Page 42 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062942b- Field overloading:- is another error source that typically results when developers squeeze new attribute definitions into unused (bit) portions of already defined attributes (e.g., an unused bit of an attribute that has a value range that uses only 31 out of32 bits). c- Unique rule:- says that each value of the given attribute must be different from all other values for that attribute. Each value of the given attribute must be different from all other values for that attribute. A consecutive rule says that there can be no missing values between the lowest and highest values for the attribute, and that all values must also be unique as in check numbers. A null rule specifies the use of blanks, question marks, special characters, or other strings that may indicate the null condition (e.g., where a value for a given attribute is not available), and how such values should be handled. d- Commercial tools:- data scrubbing tools use simple domain knowledge (e.g., knowledge of postal addresses and spellchecking) to detect errors and make corrections in the data. These tools rely on parsing and fuzzy matching techniques when cleaning data from multiple sources. Data auditing tools find discrepancies by analyzing the data to discover rules and relationships and detecting data that violate such conditions. They are variants of data mining tools. For example, they may employ statistical analysis to find correlations, or clustering to identify outliers. They may also use the basic statistical data descriptions 4- Data migration and integration: Data migration tools allow simple transformations to be specified such as to replace the string %u201cgender%u201d by %u201csex%u201d. ETL (extraction/transformation/loading) tools allow users to specify transforms through a graphical user interface (GUI). New approaches to data cleaning emphasize increased interactivity. Potter%u2019s Wheel, for example, is a publicly available data cleaning tool that integrates discrepancy detection and transformation. Another approach to increased interactivity in data cleaning is the development of declarative languages for the specification of data transformation operators. Such work focuses on defining powerful extensions to SQL and algorithms that enable users to express data cleaning specifications efficiently. IV. Data integration