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 {cappiell|francala|pernici|plebani}@elet.polimi.it 2 Università di Roma, “La Sapienza”, Rome, Italy IASI-CNR, Rome, Italy monscan@dis.uniroma1.it

Outline Definitions of data quality dimensions Relevant data quality dimensions in CISs A Quality Management Architecture Data Quality Certificate Future work Cinzia Cappiello

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. Cinzia Cappiello

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 Cinzia Cappiello

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 Cinzia Cappiello

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] Cinzia Cappiello

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] Cinzia Cappiello

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. Cinzia Cappiello

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 Cinzia Cappiello

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 Cinzia Cappiello

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 Cinzia Cappiello

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 Cinzia Cappiello

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 Cinzia Cappiello