Data Cleaning and Transformation

1. Data Cleaning and Transformation Helena Galhardas DEI IST (based on the slides: “A Survey of Data Quality Issues in Cooperative Information Systems”, Carlo Batini, Tiziana Catarci, Monica Scannapieco, 23rd International Conference on Conceptual Modelling (ER 2004))

2. Agenda • Introduction • Data Quality Problems • Data Quality Dimensions • Relevant activities in Data Quality

3. Data Extraction Data Transformation Data Loading When materializing the integrated data (data warehousing)… SOURCE DATA TARGET DATA ... ... ETL: Extraction, Transformation and Loading 70% of the time in a datawarehousing project is spent with the ETL process

4. Why Data Cleaning and Transformation? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data • e.g., occupation=“” noisy: containing errors or outliers (spelling, phonetic and typing errors, word transpositions, multiple values in a single free-form field) • e.g., Salary=“-10” inconsistent: containing discrepancies in codes or names (synonyms and nicknames, prefix and suffix variations, abbreviations, truncation and initials) • e.g., Age=“42” Birthday=“03/07/1997” • e.g., Was rating “1,2,3”, now rating “A, B, C” • e.g., discrepancy between duplicate records

5. Why Is Data Dirty? • Incomplete data comes from: • non available data value when collected • different criteria between the time when the data was collected and when it is analyzed. • human/hardware/software problems • Noisy data comes from: • data collection: faulty instruments • data entry: human or computer errors • data transmission • Inconsistent (and redundant) data comes from: • Different data sources, so non uniform naming conventions/data codes • Functional dependency and/or referential integrity violation

6. Why is Data Quality Important? Activity of converting source data into target data without errors, duplicates, and inconsistencies, i.e., Cleaning and Transforming to get… High-quality data! • No quality data, no quality decisions! • Quality decisions must be based on good quality data (e.g., duplicate or missing data may cause incorrect or even misleading statistics)

7. Research issues related to DQ • Source Selection • Source Composition • Query Result Selection • Time Syncronization • … • Record Matching(deduplication) • Data Transformation • … • Conflict Resolution • Record Matching • … Data Quality Data Integration Data Cleaning Statistical Data Analysis Data Mining • Edit-imputation • Record Linkage • … Management Information Systems • Error Localization • DB profiling • Patterns in text strings • … Knowledge Representation • Assessment • Process Improvement • Tradeoff Cost/Optimization • … • Conflict Resolution • …

8. Research areas Research areas in DQ systems Models Methodologies Dimensions Measurement/Improvement Techniques Measurement/Improvement Tools and Frameworks EGov Scientific Data Web Data Application Domains …

9. Application contexts • Integrate data from different sources • E.g.,populating a DW from different operational data stores • Eliminate errors and duplicates within a single source • E.g., duplicates in a file of customers • Migrate data from a source schema into a different fixed target schema • E.g., discontinued application packages • Convert poorly structured data into structured data • E.g., processing data collected from the Web

10. Data Quality Dimensions • Accuracy • Errors in data Example:”Jhn” vs. “John” • Currency • Lack of updated data Example: Residence (Permanent) Address: out-dated vs. up-to-dated • Consistency • Discrepancies into the data Example: ZIP Code and City consistent • Completeness • Lack of data • Partial knowledge of the records in a table or of the attributes in a record

11. Example completeness

12. Existing technology • Ad-hoc programs written in a programming language like C or Java or using an RDBMS proprietary language • Programs difficult to optimize and maintain • RDBMS mechanisms for guaranteeing integrity constraints • Do not address important data instance problems • Data transformation scripts using an ETL (Extraction-Transformation-Loading)ordata quality tool

13. Human Knowledge Human Knowledge Data Transformation Typical architecture of a DQ system TARGET DATA SOURCE DATA Data Extraction Data Transformation Data Loading ... ... Data Analysis Metadata Dictionaries Schema Integration

14. Data Quality problems

15. Several taxonomies • Barateiro and Galhardas, 2005. Barateiro, J. and Galhardas, H. (2005). “A survey of data quality tools”. Datenbank-Spektrum, 14:15-21. • Oliveira, P. (2009). “Detecção e correcção de problemas de qualidade de dados: Modelo, Sintaxe e Semântica”. PhD thesis, U. do Minho. • Kim, W., Choi, B.-J., Hong, E.-K., Kim, S.-K., and Lee, D. (2003). “A taxonomy of dirty data. Data Mining and Knowledge Discovery”, 7:81-99. • Mueller, H. and Freytag, J.-C. (2003). “Problems, methods, and challenges in comprehensive data cleansing”. Technical report, Humboldt-Universitaet zu Berlin zu Berlin. • Rahm, E. and Do, H. H. (2000). “Data cleaning: Problems and current approaches”. Bulletin of the Technical Committe on Data Engineering, Special Issue on Data Cleaning, 23:3-13.

