237,593 research outputs found
Four facets of a process modeling facilitator
Business process modeling as a practice and research field has received great attention in recent years. However, while related artifacts such as models, tools or grammars have substantially matured, comparatively little is known about the activities that are conducted as part of the actual act of process modeling. Especially the key role of the modeling facilitator has not been researched to date. In this paper, we propose a new theory-grounded, conceptual framework describing four facets (the driving engineer, the driving artist, the catalyzing engineer, and the catalyzing artist) that can be used by a facilitator. These facets with behavioral styles have been empirically explored via in-depth interviews and additional questionnaires with experienced process analysts. We develop a proposal for an emerging theory for describing, investigating, and explaining different behaviors associated with Business Process Modeling Facilitation. This theory is an important sensitizing vehicle for examining processes and outcomes from process modeling endeavors
Active Object Localization in Visual Situations
We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images.Comment: 14 page
Information system support in construction industry with semantic web technologies and/or autonomous reasoning agents
Information technology support is hard to find for the early design phases of the architectural design process. Many of the existing issues in such design decision support tools appear to be caused by a mismatch between the ways in which designers think and the ways in which information systems aim to give support. We therefore started an investigation of existing theories of design thinking, compared to the way in which design decision support systems provide information to the designer. We identify two main strategies towards information system support in the early design phase: (1) applications for making design try-outs, and (2) applications as autonomous reasoning agents. We outline preview implementations for both approaches and indicate to what extent these strategies can be used to improve information system support for the architectural designer
Simulating activities: Relating motives, deliberation, and attentive coordination
Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems
Modeling social networks from sampled data
Network models are widely used to represent relational information among
interacting units and the structural implications of these relations. Recently,
social network studies have focused a great deal of attention on random graph
models of networks whose nodes represent individual social actors and whose
edges represent a specified relationship between the actors. Most inference for
social network models assumes that the presence or absence of all possible
links is observed, that the information is completely reliable, and that there
are no measurement (e.g., recording) errors. This is clearly not true in
practice, as much network data is collected though sample surveys. In addition
even if a census of a population is attempted, individuals and links between
individuals are missed (i.e., do not appear in the recorded data). In this
paper we develop the conceptual and computational theory for inference based on
sampled network information. We first review forms of network sampling designs
used in practice. We consider inference from the likelihood framework, and
develop a typology of network data that reflects their treatment within this
frame. We then develop inference for social network models based on information
from adaptive network designs. We motivate and illustrate these ideas by
analyzing the effect of link-tracing sampling designs on a collaboration
network.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS221 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Enhancing the Guidance of the Intentional Model "MAP": Graph Theory Application
The MAP model was introduced in information system engineering in order to
model processes on a flexible way. The intentional level of this model helps an
engineer to execute a process with a strong relationship to the situation of
the project at hand. In the literature, attempts for having a practical use of
maps are not numerous. Our aim is to enhance the guidance mechanisms of the
process execution by reusing graph algorithms. After clarifying the existing
relationship between graphs and maps, we improve the MAP model by adding
qualitative criteria. We then offer a way to express maps with graphs and
propose to use Graph theory algorithms to offer an automatic guidance of the
map. We illustrate our proposal by an example and discuss its limitations.Comment: 9 page
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