1. Data Quality Assurance in Cooperative Information Systems: a Multi-dimension Quality Certificate
Cinzia Cappiello1, Chiara Francalanci1, Barbara Pernici1, Pierluigi Plebani1, Monica Scannapieco2
1 Politecnico di Milano, Milano, Italy
2 Università di Roma, “La Sapienza”, Rome, Italy
IASI-CNR, Rome, Italy

2. Outline Definitions of data quality dimensions
Relevant data quality dimensions in CISs
A Quality Management Architecture
Data Quality Certificate
Future work
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3. Definitions of data quality dimensions
The data quality literature provides a thorough classification of data quality dimensions.
There is not general agreement on the definition of most dimensions.
The selected definitions are founded on a survey of the quality dimensions proposed in the literature over the past 10 years [Catarci, Scannapieco 2002].
On the basis of this classification a basic set of data quality dimensions is defined including accuracy, completeness, consistency, timeliness, interpretability and accessibility; which represent the dimensions considered by the majority of the authors.
Timeliness is considered together with the other time related dimensions: currency and volatility.
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4. Relevant data quality dimensions in CISs
Category
Major dimensions
Sub dimensions
Object Dimensions
Accuracy
Completeness
Consistency
Subject Dimensions
Interpretability
Architectural Dimensions
Reliability
Accessibility
Process Dimensions
Timeliness
Volatility
Currency
Currency Level
Security/Access Security
Relevance
History
Cost
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5. Object dimensions
Accuracy: “a measure of the proximity of a data value v to some other value v’ that are considered correct” [Redman 1996]
Completeness: “degree to which specific values are included in a data collection” [Wang & Strong 1996]
Consistency: it is defined at three levels [Redman 1996]
View consistency
Value consistency
Representation consistency
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6. Subject and architectural dimensions
Interpretability: it is related to the format in which data are specified and to the clarity of data definitions [Wang &Strong 1996]
Reliability: it can be defined at two levels: data reliability and source reliability. Data are considered reliable if they can be trusted to convey the right information. Source reliability is calculated considering the reputation of the source. [Wand &Wang 1996]
Accessibility: “the degree in which data are available or quickly or easily retrievable”. [Wang & Strong 1996]
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7. Process dimensions(1)
Timeliness: “the extent to which the age of data is appropriate for the task at hand”. A possible measure [Ballou 1998]:
Currency: “the time interval between the latest update of a data value and the time it is used”
Currency level: specifies the degree to which a data set is up-to-date [Cappiello, Francalanci, Pernici 2002]
Volatility: it is defined as the temporal dynamics of Expiration which is the time until data remain valid.
Volatility is a function that measures the probability that the expiration time will change within the interval between publication and expiration time [Pernici, Scannapieco 2002]
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8. Process dimensions (2)
Security/Access Security: it is defined as “the extent to which access to data can be restricted and hence kept secure” [Wang & Strong 1996].
We have listed the security requirements that should be satisfied to assure data security.
The percentage of satisfied requirements in IS can be a measure of the value of this dimension.
Relevance: it is a measure of the appropriateness of the data extracted for the requested task.
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9. Process dimensions (3)
History: the storage of what operations of quality improvement have been performed on data allows to build a certificate in which all the operations that have modified data are listed. For each operation has to be stored:
Type of operation
Execution date
Percentage of improvement
Cost: Dimensions that is able to evaluate the cost impact of the errors due to bad data quality
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10. A Quality Management Architecture
Information System
DBMS
Quality
Factory
Software
Application
Organization Infrastructure
Data Quality Broker
Common Data Quality Service
Applications
Request/Response
CIS Data Quality
Repository
Common Data Quality
Databases
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11. The Quality Factory
Translates the request into a format comprehensible to the IS
Identifies the required data and extracts them from the Data Repository
Identifies which data and data quality dimensions have been evaluated
Using internal measurement tools, performs a static analysis of the values of the data quality dimension
If data values do not satisfy quality requirements quality assessment sends an alert message to the Monitoring module
Associates a quality certificate with data that satisfy quality requirements
Stores the events in which data quality requirements are not satisfied
Executes periodical monitoring operations on the data contained in Data Repository
Translates the response into a format comprehensible to the user
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12. Data Quality Certificate
Stores the value associated to each quality dimensions adopted
Contains sensitivity information
Denotes the level of confidentiality of data being transferred
It is owned by the source organization that provides the authentication of the data source
Provides the integrity of the transmitted data
Exchange unit format
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13. Future work
Software implementation of quality factory architecture and data quality certificate
Application of the data quality certificate to evaluate the quality of Web services in a cooperative environment
Evaluation of the impact of data replication and distribution on data quality dimensions
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