Presentation on theme: "Data Cleaning and Transformation Helena Galhardas DEI IST (based on the slides: “A Survey of Data Quality Issues in Cooperative Information Systems”, Carlo."— Presentation transcript:
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))
Agenda Introduction Data Quality Problems Data Quality Dimensions Relevant activities in Data Quality
When materializing the integrated data (data warehousing)… Data Extraction Data Loading Data Transformation... SOURCE DATA TARGET DATA ETL: Extraction, Transformation and Loading 70% of the time in a datawarehousing project is spent with the ETL process
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
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
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)
Data Quality Data Cleaning Data Integration Data Mining Statistical Data Analysis Management Information Systems Record Matching(deduplication) Data Transformation … Error Localization DB profiling Patterns in text strings … Conflict Resolution Record Matching … Source Selection Source Composition Query Result Selection Time Syncronization … Assessment Process Improvement Tradeoff Cost/Optimization … Knowledge Representation Research issues related to DQ Edit- imputation Record Linkage … Conflict Resolution …
EGov Scientific Data Web Data … Research areas in DQ systems Application Domains Research areas Models Measurement/Improvement Techniques Dimensions Measurement/Improvement Tools and Frameworks Methodologies
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
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
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) or data quality tool
Typical architecture of a DQ system Human Knowledge Human Knowledge Data Extraction Data Loading Data Transformation Metadata Dictionaries Data Analysis Schema Integration... SOURCE DATA TARGET DATA Data Transformation
Data Quality problems
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.
Data quality problems (1/3) Schema level data quality problems prevented with better schema design, schema translation and integration. Instance level data quality problems errors and inconsistencies of data that are not prevented at schema level
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 Data quality problems (2/3)
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 Data quality problems (3/3)
Data Quality Dimensions
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
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 or domain Coincides with correctness The corresponding true value has to be known
Ganularity of accuracy definition Accuracy may refer to: a single value of a relation attribute an attribute or column a relation the whole database
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
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
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.
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
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)
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.
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.
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
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
Evolution of dimensions Traditional dimensions are Accuracy, Completeness, Timeliness, Consistency 1. With the advent of networks, sources increase dramatically, and data become often “found data”. 2. Federated data, where many disparate data are integrated, are highly valued 3. Data collection and analysis are frequently disconnected. As a consequence we have to revisit the concept of DQ and new dimensions become fundamental.
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.
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
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”
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
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.
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
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
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”
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.
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)
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.
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