The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. This workload-driven approach, however, has two major downsides. First, collecting the training data can be very expensive, since all queries need to be executed on potentially large databases. Second, training data has to be recollected when the workload or the database changes. To overcome these limitations, we take a different route and propose a new data-driven approach for learned DBMS components which directly supports changes of the workload and data without the need of retraining. Indeed, one may now expect that this comes at a price of lower accuracy since workload-driven approaches can make use of more information. However, this is not the case. The results of our empirical evaluation demonstrate that our data-driven approach not only provides better accuracy than state-ofthe- art learned components but also generalizes better to unseen queries.

This paper proposes a didactic approach to teaching model-driven software development (MDSD). The main idea is to convey the underlying concepts instead of using a concrete tool or presenting a purely theoretical approach. This goal is to be achieved by the development of a simple, but fully functional code generator. The entire process chain, from graphical modeling to actual code generation, is run through twice. In a first run from back to front to present the main concepts of a code generator engine and in a second run in reverse order to add additional functionality to the generator. At the end of the course, the acquired knowledge is to be transferred and presented by means of a small project using a concrete generator tool.

In modern production environments, decision support systems for flexible and scalable manufacturing of functional components have become a critical issue for economic success, especially for small and medium enterprises. Knowledge-based modelling of process chains has been effectively applied to the manufacturing of macro-scale products. However, modelling and therefore the process planning of manufacturing for micro scale products with tight tolerances and high accuracy proves to be very challenging. This paper proposes a methodology to support developers work in the field of micro manufacturing technologies. In particular, support for decision making process through workflow technologies as well as an example for managing technological processes based on a capability database is presented. The ontology allows for modelling and storage of technical capabilities, supporting product developers during the initial product design phase as well as the set-up of suitable manufacturing chains by taking into account different views (materials, technologies, tools, equipment) on a production process. This enables a highly flexible production system, allowing for a fast exchange of design variants and implementation of new manufacturing modules and techniques.

Object shape information is essential for robot manipulation tasks, in particular for grasp planning and collision-free motion planning. But in general a complete object model is not available, in particular when dealing with unknown objects. We propose a method for completing shapes that are only partially known, which is a common situation when a robot perceives a new object only from one direction. Our approach is based on the assumption that most objects used in service robotic setups have symmetries. We determine and rate symmetry plane candidates to estimate the hidden parts of the object. By finding possible supporting planes based on its immediate neighborhood, the search space for symmetry planes is restricted, and the bottom part of the object is added. Gaps along the sides in the direction of the view axis are closed by linear interpolation. We evaluate our approach with real-world experiments using the YCB object and model set [1].

When searching in a document collection by keywords, good auto-completion suggestions can be derived from query logs and corpus statistics. On the other hand, when querying documents which have automatically been linked to entities and semantic categories, auto-completion has not been investigated much. We have developed a semantic auto-completion system, where suggestions for entities and categories are computed in real-time from the context of already entered entities or categories and from entity-level co-occurrence statistics for the underlying corpus. Given the huge size of the knowledge bases that underlie this setting, a challenge is to compute the best suggestions fast enough for interactive user experience. Our demonstration shows the effectiveness of our method, and its interactive usability.