Data Cleaning as a Process data discrepancy detection Use metadata(e.g, domain, range, dependency, distribution ◆ Check field overloading e Check uniqueness rule, consecutive rule and null rule ◆ Use commercial tools n Data scrubbing use simple domain knowledge(e. g, postal code, spell-check to detect errors and make corrections n Data auditing by analyzing data to discover rules and relationship to detect violators(e.g, correlation and clustering to find outliers Data migration and integration e Data migration tools allow transformations to be specified ETL(EXtraction/Transformation/Loading)tools: allow users to specify transformations through a graphical user interface Integration of the two processes Iterative and interactive(e.g, Potter's Wheels) 11 同济大学软件学院 ool of Software Engineering. Tongpi Unversity
11 Data Cleaning as a Process ◼ Data discrepancy detection ◆ Use metadata (e.g., domain, range, dependency, distribution) ◆ Check field overloading ◆ Check uniqueness rule, consecutive rule and null rule ◆ Use commercial tools Data scrubbing: use simple domain knowledge (e.g., postal code, spell-check) to detect errors and make corrections Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers) ◼ Data migration and integration ◆ Data migration tools: allow transformations to be specified ◆ ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface ◼ Integration of the two processes ◆ Iterative and interactive (e.g., Potter’s Wheels)
Data Preprocessing a Data Preprocessing: An Overview ◆ Data Quality Major Tasks in Data Preprocessing a Data Cleaning u Data Integration ■ Data reduction Data Transformation and data discretization ■ Summary 同济大学软件学院 ool of Software Engineering. Tongpi Unversity
1212 Data Preprocessing ◼ Data Preprocessing: An Overview ◆ Data Quality ◆ Major Tasks in Data Preprocessing ◼ Data Cleaning ◼ Data Integration ◼ Data Reduction ◼ Data Transformation and Data Discretization ◼ Summary
Data Integration Data integration o Combines data from multiple sources into a coherent store ■ Schema integration:e.g.,A. cust-id≡B.cust# o Integrate metadata from different sources Entity identification problem Identify real world entities from multiple data sources, e. g, Bill Clinton= William clinton a Detecting and resolving data value conflicts For the same real world entity, attribute values from different sources are different Possible reasons: different representations, different scales, e. g metric vs. British units 同济大学软件学院 13 ool of Software Engineering. Tongpi Unversity
13 13 Data Integration ◼ Data integration: ◆ Combines data from multiple sources into a coherent store ◼ Schema integration: e.g., A.cust-id B.cust-# ◆ Integrate metadata from different sources ◼ Entity identification problem: ◆ Identify real world entities from multiple data sources, e.g., Bill Clinton = William Clinton ◼ Detecting and resolving data value conflicts ◆ For the same real world entity, attribute values from different sources are different ◆ Possible reasons: different representations, different scales, e.g., metric vs. British units
Handling Redundancy in Data Integration Redundant data occur often when integration of multiple databases o Object identification The same attribute or object may have different names in different databases ◆ Derivab/ data: One attribute may be a“ derived attribute in another table, e.g., annual revenue a Redundant attributes may be able to be detected by correlation analysis and covariance analysis a Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality 同济大学软件学院 14 ool of Software Engineering. Tongpi Unversity 14
14 14 Handling Redundancy in Data Integration ◼ Redundant data occur often when integration of multiple databases ◆ Object identification: The same attribute or object may have different names in different databases ◆ Derivable data: One attribute may be a “derived” attribute in another table, e.g., annual revenue ◼ Redundant attributes may be able to be detected by correlation analysis and covariance analysis ◼ Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality
Correlation Analysis(Nominal Data) a X(chi-square)test (Observed-Expected) x Expected a The larger the x value the more likely the variables are elated the cells that contribute the most to the x2 value are those whose actual count is very different from the expected count Correlation does not imply causality of hospitals and of car-theft in a city are correlated Both are causally linked to the third variable: population 同济大学软件学院 ool of Software Engineering. Tongpi Unversity 15
15 Correlation Analysis (Nominal Data) ◼ Χ2 (chi-square) test ◼ The larger the Χ2 value, the more likely the variables are related ◼ The cells that contribute the most to the Χ2 value are those whose actual count is very different from the expected count ◼ Correlation does not imply causality ◆ # of hospitals and # of car-theft in a city are correlated ◆ Both are causally linked to the third variable: population − = Expected Observed Expected 2 2 ( )