16. Data quality problems (1/3) • Schema level data quality problemsprevented with better schema design, schema translation and integration. • Instance level data quality problemserrors and inconsistencies of data that are not prevented at schema level

17. Data quality problems (2/3) • Schema level data quality problems • Avoided by an RDBMS • Missing data – product price not filled in • Wrong data type – “abc” in product price • Wrong data value – 0.5 in product tax (iva) • Dangling data – category identifier of product does not exist • Exact duplicate data – different persons with same ssn • Generic domain constraints – incorrect invoice price • Not avoided by an RDBMS • Wrong categorical data – countries and corresponding states • Outdated temporal data – just-in-time requirement • Inconsistent spatial data – coordinates and shapes • Name conflicts – person vs person or person vs client • Structural Conflicts - addresses

18. Data quality problems (3/3) • Instance level data quality problems • Single record • Missing data in a not null field – ssn:-9999999 • Erroneous data – price:5 but real price:50 • Misspellings: José Maria Silva vs José Maria Sliva • Embedded values: Prof. José Maria Silva • Misfielded values: city: Portugal • Ambiguous data: J. Maria Silva; Miami Florida,Ohio • Multiple records • Duplicate records: Name:Jose Maria Silva, Birth:01/01/1950 and Name:José Maria Sliva, Birth:01/01/1950 • Contradicting records: Name:José Maria Silva, Birth:01/01/1950 and Name:José Maria Silva, Birth:01/01/1956 • Non-standardized data: José Maria Silva vs Silva, José Maria

19. Data Quality Dimensions

20. Traditional data quality dimensions • Accuracy • Completeness • Time-related dimensions: Currency, Timeliness, and Volatility • Consistency • Their definitions do not provide quantitative measures so one or more metrics have to be associated • For each metric, one or more measurement methods have to be provided regarding: (i) where the measurement is taken; (ii) what data are included; (iii) the measurement device; and (iv) the scale on which results are reported. • Schema quality dimensions are also defined

21. Accuracy • Closeness between a value v and a value v’, considered as the correct representation of the real-world phenomenon that v aims to represent. • Ex: for a person name “John”, v’=John is correct, v=Jhn is incorrect • Syntatic accuracy: closeness of a value v to the elements of the corresponding definition domain D • Ex: if v=Jack, even if v’=John , v is considered syntactically correct • Measured by means of comparison functions (e.g., edit distance) that returns a score • Semantic accuracy: closeness of the value v to the true value v’ • Measured with a <yes, no> or <correct, not correct> domain • Coincides with correctness • The corresponding true value has to be known

22. Ganularity of accuracy definition • Accuracy may refer to: • a single value of a relation attribute • an attribute or column • a relation • the whole database

23. Metrics for quantifying accuracy • Weak accuracy error • Characterizes accuracy errors that do not affect identification of tuples • Strong accuracy error • Characterizes accuracy errors that affect identification of tuples • Percentage of accurate tuples • Characterizes the fraction of accurate matched tuples

24. Completeness • “The extent to which data are of sufficient breadth, depth, and scope for the task in hand.” • Three types: • Schema completeness: degree to which concepts and their properties are not missing from the schema • Column completeness: evaluates the missing values for a specific property or column in a table. • Population completeness: evaluates missing values with respect to a reference population

25. Completeness of relational data • The completeness of a table characterizes the extent to which the table represents the real world. • Can be characterized wrt: • The presence/absence and meaning of null values Example: Person(name, surname, birthdate, email), if email is null may indicate the person has no mail (no incompleteness), email exists but is not known (incompletenss), is is not known whether Person has an email (incompleteness may not be the case) • Validity of open world assumption (OWA) or closed world assumption (CWA) • OWA: cannot state neither the truth or falsity of facts not represented in the tuples of a relation • CWA: only the values actually present in a relational table and no other values represent facts of the real world.

26. Metrics for quantifying completeness (1) • Model without null values with OWA • Need a reference relation r’ for a relation r, that contains all the tuples that satisfy the schema of r C(r) = |r|/|ref(r)| Example: according to a registry of Lisbon municipality, the number of citizens is 2 million. If a company stores data about Lisbon citizens for the purpose of its business and that number is 1,400,000 then C(r) = 0,7

27. Metrics for quantifying completeness (2) • Model with null values with CWA: specific definitions for different granularities: • Values: to capture the presence of null values for some fields of a tuple • Tuple: to characterize the completeness of a tuple wrt the values of all its fields: • Evaluates the % of specified values in the tuple wrt the total number of attributes of the tuple itself Example: Student(stID, name, surname, vote, examdate) Equal to 1 for (6754, Mike, Collins, 29, 7/17/2004) Equal to 0.8 for (6578, Julliane, Merrals, NULL, 7/17/2004)

28. Metrics for quantifying completeness (3) • Attribute: to measure the number of null values of a specific attribute in a relation • Evaluates % of specified values in the column corresponding to the attribute wrt the total number of values that should have been specified. Example: For calculating the average of votes in Student, a notion of the completeness of Vote should be useful • Relations: to capture the presence of null values in the whole relation • Measures how much info is represented in the relation by evaluating the content of the info actually available wrt the maximum possible content, i.e., without null values.

