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Auflistung nach Schlagwort "Adaptivity"

1 - 4 von 4
  • Zeitschriftenartikel
    From User-adaptive to Context-adaptive Information Systems (Von benutzeradaptiven zu kontextadaptiven Informationssystemen)
    (i-com: Vol. 4, No. 3, 2005) Oppermann, Reinhard
    Summary This paper introduces context-adaptive information systems reflecting the current needs of the user. Context-adaptive information systems reflect more than classical user-adaptive systems where user and task characteristics are considered for adaptation. In context-adaptive information systems the usage episode is additionally defined by the time and the location, by the physical and social environment and the technical infrastructure and eventually by relevant situational characteristic such as sound, light or movement. To begin with, the rationality of adaptive systems and the concept of context-adaptiveness will be explained. Based upon the description of the three functions of adaptivity, i.e., the interaction logging, adaptation inference and adaptation performance, we describe user-adaptive and context-adaptive systems and the role sharing between the system and the user during the adaptation process. Techniques of location-awareness as currently an important determinant of context are descr...
  • Zeitschriftenartikel
    Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning
    (KI - Künstliche Intelligenz: Vol. 34, No. 2, 2020) Sioutis, Michael
    We discuss a research roadmap for going beyond the state of the art in qualitative spatial and temporal reasoning (QSTR). Simply put, QSTR is a major field of study in Artificial Intelligence that abstracts from numerical quantities of space and time by using qualitative descriptions instead (e.g., precedes, contains, is left of); thus, it provides a concise framework that allows for rather inexpensive reasoning about entities located in space or time. Applications of QSTR can be found in a plethora of areas and domains such as smart environments, intelligent vehicles, and unmanned aircraft systems. Our discussion involves researching novel local consistencies in the aforementioned discipline, defining dynamic algorithms pertaining to these consistencies that can allow for efficient reasoning over changing spatio-temporal information, and leveraging the structures of the locally consistent related problems with regard to novel decomposability and theoretical tractability properties. Ultimately, we argue for pushing the envelope in QSTR via defining tools for tackling dynamic variants of the fundamental reasoning problems in this discipline, i.e., problems stated in terms of changing input data. Indeed, time is a continuous flow and spatial objects can change (e.g., in shape, size, or structure) as time passes; therefore, it is pertinent to be able to efficiently reason about dynamic spatio-temporal data. Finally, these tools are to be integrated into the larger context of highly active areas such as neuro-symbolic learning and reasoning, planning, data mining, and robotic applications. Our final goal is to inspire further discussion in the community about constraint-based QSTR in general, and the possible lines of future research that we outline here in particular.
  • Zeitschriftenartikel
    Mobilität, Adaptivität und Kontextbewusstsein im E-Learning
    (i-com: Vol. 11, No. 1, 2012) Lucke, Ulrike; Specht, Marcus
    Die weite Verbreitung drahtloser Netze und mobiler Endgeräte ermöglicht nicht nur einen orts- und zeitunabhängigen Zugang zu Bildungsangeboten, sondern auch deren Anpassung an Person, Ort und Umfeld sowie die nahtlose Verschmelzung verschiedener Lernorte.
  • Textdokument
    NeMeSys – Energy Adaptive Graph Pattern Matching on NUMA-based Multiprocessor Systems
    (BTW 2019, 2019) Krause, Alexander; Ungethüm, Annett; Kissinger, Thomas; Habich, Dirk; Lehner, Wolfgang
    NeMeSys is a NUMA-aware graph pattern processing engine, which leverages intelligent resource management for energy adaptive processing. With modern server systems incorporating an increasing amount of main memory, we can store graphs and compute analytical graph algorithms like graph pattern matching completely in-memory. Such server systems usually contain several powerful multiprocessors, which come with a high demand for energy. We demonstrate, that graph patterns can be processed in given performance constraints while saving energy, which would be wasted without proper controlling.