instance. Source LCS consist of relations S = {S1,S2,..,Sm} , GCS consists of set of global relations T = {T1, ,Tn} and M consists of set of schema match rules.
Algorithm This algorithm proceeds into 4 steps. Step 1: Mk is partitioned into its subsets (Mk1, Mkn) such that each mk contains at most one match for each attribute of Tk. Step 2: Analyzes each potential candidate set Mjk to see if a good query can be produced for it. If all the matches in Mjk map values from a single source relation to Tk, then it is easy to generate a query corresponding to Mjk .
Algorithm Cont.. Step 3: The algorithm looks for a cover of the candidate sets Mk. The point of determining a cover is that it accounts for all of the matches and is, therefore, sufficient to generate the target relation Tk. If there are multiple covers, then they are ranked in increasing number of the candidate sets in the cover. Step 4: The final step of the algorithm builds a query Qjk for each of the candidate sets in the cover selected in the previous step.
Example Source relation(LCS): S1(A1;A2) S2(B1;B2;B3) S3(C1;C2;C3) S4(D1;D2) Target relation (GCS) T(W1;W2;W3;W4)
Output After its relatively applied to each target relation Tk is a set of queries Q = {Qk} that when executed , produce data for the GCS relations. The algorithm takes in to account semantics of the source schema since it considers foreign key relationships in determining which queries to generate.
Query building SELECT clause includes all correspondences(c). FROM clause includes all source relations and join the paths determined earlier. WHERE clause includes conjunct of all predicates. GROUPBY is used over attributes in SELECT clause. If there is no aggregate in c , HAVING clause is added.
Algorithm 2 To deal with target semantics as well as source semantics, existing algorithm has been extended. It is more powerful then the previous one as it can also handle the nested structures that are common in XML ,object databases and relation systems. Operates in 2 steps: Semantic translation and Data translation. Semantic translation further classified in to 2 steps: Step1: Produce logical relation after examining the inter-schema semantics. Step 2: Produces set of queries after interpret the inter-schema semantics.
