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Zeit: Montag, 13. Juli 2020, 16:00 Uhr
Zoom:
<https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09>
https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09
Meeting-ID: 956 7645 5814
Passwort: 302988
Referentin: M.Eng. Rihan Hai
Lehrstuhl Informatik 5
Thema: Data integration and Metadata Management in Data Lakes
Abstract:
Although big data has been discussed for some years, it still has many
research challenges, such as the variety of data. Non-integrated data
management systems with heterogeneous schemas, query languages, and data
models result in information silos. As traditional 'schema-on-write'
approaches such as data warehouses cannot solve the challenges to
efficiently integrate, access, and query the information silos, data lake
systems have been proposed as a solution to this problem. Data lakes are
repositories storing raw data in its original format and providing a common
access interface.
In this thesis, we present a comprehensive and flexible data lake
architecture and the prototype system Constance. First, we propose a native
mapping representation to capture the hierarchical structures of nested
mappings and efficient mapping generation algorithms. Second, to provide a
unified querying interface, we design a novel query rewriting engine that
combines logical methods for data integration based on declarative mappings
with the big data processing system Apache Spark. Third, we also study the
formalism of the generated schema mappings as dependencies. Our algorithmic
approach transforms schema mappings expressed in second-order logic to their
logically equivalent first-order forms. Finally, we introduce
clustering-based algorithms to discover relaxed functional dependencies,
which enrich the metadata and improve data quality in the data lake.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Dienstag, 30. Juni 2020, 14.00-15.00 Uhr
Zoom:
<https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09>
https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09
Meeting-ID: 956 7645 5814
Passwort: 302988
Referent: Dipl.-Kfm. Markus Beutel
Thema: End-to-End-Integration von komplementären Mobilitätsdienstleistungen
durch unternehmensübergreifende Anbieterkooperation
Abstract:
Reisenden steht heutzutage eine Vielzahl unterschiedlicher Verkehrsmodi zur
Verfügung. Dabei können sich viele Fortbewegungsmittel aufgrund
individueller Charakteristika gegenseitig ergänzen, anstatt sich zu
substituieren. Durch eine zumindest in Teilen segmentierte und
anbieterspezifische Bereitstellung von Verkehrsdienstleistungen können
beispielsweise ökonomische und ökologische Ineffizienzen entstehen. Im
Zentrum einer vollständigen Digitalisierung und Integration heterogener
Mobilitätsdienstleistungen sollte daher der ganzheitliche
Dienstleistungsprozess, über die gesamte Servicekette hinweg, in den
Mittelpunkt gestellt werden.
Das übergeordnete Ziel dieser Arbeit besteht in der Erforschung einer
integrierten Bereitstellung sich ergänzender Mobilitätsdienstleistungen,
über Unternehmensgrenzen hinweg. Ausgangspunkt dieser Arbeit bildet die
Untersuchung einer spezifischen Mobilitätsplattform im Hinblick auf
verschiedene Integrationsfaktoren. Auf Basis einer umfassenden Analyse von
Mobilitätsplattformen und in Verbindung mit der Beschreibung eines
organisatorischen Rollenmodells werden daraufhin mögliche
Anbieterkooperationsszenarien beschrieben. Um die unternehmensübergreifende
Integration auf Prozessebene zu betrachten, wird schließlich ein Ansatz zur
Erweiterung eines Softwarewerkzeugs zur Fusion von Geschäftsprozessmodellen
evaluiert.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Freitag, 08. Mai 2020, 11.00 Uhr
Zoom:
https://rwth.zoom.us/j/94637532047?pwd=aVlweVRLdGU0OWNjdEh4TmJhSktrUT09
Referent: Dipl.-Medieninf. István Koren
Lehrstuhl Informatik 5
Thema: DevOpsUse: Community-Driven Continuous Innovation of Web Information
Infrastructures
Abstract:
The steady evolution of the Web over the last thirty years was shaped by an
interplay of new technologies and innovative applications. The current
challenges are caused by the ongoing digital transformation of whole
societies. In Industry 4.0 for example, these are changing workplace
settings and the adoption of the Internet of Things. Inhibiting the demanded
fast innovation cycles, this may create a disruptive and unstable
environment in which the requirements of heterogeneous professional
communities need to be addressed.
Information systems infrastructure, while only partially visible and thus
hard to grasp, has a strong influence on practices in professional
communities. Therefore, our aim is to stabilize the dichotomies apparent in
the Web by means of an agile information systems development methodology. It
supports the evolution of infrastructure through community-driven and
model-based technologies to guide it on a sustainable path of continuous
innovation. Agile development practices in software engineering, in
particular the already established DevOps approach, promote stronger
cooperation between development and operating teams. Our DevOpsUse
methodology additionally fosters a stronger involvement of end user
communities in software development processes by including them in the
process of infrastructuring, i.e. the appropriation of infrastructure during
its usage.
