174,072 research outputs found
Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches
Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements
Designing Software Architectures As a Composition of Specializations of Knowledge Domains
This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: âA software architecture should be a composition of specializations of knowledge domainsâ. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience
Improving the viability of mental models held by novice programmers
Recent research has found that many novice programmers often hold non-viable mental models of basic programming concepts such as assignment and object reference. This paper proposes a constructivist-based teaching model, integrating a cognitive conflict strategy with program visualization, with the aim of improving novice programmersâ mental models. The results of a preliminary empirical study suggest that, for the relatively straightforward concept of assignment, tight integration of program visualization with a cognitive conflict event that highlights a studentâs inappropriate understanding can help improve studentsâ non-viable mental models. 14 out of 18 participants who held non-viable mental models of the assignment process successfully changed their model to be viable as a result of the proposed teaching model
Metamodel for Tracing Concerns across the Life Cycle
Several aspect-oriented approaches have been proposed to specify aspects at different phases in the software life cycle. Aspects can appear within a phase, be refined or mapped to other aspects in later phases, or even disappear.\ud
Tracing aspects is necessary to support understandability and maintainability of software systems. Although several approaches have been introduced to address traceability of aspects, two important limitations can be observed. First, tracing is not yet tackled for the entire life cycle. Second, the traceability model that is applied usually refers to elements of specific aspect languages, thereby limiting the reusability of the adopted traceability model.We propose the concern traceability metamodel (CTM) that enables traceability of concerns throughout the life cycle, and which is independent from the aspect languages that are used. CTM can be enhanced to provide additional properties for tracing, and be instantiated to define\ud
customized traceability models with respect to the required aspect languages. We have implemented CTM in the tool M-Trace, that uses XML-based representations of the models and XQuery queries to represent tracing information. CTM and M-Trace are illustrated for a Concurrent Versioning System to trace aspects from the requirements level to architecture design level and the implementation
GTA: Groupware task analysis Modeling complexity
The task analysis methods discussed in this presentation stem from Human-Computer Interaction (HCI) and Ethnography (as applied for the design of Computer Supported Cooperative Work CSCW), different disciplines that often are considered conflicting approaches when applied to the same design problems. Both approaches have their strength and weakness, and an integration of them does add value to the early stages of design of cooperation technology. In order to develop an integrated method for groupware task analysis (GTA) a conceptual framework is presented that allows a systematic perspective on complex work phenomena. The framework features a triple focus, considering (a) people, (b) work, and (c) the situation. Integrating various task-modeling approaches requires vehicles for making design information explicit, for which an object oriented formalism will be suggested. GTA consists of a method and framework that have been developed during practical design exercises. Examples from some of these cases will illustrate our approach
Strategic Planning, Hypercompetition, and Knowledge Management
When both product andresource markets are turbulentand disruptive, competitiveadvantage has to be continuallyrenewed. But a firm can do this onlyby managing its knowledge. Using twoelements of strategic planningâscenariobuilding and internal situation analysisâmanagers can map and integrate knowledge togain a sustainable competitive edge in todayâshypercompetitive markets
The future of technology enhanced active learning â a roadmap
The notion of active learning refers to the active involvement of learner in the learning process,
capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap,
the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a
best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap
Ontology-based patterns for the integration of business processes and enterprise application architectures
Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge
the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture
descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of
software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data.
Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an
ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their
applicability in business process-driven application integration is demonstrated
Two-Stream Action Recognition-Oriented Video Super-Resolution
We study the video super-resolution (SR) problem for facilitating video
analytics tasks, e.g. action recognition, instead of for visual quality. The
popular action recognition methods based on convolutional networks, exemplified
by two-stream networks, are not directly applicable on video of low spatial
resolution. This can be remedied by performing video SR prior to recognition,
which motivates us to improve the SR procedure for recognition accuracy.
Tailored for two-stream action recognition networks, we propose two video SR
methods for the spatial and temporal streams respectively. On the one hand, we
observe that regions with action are more important to recognition, and we
propose an optical-flow guided weighted mean-squared-error loss for our
spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving
objects. On the other hand, we observe that existing video SR methods incur
temporal discontinuity between frames, which also worsens the recognition
accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR)
training that emphasizes the temporal continuity between consecutive frames. We
perform experiments using two state-of-the-art action recognition networks and
two well-known datasets--UCF101 and HMDB51. Results demonstrate the
effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.Comment: Accepted to ICCV 2019. Code:
https://github.com/AlanZhang1995/TwoStreamS
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