27,870 research outputs found
Integration of BPM systems
New technologies have emerged to support the global economy where for instance suppliers, manufactures and retailers are working together in order to minimise the cost and
maximise efficiency. One of the technologies that has become a buzz word for many businesses is business process management or BPM. A business process comprises activities
and tasks, the resources required to perform each task, and the business rules linking these activities and tasks. The tasks may be performed by human and/or machine actors.
Workflow provides a way of describing the order of execution and the dependent relationships between the constituting activities of short or long running processes.
Workflow allows businesses to capture not only the information but also the processes that transform the information - the process asset (Koulopoulos, T. M., 1995). Applications which involve automated, human-centric and collaborative processes across organisations are
inherently different from one organisation to another. Even within the same organisation but over time, applications are adapted as ongoing change to the business processes is seen as the norm in today’s dynamic business environment. The major difference lies in the specifics of business processes which are changing rapidly in order to match the way in which businesses operate. In this chapter we introduce and discuss Business Process Management (BPM) with a focus on the integration of heterogeneous BPM systems across multiple organisations. We identify the problems and the main challenges not only with regards to technologies but also in the social and cultural context. We also discuss the issues that have arisen in our bid to find the solutions
Optimising ITS behaviour with Bayesian networks and decision theory
We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next tutorial action. Because normative theories are a general framework for rational behaviour, they can be used to both define and apply learning theories in a rational, and therefore optimal, way. This contrasts to the more traditional approach of using an ad-hoc scheme to implement the learning theory. A key step of the methodology is the induction and the continual adaptation of the Bayesian network student model from student performance data, a step that is distinct from other recent Bayesian net approaches in which the network structure and probabilities are either chosen beforehand by an expert, or by efficiency considerations. The methodology is demonstrated by a description and evaluation of CAPIT, a normative constraint-based tutor for English capitalisation and punctuation. Our evaluation results show that a class using the full normative version of CAPIT learned the domain rules at a faster rate than the class that used a non-normative version of the same system
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Intelligent synthesis mechanism for deriving streaming priorities of multimedia content
We address the problem of integrating user preferences with network Quality of Service parameters for the streaming of media content, and suggest protocol stack configurations
that satisfy user and technical requirements to the best available degree. Our approach is able to handle inconsistencies between user and networking considerations, formulating the
problem of construction of tailor-made protocols as a prioritization problem, solvable using fuzzy programming
Intelligent synthesis mechanism for deriving streaming priorities of multimedia content
We address the problem of integrating user preferences with network quality of service parameters for the streaming of media content, and suggest protocol stack configurations that satisfy user and technical requirements to the best available degree. Our approach is able to handle inconsistencies between user and networking considerations, formulating the problem of construction of tailor-made protocols as a prioritization problem, solvable using fuzzy programming
Keynote address: the networked bank
Technology ; Banks and banking - Customer services ; Automated tellers
GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics
Large scale graph processing is a major research area for Big Data
exploration. Vertex centric programming models like Pregel are gaining traction
due to their simple abstraction that allows for scalable execution on
distributed systems naturally. However, there are limitations to this approach
which cause vertex centric algorithms to under-perform due to poor compute to
communication overhead ratio and slow convergence of iterative superstep. In
this paper we introduce GoFFish a scalable sub-graph centric framework
co-designed with a distributed persistent graph storage for large scale graph
analytics on commodity clusters. We introduce a sub-graph centric programming
abstraction that combines the scalability of a vertex centric approach with the
flexibility of shared memory sub-graph computation. We map Connected
Components, SSSP and PageRank algorithms to this model to illustrate its
flexibility. Further, we empirically analyze GoFFish using several real world
graphs and demonstrate its significant performance improvement, orders of
magnitude in some cases, compared to Apache Giraph, the leading open source
vertex centric implementation.Comment: Under review by a conference, 201
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