The developed DevOpsUse methodology has been successfully validated by the
transitions between three generations of technologies: near real-time
peer-to-peer Web architectures, edge computing, and the Internet of Things.
In particular, we were able to demonstrate our methodologys capabilities
through longitudinal studies in several large-scale international
digitalization projects. Beyond Web information systems, the framework and
its open source tools are applicable in further innovative areas like mixed
reality. Its broad adaptability testifies that DevOpsUse has the potential
to unlock sustainable innovation capabilities.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Dienstag, 05. Mai 2020, 14.00 Uhr
Zoom:
https://us02web.zoom.us/j/84813327259?pwd=Y2NydlRMRzE1dkpkcmpERkFwMWZYZz09
Referent: Kazuki Irie, M.Sc.
Thema: Advancing Neural Language Modeling in Automatic Speech Recognition//
Abstract:
Statistical language modeling is one of the fundamental problems in
natural language processing. In the recent years, language modeling has
seen great advances by active research and engineering efforts in
applying artificial neural networks, especially those which are
recurrent. The application of neural language models to speech
recognition has now become well established and ubiquitous. Despite this
impression of some degree of maturity, we claim that the full potential
of the neural network based language modeling is yet to be explored. In
this thesis, we further advance neural language modeling in automatic
speech recognition, by investigating a number of new perspectives. From
the architectural view point, we investigate the newly proposed
Transformer neural networks for language modeling application. The
original model architecture proposed for machine translation is studied
and modified to accommodate the specific task of language modeling.
Particularly deep models with about one hundred layers are developed. We
present an in-depth comparison with the state-of-the-art recurrent
neural network language models based on the long short-term memory.
While scaling up language modeling to larger scale datasets, the
diversity of the data emerges as an opportunity and a challenge. The
current state-of-the-art neural language modeling lacks a mechanism of
handling diverse data from different domains for a single model to
perform well across different domains. In this context, we introduce
domain robust language modeling with neural networks, and propose two
solutions. As a first solution, we propose a new type of adaptive
mixture of experts model which is fully based on neural networks. In the
second approach, we investigate knowledge distillation from multiple
domain expert models, as a solution to the large model size problem seen
in the first approach. Methods for practical applications of knowledge
distillation to large vocabulary language modeling are proposed, and
studied to a large extent.
Finally, we investigate the potential of neural language models to
leverage long-span cross-sentence contexts for cross-utterance speech
recognition. The appropriate training method for such a scenario is
under-explored in the existing works. We carry out systematic
comparisons of the training methods, allowing us to achieve improvements
in cross-utterance speech recognition. In the same context, we study the
sequence length robustness for both recurrent neural networks based on
the long short-term memory and Transformers, because such a robustness
is one of the fundamental properties we wish to have, in neural networks
with the ability to handle variable length contexts. Throughout the
thesis, we tackle these problems through novel perspectives of neural
language modeling, while keeping the traditional spirit of language
modeling in speech recognition.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
--
Stephanie Jansen
Faculty of Mathematics, Computer Science and Natural Sciences
HLTPR - Human Language Technology and Pattern Recognition
RWTH Aachen University
Ahornstraße 55
D-52074 Aachen
Tel. Frau Jansen: +49 241 80-216 06
Tel. Frau Andersen: +49 241 80-216 01
Fax: +49 241 80-22219
sek(a)i6.informatik.rwth-aachen.de
www.hltpr.rwth-aachen.de
Tel: +49 241 80-216 01/06
Fax: +49 241 80-22219
sek(a)i6.informatik.rwth-aachen.de
www.hltpr.rwth-aachen.de
Hallo,
Matthias Wichtlhuber vom DeCIX hält nächsten Dienstag für die Aachener
Informatik einen Vortrag über Gegenmaßnahmen zu
Distributed-Denial-of-Service (DDoS) attacks am DeCIX und über die
Traffic Veränderungen am DeCIX aufgrund des COVID-19 lockdowns.
Wann: Dienstag 24.4. 11:00
Zoom Meeting ID: 916 2538 0906, Password: 091338
https://rwth.zoom.us/j/91625380906?pwd=cEJCSk01VkVEUUpTSjgzYTlnNk5Odz09
Abstract des Vortrags:
We will start with a short introduction of Internet Exchange Points
(IXPs) and their operations. With this background, we will discuss the
topic of Distributed-Denial-of-Service (DDoS) attacks at IXPs. We
present results of a recent measurement study of DDoS-for-hire Websites
(Booters) and the traffic effects of a seizure operation of 15 Booter
websites by the FBI in late 2018 [1]. Subsequently, we introduce the
concept of Advanced Blackholing, a published and operational mechanism
designed by the DE-CIX research team to defend IXP links against DDoS
attacks [2]. Due to the current COVID-19 situation, we will close the
presentation by showing some preliminary results on the traffic shifts
caused by nation-wide lockdowns in several countries.
