Specification of Complex Structures in Distributed Service Function Chaining Using a YANG Data Model - Dräxler, Sevil; Karl, Holger (CoRR)

Dynamic Backhaul Network Configuration in SDN-based Cloud RANs - Dräxler, Martin; Karl, Holger (CoRR)

Pipelines for Ad-hoc Large-scale Text Mining - Wachsmuth, Henning
Today's web search and big data analytics applications aim to address information needs~(typically given in the form of search queries) ad-hoc on large numbers of texts. In order to directly return relevant information instead of only returning potentially relevant texts, these applications have begun to employ text mining. The term text mining covers tasks that deal with the inference of structured high-quality information from collections and streams of unstructured input texts. Text mining requires task-specific text analysis processes that may consist of several interdependent steps. These processes are realized with sequences of algorithms from information extraction, text classification, and natural language processing. However, the use of such text analysis pipelines is still restricted to addressing a few predefined information needs. We argue that the reasons behind are three-fold: First, text analysis pipelines are usually made manually in respect of the given information need and input texts, because their design requires expert knowledge about the algorithms to be employed. When information needs have to be addressed that are unknown beforehand, text mining hence cannot be performed ad-hoc. Second, text analysis pipelines tend to be inefficient in terms of run-time, because their execution often includes analyzing texts with computationally expensive algorithms. When information needs have to be addressed ad-hoc, text mining hence cannot be performed in the large. And third, text analysis pipelines tend not to robustly achieve high effectiveness on all texts, because their results are often inferred by algorithms that rely on domain-dependent features of texts. Hence, text mining currently cannot guarantee to infer high-quality information. In this thesis, we contribute to the question of how to address information needs from text mining ad-hoc in an efficient and domain-robust manner. We observe that knowledge about a text analysis process and information obtained within the process help to improve the design, the execution, and the results of the pipeline that realizes the process. To this end, we apply different techniques from classical and statistical artificial intelligence. In particular, we first develop knowledge-based approaches for an ad-hoc pipeline construction and for an optimal execution of a pipeline on its input. Then, we show theoretically and practically how to optimize and adapt the schedule of the algorithms in a pipeline based on information in the analyzed input texts in order to maximize execution efficiency. Finally, we learn patterns in the argumentation structures of texts statistically that remain strongly invariant across domains and that, thereby, allow for more robust analysis results in a restricted set of tasks. We formally analyze all developed approaches and we implement them as open-source software applications. Based on these applications, we evaluate the approaches on established and on newly created collections of texts for scientifically and industrially important text analysis tasks, such as financial event extraction and fine-grained sentiment analysis. Our findings show that text analysis pipelines can be designed automatically, which process only portions of text that are relevant for the information need at hand. Through scheduling, the run-time efficiency of pipelines can be improved by up to more than one order of magnitude while maintaining effectiveness. Moreover, we provide evidence that a pipeline's domain robustness substantially benefits from focusing on argumentation structure in tasks like sentiment analysis. We conclude that our approaches denote essential building blocks of enabling ad-hoc large-scale text mining in web search and big data analytics applications.

Challenges for Model-Integrating Components - Derakhshanmanesh , Mahdi; Ebert , Jürgen; Grieger, Marvin (Proceedings of the 2nd International Workshop on Model-Driven Engineering for Component-Based Software Systems co-located with ACM/IEEE 18th International Conference on Model Driven Engineering Languages & Systems (MoDELS 2015))

On Enabling Technologies for Longevity in Software - Derakhshanmanesh , Mahdi; Grieger, Marvin (Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering 2015)

On the Need for Transactional Models@Run.Time - Derakhshanmanesh , Mahdi; Grieger , Marvin; Ebert, Jürgen (Proceedings of the 10th Workshop on Models@run.time co-located with 18th International Conference on Model Driven Engineering Languages and Systems (MODELS 2015))

Perceived Autonomy in the First Semester of Mathematics Studies - Liebendörfer, Michael; Hochmuth, Reinhard (Proceedings of the Ninth Congress of the European Society for Research in Mathematics Education)

Initiative Bottom-Up-Nachtreffen zum Doktorandenkolloquium der GDM - Braun, Eileen Angélique; Liebendörfer, Michael; Schorcht, Sebastian (Mitteilungen der Gesellschaft für Didaktik der Mathematik)

Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction - Peitz, Sebastian; Dellnitz, Michael (PAMM)
n this article an efficient numerical method to solve multiobjective optimization problems for fluid flow governed by the Navier Stokes equations is presented. In order to decrease the computational effort, a reduced order model is introduced using Proper Orthogonal Decomposition and a corresponding Galerkin Projection. A global, derivative free multiobjective optimization algorithm is applied to compute the Pareto set (i.e. the set of optimal compromises) for the concurrent objectives minimization of flow field fluctuations and control cost. The method is illustrated for a 2D flow around a cylinder at Re = 100.

„Career Planning for dynamic Economies" – Individualisierung und Neue Medien als Anknüpfungspunkt für berufliche Orientierung im Rahmen des EU geförderten Projekts SELFIE - Beutner, Marc; Rose, S (Wirtschaft und Erziehung)