000 Informatik, Wissen, Systeme
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30. Forum Bauinformatik
(2018)
Die Bauhaus-Universität Weimar ist seit langer Zeit mit dem Forum Bauinformatik eng verbunden. So wurde die Veranstaltung 1989 hier durch den Arbeitskreis Bauinformatik ins Leben gerufen und auch das 10. und 18. Forum Bauinformatik (1998 bzw. 2006) fand in Weimar statt. In diesem Jahr freuen wir uns daher besonders, das 30. Jubiläum an der Bauhaus-Universität Weimar ausrichten zu dürfen und viele interessierte Wissenschaftler und Wissenschaftlerinnen aus dem Bereich der Bauinformatik in Weimar willkommen zu heißen.
Das Forum Bauinformatik hat sich längst zu einem festen Bestandteil der Bauinformatik im deutschsprachigen Raum entwickelt. Dabei steht es traditionsgemäß unter dem Motto „von jungen Forschenden für junge Forschende“, wodurch insbesondere Nachwuchswissenschaftlerinnen und ‑wissenschaftlern die Möglichkeit geboten wird, ihre Forschungsarbeiten zu präsentieren, Problemstellungen fachspezifisch zu diskutieren und sich über den neuesten Stand der Forschung zu informieren. Zudem wird eine ausgezeichnete Gelegenheit geboten, in die wissenschaftliche Gemeinschaft im Bereich der Bauinformatik einzusteigen und Kontakte mit anderen Forschenden zu knüpfen.
In diesem Jahr erhielten wir 49 interessante und qualitativ hochwertige Beiträge vor allem in den Themenbereichen Simulation, Modellierung, Informationsverwaltung, Geoinformatik, Structural Health Monitoring, Visualisierung, Verkehrssimulation und Optimierung. Dafür möchten wir uns ganz besonders bei allen Autoren, Co-Autoren und Reviewern bedanken, die durch ihr Engagement das diesjährige Forum Bauinformatik erst möglich gemacht haben. Wir danken zudem Professor Große und Professor Díaz für die Unterstützung bei der Auswahl der Beiträge für die Best Paper Awards.
Ein herzliches Dankeschön geht an die Kollegen an der Professur Informatik im Bauwesen der Bauhaus-Universität Weimar für die organisatorische, technische und beratende Unterstützung während der Planung der Veranstaltung.
Modern distributed engineering applications are based on complex systems consisting of various subsystems that are connected through the Internet. Communication and collaboration within an entire system requires reliable and efficient data exchange between the subsystems. Middleware developed within the web evolution during the past years provides reliable and efficient data exchange for web applications, which can be adopted for solving the data exchange problems in distributed engineering applications. This paper presents a generic approach for reliable and efficient data exchange between engineering devices using existing middleware known from web applications. Different existing middleware is examined with respect to the suitability in engineering applications. In this paper, a suitable middleware is shown and a prototype implementation simulating distributed wind farm control is presented and validated using several performance measurements.
We apply keyquery-based taxonomy composition to compute a classification system for the CORE dataset, a shared crawl of about 850,000 scientific papers. Keyquery-based taxonomy composition can be understood as a two-phase hierarchical document clustering technique that utilizes search queries as cluster labels: In a first phase, the document collection is indexed by a reference search engine, and the documents are tagged with the search queries they are relevant—for their so-called keyqueries. In a second phase, a hierarchical clustering is formed from the keyqueries within an iterative process. We use the explicit topic model ESA as document retrieval model in order to index the CORE dataset in the reference search engine. Under the ESA retrieval model, documents are represented as vectors of similarities to Wikipedia articles; a methodology proven to be advantageous for text categorization tasks. Our paper presents the generated taxonomy and reports on quantitative properties such as document coverage and processing requirements.
Information technology plays a key role in the everyday operation of buildings and campuses. Many proprietary technologies and methodologies can assist in effective Building Performance Monitoring (BPM) and efficient managing of building resources. The integration of related tools like energy simulator packages, facility, energy and building management systems, and enterprise resource planning systems is of benefit to BPM. However, the complexity to integrating such domain specific systems prevents their common usage. Service Oriented Architecture (SOA) has been deployed successfully in many large multinational companies to create integrated and flexible software systems, but so far this methodology has not been applied broadly to the field of BPM. This paper envisions that SOA provides an effective integration framework for BPM. Service oriented architecture for the ITOBO framework for sustainable and optimised building operation is proposed and an implementation for a building performance monitoring system is introduced.
A four-node quadrilateral shell element with smoothed membrane-bending based on Mindlin-Reissner theory is proposed. The element is a combination of a plate bending and membrane element. It is based on mixed interpolation where the bending and membrane stiffness matrices are calculated on the boundaries of the smoothing cells while the shear terms are approximated by independent interpolation functions in natural coordinates. The proposed element is robust, computationally inexpensive and free of locking. Since the integration is done on the element boundaries for the bending and membrane terms, the element is more accurate than the MITC4 element for distorted meshes. This will be demonstrated for several numerical examples.
