When datasets are collected and accessed legitimately, they must still be used appropriately according to policies, guidelines, rules, laws, and/or the (current) preferences of data subjects. Any inconsistency between the data collection and data usage processes can conflict with many principles of privacy like the transparency principle, no secondary use principle, or intended purpose usage principle. In this contribution we show how the usage control for the inner join operation in vertically separated relational datasets can be characterized as pre and post obligations of the Usage Control (UCON) model. This type of obligations is defined not only by the state of the UCON object (i.e., a dataset) itself, but also with respect to the state of another dataset. Such dependency on two datasets/objects provides a new insight in UCON obligation constructs when applied to the join operation. We describe also a mechanism to realize the identified obligation in a database management system.

Dealing with data quality related problems is an important issue that all organizations face in realizing and sustaining data intensive advanced applications. Upon detecting these problems in datasets, data analysts often register them in issue tracking systems in order to address them later on categorically and collectively. As there is no standard format for registering these problems, data analysts often describe them in natural languages and subsequently rely on ad-hoc, non-systematic, and expensive solutions to categorize and resolve registered problems. In this contribution we present a formal description of an innovative data quality resolving architecture to semantically and dynamically map the descriptions of data quality related problems to data quality attributes. Through this mapping, we reduce complexity – as the dimensionality of data quality attributes is far smaller than that of the natural language space – and enable data analysts to directly use the methods and tools.

The availability of real-life data sets is of crucial importance for algorithm and application development, as these often require insight into the specific properties of the data. Often, however, such data are not released because of their proprietary and confidential nature. We propose to solve this problem using the statistical technique of multiple imputation, which is used as a powerful method for generating realistic synthetic data sets. Additionally, it is shown how the generated records can be combined into networked data using clustering techniques.

A method, named Trendwatch, has been developed for explaining and predicting trends in which a structural break has occurred. Using this method it is possible to explain such a break in terms of causal networks of factors. The thus created explanation is validated using evidential arguments based on expert opinions. Moreover, this explanation is used to predict the future course of the trend after the break. To do so, experts are asked to predict the development of the factors at the beginning of the explanation.

In this paper, we have analyzed the complaint database of the National Ombudsman who handles complaints with regard to almost all government organizations in the Netherlands. We searched for trends with regard to complaints and handling of complaints during a period of 25 years. For this purpose, we have applied a combination of conventional database techniques and a genetic-based mining algorithm. Our experience is that such a combination and the guidance by domain experts are necessary to produce useful and interesting results.

New application domains cause today's database sizes to grow rapidly, posing great demands on technology. Data fragmentation facilitates techniques (like distribution, parallelization. and main-memory computing) meeting these demands. Also, fragmentation might help to improve efficient processing of query types such as top N. Database design and query optimization require a good notion of the costs resulting from a certain fragmentation. Our mathematically derived selectivity model facilitates this. Once its two parameters have been computed based on the fragmentation, after each (though usually infrequent) update, our model can forget the data distribution, resulting in fast and quite good selectivity estimation. We show experimental verification for Zipfian distributed IR databases.

Efficient, flexible, and scalable integration of full text information retrieval (IR) in a DBMS is not a trivial case. This holds in particular for query optimization in such a context. To facilitate the bulk-oriented behavior of database query processing, a priori knowledge of how to limit the data efficiently prior to query evaluation is very valuable at optimization time. The usually imprecise nature of IR querying provides an extra opportunity to limit the data by a trade-off with the quality of the answer. In this paper we present a mathematically derived model to predict the quality implications of neglecting information before query execution. In particular we investigate the possibility to predict the retrieval quality for a document collection for which no training information is available, which is usually the case in practice. Instead, we construct a model that can be trained on other document collections for which the necessary quality information is available, or can be obtained quite easily. We validate our model for several document collections and present the experimental results. These results show that our model performs quite well, even for the case were we did not train it on the test collection itself.

An operation in object-oriented databases gives rise to the processing of a path. Several database operations may result into the same path. The authors address the problem of optimal index configuration for a single path. As it is shown an optimal index configuration for a path can be achieved by splitting the path into subpaths and by indexing each subpath with the optimal index organization. The authors present an algorithm which is able to select an optimal index configuration for a given path. The authors consider a limited number of existing indexing techniques (simple index, inherited index, nested inherited index, multi-index, and multi-inherited index) but the principles of the algorithm remain the same adding more indexing techniques.>