The problem of Skyline computation has attracted considerable research attention in the last decade. A Skyline query selects those tuples from a dataset that are optimal with respect to a set of designated preference attributes. Since multicore processors are going mainstream, it has become imperative to develop parallel algorithms, which fully exploit the advantages of such modern hardware architectures. In this paper, the authors present high-performance parallel Skyline algorithms based on the lattice structure generated by a Skyline query. For this, they propose different evaluation strategies and compare several data structures for the parallel evaluation of Skyline queries. The authors present novel optimization techniques for lattice based Skyline algorithms based on pruning and removing one unrestricted attribute domain. They demonstrate through comprehensive experiments on synthetic and real datasets that their new algorithms outperform state-of-the-art multicore Skyline techniques for low-cardinality domains. The authors' algorithms have linear runtime complexity and fully play on modern hardware architectures.

SQL queries containing Group-by are common in data warehouse environments and OLAP. From this the concept of grouped Skyline queries emerged, wherein a Skyline of each group of tuples is requested. Grouped preference queries generalize this kind of Skyline queries. In this paper we present new algebraic transformation rules for grouped preference queries which are one of the most intuitive and practical type of queries. Our optimization laws reduce intermediate result sizes in the computation of joins, Cartesian products, and the preference selection. We have integrated these new rules into our rule-based Preference SQL query optimizer. Our performance benchmarks, building upon the well-known TPC-H and IMDB datasets, show that significant performance gains can be achieved.

Preferences are a well-established framework for database queries with soft constraints.Such queries select the best objects from large data sets according to a strict partial order induced by intuitive and semantically rich preference constructors.Together with functionality like grouping and aggregation, adapted from well-known database mechanisms, a very flexible preference framework has emerged in the last decade.In this paper we present R-Pref, an implementation of the preference framework in the statistical computing language R. R-Pref comprises less than 1000 lines of code and adheres to the formal foundations of preferences.It allows rapid prototyping of new preferences and related concepts.Exemplarily we present a use case in which a simple text mining example based on pattern matching is enriched by preferences.We argue that R-Pref paves the way for rapidly exploring new fields of application for preferences.Especially new semantic constructs for preference related operations together with equivalences of preference terms, being highly important for optimization, can be quickly evaluated.

Our demo application demonstrates a personalized location-based web application using Preference SQL that allows single users as well as groups of users to find accommodations in Istanbul that satisfy both hard constraints and user preferences. The application assists in defining spatial, numerical, and categorical base preferences and composes complex preference statements in an intuitive fashion. Unlike existing location-based services, the application considers spatial queries as soft instead of hard constraints to determine the best matches which are finally presented on a map. The underlying Preference SQL framework is implemented on top of a database, therefore enabling a seamless application integration with standard SQL back-end systems as well as efficient and extensible preference query processing.

Enabling mobility in urban and populous areas needs innovative tools and novel techniques for individual traffic planning. Represent a prototype of a traffic information system enabling personalized route planning plus advanced services like traffic jam alerting. The best routes are efficiently computed using the SR-Combine algorithm, subject to various user preferences and current traffic situation gathered dynamically from several Internet sources. We implemented a J2EE application server which smoothly adapts to distributed online processing, once high bandwidth networks like UTMS are available.

We present a new cooperative user interface for e-shopping. COSIMA is an intelligent Internet avatar with dynamic voice output that assists customers through their e-shopping tours and advises them like a real salesperson. COSIMA uses a meta search engine based on Preference SQL, computing best matching results to the customer's wishes. COSIMA can qualify these results and generates proper voice output. Our presentation shows COSIMA in action for comparison shopping.

A modern approach to manage multimedia databases is content-based retrieval. Especially in image databases graphical features like color, texture or shape can be used efficiently instead of expensive manual annotations. Due to the large number of image archives and the enormous costs of annotations it is most important to extract adequate features automatically by means of digital image processing. Using the image processing tool MVTec Halcon we propose a preprocessing for heraldic image segmentation that can automatically derive color information from monochrome images as well as semiautomatically segment heraldic bearings. We claim that exploiting application semantics leads to successful image segmentation in digital libraries.

We present a coherent modeling and reasoning methodology to extend object-oriented databases towards taxonomic and uncertain integrity constraints. Our so-called TOP database model enriches current ISA-hierarchies by more general tclasses to improve conceptual modeling. The t-classes themselves are then integrated with probabilistic constraints to express uncertainty. We give an efficient algorithm for checking the modeling-consistency of a probabilistic knowledgebase. As a typical application domain for TOP, we exemplify how various aspects of a portfolio management system can be modeled. We also demonstrate that recent probabilistic inference methods, relying on a careful interaction between taxonomic and uncertain knowledge, can be applied in this context. 1 Introduction The evolution of database technology from the relational model into object-oriented databases (OODBs) and deductive databases has been pushed forward to a state where stable and usable systems are becoming widely ...

A coherent approach is presented that extends relational and deductive database technology toward an integration of expert-system applications, which require sound and efficient capabilities to deal with uncertainty. Extending logic programming, the authors define the semantics of quantitative deductive databases, where fixpoint theory plays a central role. Calculus gives the rule programmer a great deal of flexibility to tailor the aggregation of certainties according to the application expertise at hand. Extending relational algebra, the authors also introduce a quantitative relational algebra as a suitable target language for rule compilation. This approach makes rule-based expert systems requiring uncertainty reasoning on large and complex data, feasible for a variety of practical application areas.>

As the performance demands imposed on relational database systems are steadily increasing, it becomes more difficult to master then with conventional database architectures. New architectural proposals suggesting the use of intelligent disc subsystems in combination with appropriate set-oriented interfaces look promising, because they can be realised by standard, high-performance hardware now. In particular, it makes sense to exploit the fast sequential read-out capabilities of modern discs. In such a changed environment the access-path selection problem, especially the question of whether index usage can speed up retrieval query execution, requires new solution approaches. Based on easily acquired performance data, an analytical model for restriction queries is developed that allows us to determines threshold hit ratios separating profitable from non-profitable index usage. As this model is founded on an extent-based file organisation, it is likewise applicable to conventional architectures permitting chained I/O. Simulation results for two common disc-pack drives are reported, showing that the bottleneck for random disc accesses sharpens for modern discs, and thus makes exhaustive relation scans preferable in more cases. However, indexes will still be mandatory on highly selective attributes to achieve satisfactory performance. The presented optimisation criteria are directly amenable to incorporation into a query optimiser.

Based on matured database technology the paper provides new insights into efficient ways to evaluate recursive deduction rules. We show how the forward-chaining approach to deduction can flexibly be married with goal-directed aspects of best/easiest-first strategies. From the natural fixpoint semantics of recursion we develop generally applicable differential iteration schemes that efficiently compute the fixpoint. Surprisingly the well-known Warshall-algorithm gets disclosed as a descendant of this class of algorithms. Performance measurements suggest the former as well as systolic Δ-algorithms with linear fixpoint equation as candidates for incorporating a transitive closure operator in databases. As a next important step towards integration of database technology and logic programming we suggest to profit from the standard features of concurrency control and transaction management by effectively using them for the synchronization of parallel deductions.