73,102 research outputs found
Towards rule-based visual programming of generic visual systems
This paper illustrates how the diagram programming language DiaPlan can be
used to program visual systems. DiaPlan is a visual rule-based language that is
founded on the computational model of graph transformation. The language
supports object-oriented programming since its graphs are hierarchically
structured. Typing allows the shape of these graphs to be specified recursively
in order to increase program security. Thanks to its genericity, DiaPlan allows
to implement systems that represent and manipulate data in arbitrary diagram
notations. The environment for the language exploits the diagram editor
generator DiaGen for providing genericity, and for implementing its user
interface and type checker.Comment: 15 pages, 16 figures contribution to the First International Workshop
on Rule-Based Programming (RULE'2000), September 19, 2000, Montreal, Canad
Evaluating the performance of model transformation styles in Maude
Rule-based programming has been shown to be very successful in many application areas. Two prominent examples are the specification of model transformations in model driven development approaches and the definition of structured operational semantics of formal languages. General rewriting frameworks such as Maude are flexible enough to allow the programmer to adopt and mix various rule styles. The choice between styles can be biased by the programmer’s background. For instance, experts in visual formalisms might prefer graph-rewriting styles, while experts in semantics might prefer structurally inductive rules. This paper evaluates the performance of different rule styles on a significant benchmark taken from the literature on model transformation. Depending on the actual transformation being carried out, our results show that different rule styles can offer drastically different performances. We point out the situations from which each rule style benefits to offer a valuable set of hints for choosing one style over the other
Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond
We present a graphical and dynamic framework for binding and execution of
business) process models. It is tailored to integrate 1) ad hoc processes
modeled graphically, 2) third party services discovered in the (Inter)net, and
3) (dynamically) synthesized process chains that solve situation-specific
tasks, with the synthesis taking place not only at design time, but also at
runtime. Key to our approach is the introduction of type-safe stacked
second-order execution contexts that allow for higher-order process modeling.
Tamed by our underlying strict service-oriented notion of abstraction, this
approach is tailored also to be used by application experts with little
technical knowledge: users can select, modify, construct and then pass
(component) processes during process execution as if they were data. We
illustrate the impact and essence of our framework along a concrete, realistic
(business) process modeling scenario: the development of Springer's
browser-based Online Conference Service (OCS). The most advanced feature of our
new framework allows one to combine online synthesis with the integration of
the synthesized process into the running application. This ability leads to a
particularly flexible way of implementing self-adaption, and to a particularly
concise and powerful way of achieving variability not only at design time, but
also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Statistical Mechanics of Community Detection
Starting from a general \textit{ansatz}, we show how community detection can
be interpreted as finding the ground state of an infinite range spin glass. Our
approach applies to weighted and directed networks alike. It contains the
\textit{at hoc} introduced quality function from \cite{ReichardtPRL} and the
modularity as defined by Newman and Girvan \cite{Girvan03} as special
cases. The community structure of the network is interpreted as the spin
configuration that minimizes the energy of the spin glass with the spin states
being the community indices. We elucidate the properties of the ground state
configuration to give a concise definition of communities as cohesive subgroups
in networks that is adaptive to the specific class of network under study.
Further we show, how hierarchies and overlap in the community structure can be
detected. Computationally effective local update rules for optimization
procedures to find the ground state are given. We show how the \textit{ansatz}
may be used to discover the community around a given node without detecting all
communities in the full network and we give benchmarks for the performance of
this extension. Finally, we give expectation values for the modularity of
random graphs, which can be used in the assessment of statistical significance
of community structure
The Algebraic View of Computation
We argue that computation is an abstract algebraic concept, and a computer is
a result of a morphism (a structure preserving map) from a finite universal
semigroup.Comment: 13 pages, final version will be published elsewher
Link prediction in very large directed graphs: Exploiting hierarchical properties in parallel
Link prediction is a link mining task that tries to find new edges within a given graph. Among the targets of link prediction there is large directed graphs, which are frequent structures nowadays. The typical sparsity of large graphs demands of high precision predictions in order to obtain usable results. However, the size of those graphs only permits the execution of scalable algorithms. As a trade-off between those two problems we recently proposed a link prediction algorithm for directed graphs that exploits hierarchical properties. The algorithm can be classified as a local score, which entails scalability. Unlike the rest of local scores, our proposal assumes the existence of an underlying model for the data which allows it to produce predictions with a higher precision. We test the validity of its hierarchical assumptions on two clearly hierarchical data sets, one of them based on RDF. Then we test it on a non-hierarchical data set based on Wikipedia to demonstrate its broad applicability. Given the computational complexity of link prediction in very large graphs we also introduce some general recommendations useful to make of link prediction an efficiently parallelized problem.Peer ReviewedPostprint (published version
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