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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
For example, removing duplicates using distinct may be slow in the database; thus, it makes sense to do it outside. On the other side, if using distinct significantly (x100) decreases the number of rows to be extracted, then it makes sense to remove duplications as early as possible in the database before unloading data.
A marked increase of dacrocytes is known as dacrocytosis. These tear drop cells are found primarily in diseases with bone marrow fibrosis, such as: primary myelofibrosis, myelodysplastic syndromes during the late course of the disease, rare form of acute leukemias and myelophthisis caused by metastatic cancers.
By allowing a false positive rate for the duplicates, the communication volume can be reduced further as the PEs don't have to send elements with duplicated hashes at all and instead any element with a duplicated hash can simply be marked as a duplicate. As a result, the false positive rate for duplicate detection is the same as the false ...
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The chance of it happening is a function of the degree of sharing of repetitive elements between two chromosomes. The products of this recombination are a duplication at the site of the exchange and a reciprocal deletion. Ectopic recombination is typically mediated by sequence similarity at the duplicate breakpoints, which form direct repeats.
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During this process, known as the cell cycle, a cell duplicates its contents and then divides in two. The purpose of the cell cycle is to accurately duplicate each organism's DNA and then divide the cell and its contents evenly between the two resulting cells.