5,953 research outputs found
The future of technology enhanced active learning – 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
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Conformance checking: A state-of-the-art literature review
Conformance checking is a set of process mining functions that compare
process instances with a given process model. It identifies deviations between
the process instances' actual behaviour ("as-is") and its modelled behaviour
("to-be"). Especially in the context of analyzing compliance in organizations,
it is currently gaining momentum -- e.g. for auditors. Researchers have
proposed a variety of conformance checking techniques that are geared towards
certain process model notations or specific applications such as process model
evaluation. This article reviews a set of conformance checking techniques
described in 37 scholarly publications. It classifies the techniques along the
dimensions "modelling language", "algorithm type", "quality metric", and
"perspective" using a concept matrix so that the techniques can be better
accessed by practitioners and researchers. The matrix highlights the dimensions
where extant research concentrates and where blind spots exist. For instance,
process miners use declarative process modelling languages often, but
applications in conformance checking are rare. Likewise, process mining can
investigate process roles or process metrics such as duration, but conformance
checking techniques narrow on analyzing control-flow. Future research may
construct techniques that support these neglected approaches to conformance
checking
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