136,895 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
A component-oriented programming framework for developing embedded mobile robot software using PECOS model
A practical framework for component-based software engineering of embedded real-time systems, particularly for autonomous mobile robot embedded software development using PECOS component model is proposed The main features of this framework are: (1) use graphical representation for components definition and composition; (2) target C language for optimal code generation with small micro-controller; and (3) does not requires run-time support except for real-time kernel. Real-time implementation indicates that, the PECOS component model together with the proposed framework is suitable for resource constrained embedded systems
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Towards an aspect weaving BPEL engine
This position paper proposes the use of dynamic aspects and
the visitor design pattern to obtain a highly configurable and
extensible BPEL engine. Using these two techniques, the
core of this infrastructural software can be customised to
meet new requirements and add features such as debugging,
execution monitoring, or changing to another Web Service
selection policy. Additionally, it can easily be extended to
cope with customer-specific BPEL extensions. We propose
the use of dynamic aspects not only on the engine itself
but also on the workflow in order to tackle the problems of
Web Service hot deployment and hot fixes to long running
processes. In this way, composing aWeb Service "on-the-fly"
means weaving its choreography interface into the workflow
Analytics and complexity: learning and leading for the future
There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward
INSTITUTIONAL ANALYSIS ON POVERTY REDUCTION PROGRAM IN THE SOCIETY: A CASE STUDY OF NATIONAL PROGRAM FOR COMMUNITY EMPOWERMENT OF INDEPENDENT URBAN (PNPM-MP) IN SEMARANG, INDONESIA
Institutional PNPM-MP in Semarang put poverty alleviation as the main priority of the empowerment-based development policy. The strategy developed is to synergize government agencies with community institutions built by PNPM-MP program at the village and base level, that is, Community Institutional Agency (BKM) and the Community Self-Reliance Group (KSM). The problem studied in this research is: How does PNPM-MP institutional in society involve in poverty reduction, with the aim of describing and analyzing institutional programs in the community. The research approach used phenomenological qualitative, by conducting interviews, observation, focus group discussion to obtain data from informants (BKM / KSM). Informants include two BKM and ten KSM which is purposively selected (deliberately) from two villages in two districts. The analysis was performed interactively, that is, analysis techniques which are integral cycle among data collection, data reduction, data performance and conclusion withdrawal. Research conclusion: PNPM-MP institutional at the village and base level (BKM / KSM) has not been able to be a driving force in poverty reduction and is still seen by the community as a program requirement, not institutionalized on both horizontal and vertical level. Recommendation for the research result is that it needed awareness that poverty reduction requires a synergy between government agencies and community agencies embodied in the development planning of one village one planning
Knowing Your Population: Privacy-Sensitive Mining of Massive Data
Location and mobility patterns of individuals are important to environmental
planning, societal resilience, public health, and a host of commercial
applications. Mining telecommunication traffic and transactions data for such
purposes is controversial, in particular raising issues of privacy. However,
our hypothesis is that privacy-sensitive uses are possible and often beneficial
enough to warrant considerable research and development efforts. Our work
contends that peoples behavior can yield patterns of both significant
commercial, and research, value. For such purposes, methods and algorithms for
mining telecommunication data to extract commonly used routes and locations,
articulated through time-geographical constructs, are described in a case study
within the area of transportation planning and analysis. From the outset, these
were designed to balance the privacy of subscribers and the added value of
mobility patterns derived from their mobile communication traffic and
transactions data. Our work directly contrasts the current, commonly held
notion that value can only be added to services by directly monitoring the
behavior of individuals, such as in current attempts at location-based
services. We position our work within relevant legal frameworks for privacy and
data protection, and show that our methods comply with such requirements and
also follow best-practice
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