QOIS 2009 List of accepted papers

Fabian Panse and Norbert Ritter.

Completeness in Databases with Maybe-Tuples
Some data models use so-called maybe tuples to express the uncertainty, wether or not a tuple belongs to a relation. In order to assess this relation's quality the corresponding vagueness needs to be taken into account. Current metrics of quality dimensions are not designed to deal with this uncertainty and therefore need to be adapted. One major quality dimension is data completeness. In general, there are two basic ways to distinguish maybe tuples from definite tuples. First, an attribute serving as a maybe indicator (values YES or NO) can be used. Second, tuple probabilities can be specified. In this paper, the notion of data completeness is redefined w.r.t. both concepts. Thus, a more precise estimating of data quality in databases with maybe tuples (e.g., probabilistic databases) is enabled.

Kashif Mehmood and Samira Si-said Cherfi.

Evaluating the Functionality of Conceptual Models
Conceptual models serve as the blueprints of information systems and their quality plays decisive role in the success of the end system. It has been witnessed that majority of the IS change-requests results due to deficient functionalities in the information systems. Therefore, a good analysis and design method should ensure that conceptual models are functionally correct and complete as they are the communicating mediator between the users and the development team. Conceptual model is said to be functionally complete if it represents all the relevant features of the application domain and covers all the specified requirements. Our approach evaluates the functional aspects on multiple levels of granularity in addition to providing the corrective actions or transformation for improvement. This approach has been empirically validated by practitioners through a survey.

Laura Gonzalez, Veronika Peralta, Mokrane Bouzeghoub and Raul Ruggia.

Qbox-Services: Towards a Service-Oriented Quality Platform
The data quality market is characterized by a sparse offer of tools, providing individual functionalities which have their own interest with respect to quality assessment. But interoperating among these tools remains a technical challenge because of the heterogeneity of their models and access patterns. On the other side, quality analysts require more and more integration facilities that allow them to consolidate and aggregate multiple quality measures acquired from different observations. The QBox platform, developed within the Quadris project, aims at filling this gap by supplying a service-based integration infrastructure that allows interoperability among several quality tools and provides an OLAP-based quality model to support multidimensional analysis. This paper focuses on the architectural principles of this infrastructure and illustrates its use through specific examples of quality services.

Hendrik Decker, Davide Martinenghi.

Modeling, Measuring and Monitoring the Quality of Information
Semantic properties that reflect quality criteria for stored data can be modeled by integrity constraints. Violated instances of constraints , or causes thereof, may serve as a basis for measuring quality. Such measures also serve for monitoring and controlling quality impairment across changes.