A UNIFIED APPROACH FOR THE TREATMENT OF SOME HIGHER DIMENSIONAL DIRAC TYPE EQUATIONS ON SPHERES
(2010)
Using Clifford analysis methods, we provide a unified approach to obtain explicit solutions of some partial differential equations combining the n-dimensional Dirac and Euler operators, including generalizations of the classical time-harmonic Maxwell equations. The obtained regular solutions show strong connections between hypergeometric functions and homogeneous polynomials in the kernel of the Dirac operator.
The uncertainty existing in the construction industry is bigger than in other industries. Consequently, most construction projects do not go totally as planned. The project management plan needs therefore to be adapted repeatedly within the project lifecycle to suit the actual project conditions. Generally, the risks of change in the project management plan are difficult to be identified in advance, especially if these risks are caused by unexpected events such as human errors or changes in the client preferences. The knowledge acquired from different resources is essential to identify the probable deviations as well as to find proper solutions to the faced change risks. Hence, it is necessary to have a knowledge base that contains known solutions for the common exceptional cases that may cause changes in each construction domain. The ongoing research work presented in this paper uses the process modeling technique of Event-driven Process Chains to describe different patterns of structure changes in the schedule networks. This results in several so called “change templates”. Under each template different types of change risk/ response pairs can be categorized and stored in a knowledge base. This knowledge base is described as an ontology model populated with reference construction process data. The implementation of the developed approach can be seen as an iterative scheduling cycle that will be repeated within the project lifecycle as new change risks surface. This can help to check the availability of ready solutions in the knowledge base for the situation at hand. Moreover, if the solution is adopted, CPSP, “Change Project Schedule Plan „a prototype developed for the purpose of this research work, will be used to make the needed structure changes of the schedule network automatically based on the change template. What-If scenarios can be implemented using the CPSP prototype in the planning phase to study the effect of specific situations without endangering the success of the project objectives. Hence, better designed and more maintainable project schedules can be achieved.
We present recent developments of adaptive wavelet solvers for elliptic eigenvalue problems. We describe the underlying abstract iteration scheme of the preconditioned perturbed iteration. We apply the iteration to a simple model problem in order to identify the main ideas which a numerical realization of the abstract scheme is based upon. This indicates how these concepts carry over to wavelet discretizations. Finally we present numerical results for the Poisson eigenvalue problem on an L-shaped domain.
Search engines are very good at answering queries that look for facts. Still, information needs that concern forming opinions on a controversial topic or making a decision remain a challenge for search engines. Since they are optimized to retrieve satisfying answers, search engines might emphasize a specific stance on a controversial topic in their ranking, amplifying bias in society in an undesired way. Argument retrieval systems support users in forming opinions about controversial topics by retrieving arguments for a given query. In this thesis, we address challenges in argument retrieval systems that concern integrating them in search engines, developing generalizable argument mining approaches, and enabling frame-guided delivery of arguments.
Adapting argument retrieval systems to search engines should start by identifying and analyzing information needs that look for arguments. To identify questions that look for arguments we develop a two-step annotation scheme that first identifies whether the context of a question is controversial, and if so, assigns it one of several question types: factual, method, and argumentative. Using this annotation scheme, we create a question dataset from the logs of a major search engine and use it to analyze the characteristics of argumentative questions. The analysis shows that the proportion of argumentative questions on controversial topics is substantial and that they mainly ask for reasons and predictions. The dataset is further used to develop a classifier to uniquely map questions to the question types, reaching a convincing F1-score of 0.78.
While the web offers an invaluable source of argumentative content to respond to argumentative questions, it is characterized by multiple genres (e.g., news articles and social fora). Exploiting the web as a source of arguments relies on developing argument mining approaches that generalize over genre. To this end, we approach the problem of how to extract argument units in a genre-robust way. Our experiments on argument unit segmentation show that transfer across genres is rather hard to achieve using existing sequence-to-sequence models.
Another property of text which argument mining approaches should generalize over is topic. Since new topics appear daily on which argument mining approaches are not trained, argument mining approaches should be developed in a topic-generalizable way. Towards this goal, we analyze the coverage of 31 argument corpora across topics using three topic ontologies. The analysis shows that the topics covered by existing argument corpora are biased toward a small subset of easily accessible controversial topics, hinting at the inability of existing approaches to generalize across topics. In addition to corpus construction standards, fostering topic generalizability requires a careful formulation of argument mining tasks. Same side stance classification is a reformulation of stance classification that makes it less dependent on the topic. First experiments on this task show promising results in generalizing across topics.
To be effective at persuading their audience, users of an argument retrieval system should select arguments from the retrieved results based on what frame they emphasize of a controversial topic. An open challenge is to develop an approach to identify the frames of an argument. To this end, we define a frame as a subset of arguments that share an aspect. We operationalize this model via an approach that identifies and removes the topic of arguments before clustering them into frames. We evaluate the approach on a dataset that covers 12,326 frames and show that identifying the topic of an argument and removing it helps to identify its frames.