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El autor Miguel Toro ha publicado 5 artículo(s):

1 -

2 -

3 - VISUAL PPINOT: A Graphical Notation for Process Performance Indicators (Summary)

Summary of the contribution

Process performance indicators (PPIs) allow the quantitative evaluation of business processes (BPs), providing essential information for decision making. However, PPI management is not only restricted to the evaluation phase of the BPM lifecycle, but also includes a number of steps that must be carried out throughout the whole lifecycle. PPIs need to be defined, the corresponding BPs must be instrumented, PPI values have to be computed, then they can be monitored and analysed using techniques such as business activity monitoring or process mining, and finally, a PPI redefinition can be required in case of the evolution of either the associated BPs or the PPIs themselves. It is common practice today that BPs and PPIs are usually modelled separately using graphical notations for the former and natural language for the latter. This approach makes PPI definitions simple to read and write, but it hinders maintenance consistency between BPs and PPIs. It also requires their manual translation into lower–level implementation languages for their operationalisation, which is a time–consuming, error– prone task because of the ambiguities inherent to natural language definitions. In this article we present Visual ppinot, a graphical notation for defining PPIs together with BP models aimed at facilitating and automating PPI management. This is mainly achieved by means of the following features. First, Visual ppinot is based on the ppinot metamodel, which provides a precise and unambiguous definition of PPIs, thus allowing their automated processing in the different ac- tivities of the lifecycle. Second, Visual ppinot provides traceability by design between PPIs and BPs because PPIs must be explicitly connected to BP elements, thus avoiding inconsistencies and promoting their co–evolution. Finally, Visual ppinot enables a definition of PPIs that is independent of the platforms used to support the PPIs in the BP lifecycle, which reduces vendor lock–in and allows definitions of PPIs encompassing several information systems. In addition, it improves current state–of–the–art proposals in terms of expressiveness and of providing an explicit visualisation of the link between PPIs and BPs. The reference implementation, developed as a complete tool suite, has allowed its validation in a multiple-case study, in which five dimensions were studied: expressiveness, precision, automation, understandability, and traceability.

Autores: Adela del-R??o-Ortega / Manuel Resinas, / Amador Durán / Beatriz Bernárdez / Antonio Ruiz-Cortés / Miguel Toro / 
Palabras Clave:

4 - Automated analysis of cloud offerings for optimal service provisioning (Summary)

Resumen de la contribución

La aparición del paradigma de la computación en la nube ha conllevado un cambio significativo dentro de la industria de las tecnologías de la información, tanto para proveedores de servicios como para los propios consumidores. Así, existen servicios como los de Amazon Elastic Computing Cloud (EC2) o Google Compute Engine que ofrecen computación virtualizada y almacenamiento de recursos (comúmente denominados Infraestructuras como Servicios o IaaS por sus siglas en inglés), de forma que los clientes pueden adquirirlos para reducir los costes de operación de sus sistemas, en comparación con el aprovisionamiento de las mismas infraestructuras de computación en un entorno local. Sin embargo, el aprovisionamiento de servicios en la nube resulta una tarea muy compleja dada la abrumadora variedad de proveedores, configuraciones y opciones de compra disponibles. En este escenario aparecen además diversas dificultades para comparar las ofertas de los distintos proveedores, debido a la heterogeneidad en la descripción de las configuraciones, opciones de compra, o incluso descuentos aplicables. A su vez, las necesidades concretas de los consumidores podrían incluir restricciones adicionales para tener en cuenta una planificación temporal previa en cuanto al número de instancias de IaaS que necesitarán en determinados momentos. Aunque existen algunas herramientas y calculadoras on-line que permiten buscar configuraciones concretas de IaaS, éstas no tienen en cuenta cuestiones como la planificación y la optimización de las opciones de compra. En este trabajo presentamos un framework de análisis automático que es capaz de analizar y comparar ofertas de servicios en la nube de distintos proveedores para obtener un plan de aprovisionamiento óptimo de acuerdo con las necesidades de los consumidores. Dicho plan especifica la cantidad y el tipo de instancias de IaaS que deben adquirirse, junto con la planificación de su uso. Hemos desarrollado un prototipo que ha sido validado en un escenario de virtualización de clases de laboratorio, comparando las opciones de dos proveedores.

Autores: José María García / Octavio Mart??n-D??az / Pablo Fernandez, / Antonio Ruiz-Cortés / Miguel Toro / 
Palabras Clave:

5 - TAPON: a two-phase machine learning approach for semantic labelling

Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then finding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.

Autores: Daniel Ayala / Inma Hernandez / David Ruiz / Miguel Toro / 
Palabras Clave: Information Integration - Machine Learning - Semantic labelling