Roland Kluge

On the Role of Discourse Markers for Discriminating Claims and Premises in Argumentative Discourse

This paper presents a study on the role of discourse markers in argumentative discourse. We annotated a German corpus with arguments according to the common claim-premise model of argumentation and performed various statistical analyses regarding the discriminative nature of discourse markers for claims and premises. Our experiments show that particular semantic groups of discourse markers are indicative of either claims or premises and constitute highly predictive features for discriminating between them.

A systematic approach to constructing families of incremental topology control algorithms using graph transformation

In the communication system domain, constructing and maintaining network topologies via topology control algorithms is an important crosscutting research area. Network topologies are usually modeled using attributed graphs whose nodes and edges represent the network nodes and their interconnecting links. A key requirement of topology control algorithms is to fulfill certain consistency and optimization properties to ensure a high quality of service. Still, few attempts have been made to constructively integrate these properties into the development process of topology control algorithms. Furthermore, even though many topology control algorithms share substantial parts (such as structural patterns or tie-breaking strategies), few works constructively leverage these commonalities and differences of topology control algorithms systematically. In previous work, we addressed the constructive integration of consistency properties into the development process. We outlined a constructive, model-driven methodology for designing individual topology control algorithms. Valid and high-quality topologies are characterized using declarative graph constraints; topology control algorithms are specified using programmed graph transformation. We applied a well-known static analysis technique to refine a given topology control algorithm in a way that the resulting algorithm preserves the specified graph constraints. In this paper, we extend our constructive methodology by generalizing it to support the specification of families of topology control algorithms. To show the feasibility of our approach, we reengineering six existing topology control algorithms and develop e-kTC, a novel energy-efficient variant of the topology control algorithm kTC. Finally, we evaluate a subset of the specified topology control algorithms using a new tool integration of the graph transformation tool eMoflon and the Simonstrator network simulation framework.

TARL: modeling topology adaptations for networking applications

Many networking applications implement topology adaptations to cope with network dynamics. Related work focuses on the specific application, lacking a general model for topology adaptations. In this paper, we analyze 14 topology adaptations from two different application domains. Based on the derived characteristics, we propose a general topology adaptation model. We present the Topology Adaptation Rule Language (TARL) to specify topology adaptation logic following this model. We discuss the execution of TARL rules for two application domains as well as how our model enables reasoning and optimizations on topology adaptations. For the evaluation, we developed a TARL runtime environment as a reusable topology adaptation framework. TARL simplifies the development of topology adaptations and is able to express 13 of the analyzed algorithms.

A systematic approach to constructing incremental topology control algorithms using graph transformation

Communication networks form the backbone of our society. Topology control algorithms optimize the topology of such communication networks. Due to the importance of communication networks, a topology control algorithm should guarantee certain required consistency properties (e.g., connectivity of the topology), while achieving desired optimization properties (e.g., a bounded number of neighbors). Real-world topologies are dynamic (e.g., because nodes join, leave, or move within the network), which requires topology control algorithms to operate in an incremental way, i.e., based on the recently introduced modifications of a topology. Visual programming and specification languages are a proven means for specifying the structure as well as consistency and optimization properties of topologies. In this paper, we present a novel methodology, based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints. More specifically, we model the possible modifications of a topology control algorithm and the environment using graph transformation rules, and we describe consistency and optimization properties using graph constraints. On this basis, we apply and extend a well-known constructive approach to derive refined graph transformation rules that preserve these graph constraints. We apply our methodology to re-engineer an established topology control algorithm, kTC, and evaluate it in a network simulation study to show the practical applicability of our approach.

CardyGAn: Tool Support for Cardinality-based Feature Models

Cardinality-based feature models (CFM) constitute a crucial and non-trivial extension to FODA feature models in terms of UML-like feature multiplicities and corresponding cardinality constraints. CFM allow for specifying configuration choices of software systems incorporating multiple instances (copies) of features, e.g., for tailoring customer-specific and even potentially unrestricted application resources. Nevertheless, the improved expressiveness of CFM compared to FODA feature models complicates configuration semantics, including sub-tree cloning and potentially unbounded configuration spaces. As a consequence, entirely novel anomalies might arise such as dead cardinality intervals, false unboundedness, and cardinality gaps, which are not properly treated by recent feature-modeling tools. In this paper, we present comprehensive tool support for assisting specification, validation, and configuration of CFM. Our tool CARDYGAN, therefore, incorporates capabilities for CFM editing, automated CFM validation including anomaly detection based on a combination of ILP and SMT solvers, as well as a CFM configuration engine based on ALLOY.

