Prof. Dr. rer. nat. habil. Gerhard Weber
Inhaltsverzeichnis
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Name
Prof. Dr. Gerhard Weber
Professur Mensch-Computer Interaktion
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Zur Professur für Mensch-Computer Interaktion
FORSCHUNGSSCHWERPUNKTE
- Informatik und Inklusion
- Assistive Technologien
- Haptik und Multimodalität in der Mensch-Computer Interaktion
LEBENSLAUF
2007 | Universitätsprofessor und Leiter der Professur Mensch-Computer Interaktion, TU Dresden, Deutschland |
2000 - 2007 | Universitätsprofessor und Leiter der Professur Human-Centered Interfaces, Christian-Albrechts-Universität zu Kiel, Deutschland |
1996 - 2000 | Professur Betriebsysteme und graf. Oberflächen, FB Automatisierung und Informatik, Hochschule Harz, Wernigerode, Deutschland |
AKTUELLE PUBLIKATIONEN
1 bis 10 von 70 Einträgen
Diese Informationen werden vom Vorgängersystem FIS bereitgestellt.
AKTUELLE FORSCHUNG
High-Capacity Knowledge Processing Pipeline (HAEC Teilprojekt B08)
Titel (Englisch)
High-Capacity Knowledge Processing Pipeline (HAEC Project B08)
Kurzbeschreibung (Deutsch)
Das Teilprojekt B08 im Sonderforschungsbereich HAEC hat sich zum Ziel gesetzt, Energieeffizienz und Adaptivität der geplanten HAEC Computerarchitektur anhand von praktisch relevanten Anwendungen im Bereich Wissensgraphen zu zeigen. Dazu entwickelt das Teilprojekt deklarative Ontologiesprachen zur Modellierung komplexer Zusammenhängen einerseits und effiziente Graph-Datenbanken und -Analyseplattformen anderseits. Ein zentrales Elemetn zur Integration beider Aspekte ist die Unterstützung ausdrucksstarker Abfragesprachen, in welche die deklarativ spezifizierten Berechnungsaufgaben übersetzt werden können.
Das Projekt soll anhand mehrerer Anwendungsfälle evaluiert werden. Ein solcher Anwendungsfall ist die deklarative Analyse des Wissensgraphen in Wikidata. Ein weiterer Anwendungsfall ist die Erkennung dynamischer Kontexte, wie sie im HAEC Teilprojekt B02 (Baader/Turhan) benötigt wird.
Das Projekt soll anhand mehrerer Anwendungsfälle evaluiert werden. Ein solcher Anwendungsfall ist die deklarative Analyse des Wissensgraphen in Wikidata. Ein weiterer Anwendungsfall ist die Erkennung dynamischer Kontexte, wie sie im HAEC Teilprojekt B02 (Baader/Turhan) benötigt wird.
Kurzbeschreibung (Englisch)
HAEC takes a comprehensive approach to energy efficiency, where individual components of hardware and software are not studied in isolation but in a greater context that allows the system to make optimal decisions at any point in time. To achieve this – and indeed even to evaluate whether it has been achieved yet – it is essential to confront the system with practically relevant computational workloads that can challenge the novel, adaptive architecture of HAEC on all levels.
To address this task, project HAEC B08 plans to create a new bridge between high-level knowledge-driven applications and low-level data management infrastructure in HAEC. This should enable applications to express complex information needs in a concise, efficient, and declarative way while exploiting the specific hardware and software characteristics of the HAEC Box. We believe that navigational query languages are the appropriate tool for this: they can capture advanced computations, such as graph search operations, yet they have a fully declarative semantics that is a sound basis for data exchange across application boundaries. Given this “middle ground,” we obtain two clearly defined main targets: (1) high-level applications must exploit the power of navigational queries to reduce communication, and (2) low-level components must answer such queries efficiently on the given computational architecture. Neither of these would make sense in isolation, since the gap can only be closed if both sides agree on the details of navigational query language to be used.
The evaluation of the work will consider two highly relevant use cases. The first is ontology-based query answering over large knowledge graphs, which is an important challenge for modern data management systems. Our work will be based on Wikidata, the new knowledge graph of Wikipedia, as an ideal example of a complex, dynamic dataset of high practical relevance. As a second use case, we will consider the context awareness approach developed in B02 (Baader/Turhan). This is a well-specified problem for which any increase in performance can directly contribute to the success of HAEC. In particular, we expect synergies with the planned work in B02 (Baader/Turhan), B04 (Härtig) and B05 (Lehner). Moreover, the application data generated in this project will provide relevant input for the work in B07 (Castrillón Mazo) and A04 (Nagel). If successful, B08 will thus close the gap in an important application stack, and enable applications to take full advantage of the adaptive, energy-efficient platform developed in HAEC.
To address this task, project HAEC B08 plans to create a new bridge between high-level knowledge-driven applications and low-level data management infrastructure in HAEC. This should enable applications to express complex information needs in a concise, efficient, and declarative way while exploiting the specific hardware and software characteristics of the HAEC Box. We believe that navigational query languages are the appropriate tool for this: they can capture advanced computations, such as graph search operations, yet they have a fully declarative semantics that is a sound basis for data exchange across application boundaries. Given this “middle ground,” we obtain two clearly defined main targets: (1) high-level applications must exploit the power of navigational queries to reduce communication, and (2) low-level components must answer such queries efficiently on the given computational architecture. Neither of these would make sense in isolation, since the gap can only be closed if both sides agree on the details of navigational query language to be used.
The evaluation of the work will consider two highly relevant use cases. The first is ontology-based query answering over large knowledge graphs, which is an important challenge for modern data management systems. Our work will be based on Wikidata, the new knowledge graph of Wikipedia, as an ideal example of a complex, dynamic dataset of high practical relevance. As a second use case, we will consider the context awareness approach developed in B02 (Baader/Turhan). This is a well-specified problem for which any increase in performance can directly contribute to the success of HAEC. In particular, we expect synergies with the planned work in B02 (Baader/Turhan), B04 (Härtig) and B05 (Lehner). Moreover, the application data generated in this project will provide relevant input for the work in B07 (Castrillón Mazo) and A04 (Nagel). If successful, B08 will thus close the gap in an important application stack, and enable applications to take full advantage of the adaptive, energy-efficient platform developed in HAEC.
Zeitraum
01.06.2015 - 30.06.2019
Art der Finanzierung
Drittmittel
Projektleiter
- Herr Prof. Dr. Markus Krötzsch
Weitere Leiter (außerhalb des Lehrstuhls)
Prof. Dr.-Ing. Franz Baader, Prof. Dr.-Ing. Wolfgang Lehner
Projektmitarbeiter
- Frau Dipl.-Inf. Veronika Thost
- Herr Dipl.-Inf. Alexander Krause
Finanzierungseinrichtungen
- DFG
Kooperationspartnerschaft
keine
Website zum Projekt
Zugeordneter Sonderforschungsbereich
SFB 912 Highly Adaptive Energy-Efficient Computing
Relevant für den Umweltschutz
Nein
Relevant für Multimedia
Nein
Relevant für den Technologietransfer
Nein
Schlagwörter
Energieeffizienz, Graphdatenbanken, Adaptivität, Ontologien
Berichtsjahr
2017