An Approach of Domain Polymorph Component Design

International audienceHeterogeneous modelling and design tools allow the design of software systems using several computation models. The designed system is built by assembling components that obey a computation model. The internal behavior of a component is specified either in some programming language or by assembling sub-components that obey a possibly different computation model. When the same behavior is used in several computation models, it must be implemented in as many components as there are models, or, if the design platform supports it, it may be implemented as a generic component. Model-specific components require the recoding of the same core behavior several times, and generic components may not take model- specific features into account. In this paper, we introduce the notion of domain-polymorph component. Such a component is able to adapt a core behavior to the semantics of several computation models. The core behavior is implemented only once and is automatically adapted to the semantics of different computation models. Domain-polymorph components can be chosen by a system designer and integrated in a computation model: they will benefit from an appropriate execution environment and their semantics will be adapted to the host model. The designer will have the choice for several parameters of the adaptation. Contrary to generic components, such components adapt their behavior to the host model instead of letting the host model interpret their generic behavior. We also present an implementation of the concept of domain-polymorph component in the Ptolemy~II framework

PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation

Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. This allows for the interactive data exploration of the largest datasets. In this paper we introduce the first framework for parallel online aggregation in which the estimation virtually does not incur any overhead on top of the actual execution. We define a generic interface to express any estimation model that abstracts completely the execution details. We design a novel estimator specifically targeted at parallel online aggregation. When executed by the framework over a massive $8\text{TB}$ TPC-H instance, the estimator provides accurate confidence bounds early in the execution even when the cardinality of the final result is seven orders of magnitude smaller than the dataset size and without incurring overhead.Comment: 36 page

Ontology-based metrics computation for business process analysis

Business Process Management (BPM) aims to support the whole life-cycle necessary to deploy and maintain business processes in organisations. Crucial within the BPM lifecycle is the analysis of deployed processes. Analysing business processes requires computing metrics that can help determining the health of business activities and thus the whole enterprise. However, the degree of automation currently achieved cannot support the level of reactivity and adaptation demanded by businesses. In this paper we argue and show how the use of Semantic Web technologies can increase to an important extent the level of automation for analysing business processes. We present a domain-independent ontological framework for Business Process Analysis (BPA) with support for automatically computing metrics. In particular, we define a set of ontologies for specifying metrics. We describe a domain-independent metrics computation engine that can interpret and compute them. Finally we illustrate and evaluate our approach with a set of general purpose metrics

The CIAO multiparadigm compiler and system: A progress report

Abstract is not available