29. Time-related dimensions • Currency: concerns how promptly data are updated Example: if the residential address of a person is updated (it corresponds to the address where the person lives) then the currency is high • Volatility: characterizes the frequency with which data vary in time Example: Birth dates (volatility zero) vs stock quotes (high degree of volatility) • Timeliness: expresses how current data are for the task in hand Example: The timetable for university courses can be current by containing the most recent data, but it cannot be timely if it is available only after the start of the classes.

30. Metrics of time-related dimensions • Last update metadata for currency • Straightforward for data types that change with a fixed frequency • Length of time that data remain valid for volatility • Currency + check that data are available before the planned usage time for timeliness

31. Consistency • Captures the violation of semantic rules defined over a set of data items, where data items can be tuples of relational tables or records in a file • Integrity constraints in relational data • Domain constraints, Key, inclusion and functional dependencies • Data edits: semantic rules in statistics

32. Evolution of dimensions • Traditional dimensions are Accuracy, Completeness, Timeliness, Consistency • With the advent of networks, sources increase dramatically, and data become often “found data”. • Federated data, where many disparate data are integrated, are highly valued • Data collection and analysis are frequently disconnected. • As a consequence we have to revisit the concept of DQ and new dimensions become fundamental.

33. Other dimensions • Interpretability: concerns the documentation and metadata that are available to correctly interpret the meaning and properties of data sources • Synchronization between different time series: concerns proper integration of data having different time stamps. • Accessibility: measures the ability of the user to access the data from his/her own culture, physical status/functions, and technologies availavle.

34. Relevant activities in DQ

35. Relevant activities in DQ • Standardization/normalization • Record Linkage/Object identification/Entity identification/Record matching • Data integration • Schema matching • Instance conflict resolution • Source selection • Result merging • Quality composition • Error localization/Data Auditing • Data editing-imputation/Deviation detection • Data profiling • Structure induction • Data correction/data cleaning/data scrubbing • Schema cleaning

36. Standardization/normalization • Modification of data with new data according to defined standards or reference formats Example: • Change “Bob” to “Robert” • Change of “Channel Str.” to “Channel Street”

37. Record Linkage/Object identification/ Entity identification/Record matching/Duplicate detection • Activity required to identify whether data in the same source or in different ones represent the same object of the real world

38. Data integration • Task of presenting a unified view of data owned by heterogeneous and distributed data sources • Two sub-activities: • Quality-driven query processing: task of providing query results on the basis of a quality characterization of data at sources • Instance-level conflict resolution: task of identifying and solving conflicts of values referring to the same real-world objects.

39. Instance-level conflict resolution • Instance level conflicts can be of three types: • representation conflicts, e.g. dollar vs. Euro • key equivalence conflicts, i.e. same real world objects with different identifiers • attribute value conflicts, i.e. Instances corresponding to same real world objects and sharing an equivalent key, differ on other attributes

40. Error localization/Data Auditing • Given one/two/n tables or groups of tables, and a group of integrity constraints/qualities (e.g. completeness, accuracy), find records that do not respect the constraints/qualities. • Data editing-imputation • Focus on integrity constraints • Deviation detection • data checking that marks deviations as possible data errors

41. Data Profiling • Evaluating statistical properties and intensional properties of tables and records • Structure induction of a structural description, i.e. “any form of regularity that can be found”

42. Data correction/data cleaning/data scrubbing • Given one/two/n tables or groups of tables, and a set of identified errors in records wrt to given qualities, generates probable corrections and correct the records, in such a way that new records respect the qualities.

43. Schema cleaning • Transform the conceptual schema in order to achieve or optimize a given set of qualities (e.g. Readability, Normalization), while preserving other properties (e.g. equivalence of content)

44. References • “Data Quality: Concepts, Methodologies and Techniques”, C. Batini and M. Scannapieco, Springer-Verlag, 2006 (Chapts. 1, 2, and 4). • “A Survey of Data Quality tools”, J. Barateiro, H. Galhardas, Datenbank-Spektrum 14: 15-21, 2005.

45. Next lectures • Data Cleaning and Transformation tools • The Ajax framework • Record Linkage • Data Fusion