Patrick Roocks

Integration of magneto-optical active bismuth iron garnet on nongarnet substrates

For optical communication, high quality magneto-optical active iron garnet films such as Y3Fe5O12 are important ceramic systems with extensive applications, e.g., as optical isolators [H. Dotsch et al., J. Opt. Soc. Am. B 22, 240 (2005)], optical modulators, etc. Thereby, garnets stand out due to their high Faraday rotation and low optical losses in the near infrared. Currently, it is desirable to integrate such macroscopic optical components on a single chip (Si, SiO2, etc.) to build up optical circuits as in the case of microelectronics (integrated optics) or the use for magneto-optical imaging. Up to now, Bi3Fe5O12 shows the highest Faraday rotation over 20°∕μm. Unfortunately, Bi3Fe5O12 forms in a nonthermodynamical way. Thus, it can only be grown on garnet substrates which prevent it from direct deposition on substrates such as Si or SiO2. In our present work, we studied the integration of Bi3Fe5O12 on different SiO2 substrates using the pulsed laser deposition method. Therefore, we deposited an Y3Fe5...

An algebraic calculus of database preferences

Preference algebra, an extension of the algebra of database relations, is a well-studied field in the area of personalized databases. It allows modelling user wishes by preference terms; they represent strict partial orders telling which database objects the user prefers over other ones. There are a number of constructors that allow combining simple preferences into quite complex, nested ones. A preference term is then used as a database query, and the results are the maximal objects according to the order it denotes. Depending on the size of the database, this can be computationally expensive. For optimisation, preference queries and the corresponding terms are transformed using a number of algebraic laws. So far, the correctness proofs for such laws have been performed by hand and in a point-wise fashion. We enrich the standard theory of relational databases to an algebraic framework that allows completely point-free reasoning about complex preferences. This black-box view is amenable to a treatment in first-order logic and hence to fully automated proofs using off-the-shelf verification tools. We exemplify the use of the calculus with some non-trivial laws, notably concerning so-called preference prefilters which perform a preselection to speed up the computation of the maximal objects proper.

Scalagon: An Efficient Skyline Algorithm for All Seasons

Skyline queries are well-known in the database community and there are many algorithms for the computation of the Pareto frontier. The most prominent algorithms are based on a block-nested-loop style tuple-to-tuple comparison (BNL). Another approach exploits the lattice structure induced by a Skyline query over low-cardinality domains. In this paper, we present Scalagon, an algorithm which combines the ideas of the lattice approach and a BNL-style algorithm to evaluate Skylines on arbitrary domains. Since multicore processors are going mainstream, we also present a parallel version of Scalagon. We demonstrate through extensive experimentation on synthetic and real datasets that our algorithm can result in a significant performance advantage over existing techniques.

Composition and efficient evaluation of context-aware preference queries

This paper presents a modular approach to context-aware preference query composition based on a novel kind of preference generator. We introduce a constructive model to generate preference terms within the Preference SQL framework. Given several sources for preference related knowledge like explicit user input, information extracted from a preference repository, domain-specific application knowledge, location-based sensor data, or web service feeds for weather data our preference generator can compile a user search request into one rather complex context-aware Preference SQL query. Choosing as use case a commercial e-business platform for outdoor activities, we demonstrate how such queries despite the power and complexity of this approach can be evaluated efficiently on a practical data set.

Design and Implementation of a Framework for Context-Aware Preference Queries

In this paper we present a framework for a novel kind of context-aware preference query composition whereby queries for the Preference SQL system are created. We choose a commercial e-business platform for outdoor activities as a use case and develop a context model for this domain within our framework. The suggested model considers explicit user input, domain-specific knowledge, contextual knowledge and location-based sensor data in a comprehensive approach. Aside from the theoretical background of preferences, the optimization of preference queries and our novel generator based model we give special attention to the aspects of the implementation and the practical experiences. We provide a sketch of the implementation and summarize our user studies which have been done in a joint project with an industrial partner.

An algebra of database preferences

Abstract Preferences allow more flexible and personalised queries in database systems. Evaluation of such a query means to select the maximal elements from the respective database w.r.t. the preference, which is a partial strict-order. We present a point-free calculus of such preferences and exemplify its use in proving algebraic laws about preferences that can be used in query optimisation. We show that this calculus can be mechanised using off-the-shelf automated first-order theorem provers.

Exploring modal worlds

Abstract Modal idempotent semirings cover a large set of different applications. The paper presents a small collection of these, ranging from algebraic logics for program correctness over bisimulation refinement, formal concept analysis, database preferences to feature oriented software development. We provide new results and/or views on these domains; the modal semiring setting allows a concise and unified treatment, while being more general than, e.g., standard relation algebra.

Algebraic optimization of grouped preference queries

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.

Preference Decomposition and the Expressiveness of Preference Query Languages

Preferences in the scope of relational databases allow modeling user wishes by queries with soft constraints. There are different frameworks for database preferences including commercially available systems. They slightly vary in semantics and expressiveness but have in common that preferences induce strict partial orders on a given data set. In the present paper we study the expressiveness of preference operators in the available implementations. Particularly, we search for decompositions of strict partial orders into fundamental preference constructs. We study which preference operators and operands are necessary to express any strict partial order. Finally, we present two decomposition algorithms and show their correctness.