A Knowledge Based Approach to Assisting in Data Quality Judgement
A Knowledge Based Approach to Assisting in Data Quality Judgement
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In our eReader you can find the full English version of the book. Read A Knowledge Based Approach to Assisting in Data Quality Judgement Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book A Knowledge Based Approach to Assisting in Data Quality Judgement
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This allows removing q-. =Vj from Q, if both q^:=v^ and . -u are elements in Q. Continue the process of removing dominated quality parameters, until no pair of the quality parameters in Q are related in the dominance relation. Let il' denote the modified Q produced at the end of this removal process. The quality merge of the quality parameters in Q' is the corresponding irreducible quality-merge statement of e, and the algorithm returns ©(Q). It is proven in (Jang & Wang, 1991) that Algorithm ...Q-Reduction shown in Figure 2 always results in a unique output in the first-order data-quality reasoner, in that all dominance relations must be first-order.
3. 2. Algorithm Q-Merge When presented with an instance of the quality-estimating problem (®{q-^, q2, --, q„), DR) for some integer n, Algorithm Q-Merge first instantiates the given quality-merge statement, accordingly. The instantiated quality-merge statement is then reduced until the reduction process results m another instantiated quality-merge statement which cannot be reduced any further (using Q-Reduction).
What to read after A Knowledge Based Approach to Assisting in Data Quality Judgement?
You can find similar books in the "Read Also" column, or choose other free books by Yeona Jang to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
This allows removing q-. =Vj from Q, if both q^:=v^ and . -u are elements in Q. Continue the process of removing dominated quality parameters, until no pair of the quality parameters in Q are related in the dominance relation. Let il' denote the modified Q produced at the end of this removal process. The quality merge of the quality parameters in Q' is the corresponding irreducible quality-merge statement of e, and the algorithm returns ©(Q). It is proven in (Jang & Wang, 1991) that Algorithm ...Q-Reduction shown in Figure 2 always results in a unique output in the first-order data-quality reasoner, in that all dominance relations must be first-order.
3. 2. Algorithm Q-Merge When presented with an instance of the quality-estimating problem (®{q-^, q2, --, q„), DR) for some integer n, Algorithm Q-Merge first instantiates the given quality-merge statement, accordingly. The instantiated quality-merge statement is then reduced until the reduction process results m another instantiated quality-merge statement which cannot be reduced any further (using Q-Reduction).
What to read after A Knowledge Based Approach to Assisting in Data Quality Judgement?
You can find similar books in the "Read Also" column, or choose other free books by Yeona Jang to read onlineMoreLess
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