A Methodology for Designing Dynamic Topology Control Algorithms via Graph Transformation

This paper presents a constructive, model-driven methodology for designing dynamic topology control algorithms. The proposed methodology characterizes valid and high quality topologies with declarative graph constraints and formulates topology control algorithms as graph transformation systems. Afterwards, a well-known static analysis technique is used to enrich graph transformation rules with application conditions derived from the graph constraints to ensure that this improved approach always produces topologies that i are optimized wrt. to a domain-specific criterion, and ii additionally fulfill all the graph constraints.

Distributed Graph-based Topology Adaptation using Motif Signatures.

A motif is a small graph pattern, and a motif signature counts the occurrences of selected motifs in a network. The motif signature of a real-world network is an important characteristic because it is closely related to a variety of semantic and functional aspects. In recent years, motif analysis has been successfully applied for adapting topologies of communication networks: The motif signatures of very good networks (e.g., in terms of load balancing) are determined a priori to derive a target motif signature. Then, a given network is adapted in iterative steps, subject to side constraints and in a distributed way, such that its motif signature approximates the target motif signature. In this paper, we formalize this adaptation problem and show that it is N P-hard. We present LoMbA, a generic approach for motif-based graph adaptation: All types of networks, all selections of motifs, and all types of consistency-maintaining constraints can be incorporated. To evaluate LoMbA, we conduct a simulation study based on several scenarios of topology adaptation from the domain of communication networks. We consider topology control in wireless ad-hoc networks, balancing of video streaming trees, and load balancing of peer-to-peer overlays. In each considered application scenario, the simulation results are remarkably good, although the implementation was not tuned toward these scenarios.

An efficient generator for clustered dynamic random networks

A planted partition graph is an Erdős-Renyi type random graph, where, based on a given partition of the vertex set, vertices in the same part are linked with a higher probability than vertices in different parts. Graphs of this type are frequently used to evaluate graph clustering algorithms, i.e., algorithms that seek to partition the vertex set of a graph into densely connected clusters. We propose a self-evident modification of this model to generate sequences of random graphs that are obtained by atomic updates, i.e., the deletion or insertion of an edge or vertex. The random process follows a dynamically changing ground-truth clustering that can be used to evaluate dynamic graph clustering algorithms. We give a theoretical justification of our model and show how the corresponding random process can be implemented efficiently.

Transitions on multiple layers for scalable, energy-efficient and robust wireless video streaming

This work demonstrates the effects of multi-layer adaptivity for a wireless live video streaming scenario. We investigate a specific type of adaptations, the so-called transitions, which switch between different network mechanisms during the runtime of an application. In comparison to a pure configuration adaptation, a transition is beneficial because a system may select those mechanisms that perform best under varying environmental conditions. We consider transitions on the overlay (by switching between different stream delivery schemes on the application layer) as well as on the underlay (by switching between different wireless topologies). At the beginning of our demonstration, some few devices receive the video stream from a central server. Then, additional devices start receiving the video stream. Under these circumstances, a transition from the client/server delivery scheme to a decentralized peer-to-peer based video streaming improves scalability. On the underlay, video stream delivery benefits from topology control mechanisms, which select specific wireless neighbors in order to restrict communication to energy-efficient communication links. However, topology control also reduces the robustness of the underlay topology. Thus, when devices that are critical nodes in the wireless network are on the verge of running out of energy, we conduct a transition to a more robust underlay topology. In summary, by performing transitions jointly on multiple layers, we demonstrate resulting improvements of energy efficiency, scalability and robustness.

Optimal reconfiguration of dynamic software product lines based on performance-influence models

Todays adaptive software systems (i) are often highly configurable product lines, exhibiting hundreds of potentially conflicting configuration options; (ii) are context dependent, forcing the system to reconfigure to ever-changing contextual situations at runtime; (iii) need to fulfill context-dependent performance goals by optimizing measurable nonfunctional properties. Usually, a large number of consistent configurations exists for a given context, and each consistent configuration may perform differently with regard to the current context and performance goal(s). Therefore, it is crucial to consider nonfunctional properties for identifying an appropriate configuration. Existing black-box approaches for estimating the performance of configurations provide no means for determining context-sensitive reconfiguration decisions at runtime that are both consistent and optimal, and hardly allow for combining multiple context-dependent quality goals. In this paper, we propose a comprehensive approach based on Dynamic Software Product Lines (DSPL) for obtaining consistent and optimal reconfiguration decisions. We use training data obtained from simulations to learn performance-influence models. A novel integrated runtime representation captures both consistency properties and the learned performance-influence models. Our solution provides the flexibility to define multiple context-dependent performance goals. We have implemented our approach as a standalone component. Based on an Internet-of-Things case study using adaptive wireless sensor networks, we evaluate our approach with regard to effectiveness, efficiency, and applicability.