Über Matthias Wichtlhuber:
Matthias Wichtlhuber holds a Diploma in Information Systems from
Universität Mannheim and a Ph. D. from Technische Universität Darmstadt.
His Ph. D. thesis focused on optimizing content delivery on the
Internet. During his Ph. D. he worked on numerous EU and nationally
funded research projects as well as industrial projects. After his
studies, he joined DE-CIX, the operator of the largest Internet Exchange
Point (IXP) in the world in Frankfurt. He is a member of the DE-CIX
research team and works on product development, system security, future
network architectures for Internet exchange points, and large-scale
network data analysis.
[1] Kopp, D., Wichtlhuber, M., Poese, I., Santanna, J., Hohlfeld, O., &
Dietzel, C.: “DDoS Hide & Seek: On the Effectiveness of a Booter
Services Takedown”, ACM IMC, 2019.
[2] Dietzel, C., Wichtlhuber, M., Smaragdakis, G., & Feldmann, A.:
“Stellar: Network Attack Mitigation using Advanced Blackholing”, ACM
CoNEXT, 2018.
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Zeit: Montag, 23. Maerz 2020, 10.00 Uhr
Ort: Raum 115, Rogowski-Gebaeude,
Referent: Markus Hoehnerbach M.Sc.
High-Performance and Automatic Computing
Thema: A Framework for the Vectorization of Molecular Dynamics Kernels
Abstract:
We introduce a domain-specific language (DSL) for many-body potentials, which are used in molecular dynamics (MD) simulations in the area of materials science. We also introduce a compiler to translate the DSL into high-performance code suitable for modern supercomputers.
We begin by studying ways to speedup up potentials on supercomputers using two case studies: The Tersoff and the AIREBO potentials. In both case studies, we identify a number of optimizations, both domain-specific and general, to achieve speedups of up to 5x; we also introduce a method to keep the resulting code performance portable.
During the AIREBO case study, we also discover that the existing code contains a number of errors. This experience motivates us to include the derivation step, the most error-prone step in manual optimization, in our automation effort.
After having identified beneficial optimization techniques, we create a ``potential compiler'', short PotC, which generates fully-usable performance-portable potential implementations from specifications written in our DSL. DSL code is significantly shorter (20x to 30x) than a manual code, reducing both manual work and opportunities to introduce bugs.
We present performance results on five different platforms: Three CPU platforms (Broadwell, Knights Landing, and Skylake) and two GPU platforms (Pascal and Volta). While the performance in some cases remains far below that of hand-written code, it also manages to match or exceed manually written implementations in other cases. For these cases, we achieve speedups of up to 9x compared to non-vectorized code.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Montag, 16. März 2020, 10:45 Uhr
Ort: Raum 9222, Gebäude E3, Ahornstr. 55
Referentin: Sandra Kiefer, M.Sc.
Lehrstuhl Informatik 7
Thema: Power and Limits of the Weisfeiler-Leman Algorithm
Abstract:
The Weisfeiler-Leman (WL) algorithm is a fundamental combinatorial procedure used to classify graphs and other relational structures. Through its connections to many research areas such as logics and machine learning, surprising characterisations of the algorithm have been discovered. We combine some of these to obtain powerful proof techniques.
For every k, the k-dimensional version of the algorithm (k-WL) iteratively computes a stable colouring of the vertex k-tuples of the input graph. The larger k, the more powerful k-WL becomes with respect to the distinguishability of graphs.
We have studied two central parameters of the algorithm, its number of iterations until stabilisation and its dimension. The results enable a precise understanding of 1-WL, namely we have determined its iteration number and have developed a complete characterisation of the graphs for which 1-WL correctly decides isomorphism.
In higher dimensions, however, the situation is different. For example, it is often not clear at all how to decide if k-WL distinguishes two particular graphs. By our results, 3-WL identifies every planar graph, which drastically improves upon all previously known bounds. Generalising this insight, we obtain the first explicit parametrisation of the WL dimension by the Euler genus of the input graph.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Donnerstag, 12. März 2020, 10:45 Uhr
Ort: Raum 9222, Gebäude E3, Ahornstr. 55
Referent: Gereon Kremer, M.Sc.
Theory of Hybrid Systems
Thema: Cylindrical Algebraic Decomposition for Nonlinear Arithmetic
Problems
Abstract:
We explore the usage of the cylindrical algebraic decomposition method
for satisfiability modulo theories solving, both theoretically and
experimentally. This method is commonly understood as an almost atomic
procedure that gathers information about an algebraic problem and then
allows to answer all kinds of questions about this algebraic problem
afterwards. We essentially break up this method into smaller components
that we can then process in varying order to derive the particular piece
of information – whether the problem is satisfiable or unsatisfiable –
allowing to avoid some amount of computations. As this method often
exhibits doubly exponential running time, these savings can be
very significant in practice.
We then turn to an alternative approach to satisfiability modulo
theories solving commonly called model-constructing satisfiability
calculus. The core idea of this framework is to integrate the theory
reasoning, in particular the construction of a theory model, very
tightly with the Boolean reasoning. The main theory reasoning engine is
again based on the cylindrical algebraic decomposition method, though we
focus more on the overall framework here.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
Gereon Kremer
Lehr- und Forschungsgebiet Theorie Hybrider Systeme
RWTH Aachen
Tel: +49 241 80 21243
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Zeit: Dienstag, 10. März 2020, 9.30 Uhr
Ort: Raum 2222, Ahornstr. 55 (Informatikgebäude, Hauptbau; Seminarraum i10)
Referent: Christian Corsten, M.Sc.
Lehrstuhl Informatik 10
Thema: Use the Force: How Force Touch Improves Input on Handheld Touchscreens
Abstract:
Handheld devices, such as smartphones, have become essential tools in our everyday life. We use them, e.g., to contact people, browse the web, or take pictures. For whatever use, to interact with the handheld device, we hold it with one or two hands and touch with our fingers on the built-in touchscreen. However, this interaction is often constrained to simple contact between the finger and the flat display glass, although touch offers further, richer properties. One such rich property is the intensity of a touch, i.e., its force, that the user applies with every tap to the touchscreen. Incorporating this property into the user’s interaction with the handheld device enables her to become more expressive with every single touch. In this thesis, we present a series of interaction techniques that take advantage of force touch input to make handheld interaction more efficient:
When holding the device with two hands in landscape orientation, most of the fingers are unavailable for interaction, since they rest at the back of the device (BoD), holding it in place. Using BoD force input, we can make efficient use of these fingers without sacrificing stability of the device grip. Our technique, BackXPress, enables quick access to shortcuts and menus to augment users’ touch interaction with the frontal screen.
For single-handed device use, users can only use their thumb to interact with the frontal touchscreen but cannot reach everywhere without re-grasping the device. Our virtual thumb extension, ForceRay, lets the user cast a ray at unreachable targets and control a cursor on that ray that moves closer to these targets the more force is applied. The technique is ergonomic for the thumb and enables users to maintain a steady device grip. Targets located at the screen edges, like menus and navigation buttons, are acquired quickly.
Selection of values from long ordered lists, such as picking a date or time, can also be sped up when using force input. With our Force Picker, users scroll through the value range at various speeds, with the speed being coupled to the force exerted by the thumb. Prior rolling of the thumb to the left or right sets the scrolling direction. Compared to touch-based pickers, our Force Picker not only makes selection faster, but also only consumes little screen space since the gesture footprint for force input is much smaller.
While controlling force via fingers requires practice, we show that with training and algorithmic optimizations, users become quickly familiar with force input and gain the benefits of the added expressiveness for handheld interaction.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Freitag, 21. Februar 2020, 9.00 Uhr
Ort: IT Center Erweiterungsbau, Kopernikusstraße 6, S003
Referent: Daniel Zielasko, M.Sc.
Lehrstuhl Informatik 12
Thema: DeskVR: Seamless Integration of Virtual Reality into Desk-based Data
Analysis Workflows
Abstract:
In this work, we are looking into the possibilities and unique challenges
virtual reality today offers for (office) desk-based scenarios, as
they are ubiquitous in data analysis.
We characterize the scenario, introduce the term deskVR, and name
the technical challenges that come with it. Furthermore, we tackle
specific demands in two pillars of interaction in virtual reality,
selection & manipulation, and navigation. Then, we investigate
passive and active methods to prevent and reduce cybersickness, as
for us, tackling cybersickness is one if not the most critical tasks
that have to be solved to integrate virtual reality into everyday life
successfully. Finally, we apply the methods and findings made in this
thesis to a prototypical application framework for immersive 3D
graph exploration, serving as proof of concept for the integrability of
virtual reality into desk-based working scenarios. In the graph
visualization domain, we then also propose new vertex positioning
and edge bundling methods that address challenges arising with the
performed up-projection into 3D interactive space.
Es laden ein: die Dozentinnen und Dozenten der Informatik