The proliferation of bioinformatics activities brings new challenges - how to understand and organise these resources, how to exchange and reuse successful experimental procedures, and to provide interoperability among data and tools. This paper describes an effort toward these directions. It is based on combining research on ontology management, AI and scientific workflows to design, reuse and annotate bioinformatics experiments. The resulting framework supports automatic or interactive composition of tasks based on AI planning techniques and takes advantage of ontologies to support the specification and annotation of bioinformatics workflows. We validate our proposal with a prototype running on real data.

Summary The significant improvement in processing power, communication, energy consumption, and the size of computational devices has led to the emergence of the Internet of Things (IoT). IoT projects raise many challenges, such as the interoperability between IoT applications because of the high number of sensors, actuators, services, protocols, and data associated with these systems. Semantics solves this problem by using annotations that define the role of each IoT element and reduces the ambiguity of information exchanged between the devices. This work presents SWoTPAD, a semantic framework that helps in the development of IoT projects. The framework is designer oriented and provides a semantic language that is more user‐friendly than OWL‐S and WSML and allows the IoT designer to specify devices, services, environment, and requests. Following this, it makes use of these specifications and maps them for RESTful services. Additionally, it generates an automatic service composition engine that is able to combine services needed to handle complex user requests. We validated this approach with two case studies. The former concerns a residential security system and the latter, the cloud application deployment. The average time required for service discovery and automatic service composition corresponds to 72.9% of the service execution time in the case study 1 and 64.4% in the case study 2.

The basic objective of a predictive algorithm for collaborative filtering (CF) is to suggest items to a particular user based on his/her preferences and other users with similar interests. Many algorithms have been proposed for CF, and some works comparing sub-sets of them can be found in the literature; however, more comprehensive comparisons are not available. In this work, a meaningful sample of CF algorithms widely reported in the literature were chosen for analysis; they represent different stages in the evolutive process of CF, starting from simple user correlations, going through online learning, up to methods which use classification techniques. Our main purpose is to compare these algorithms when applied on multi-valued ratings.Experiments were conducted on three well-known datasets with different characteristics, using two protocols and four evaluation metrics, representing coverage, accuracy, reliability and agreement of predictions with respect to real values. Results from such experiments showed that the memorybased method is a good option because its results are more precise and reliable compared with the other methods. Online Learning methods exhibit a good level of accuracy with low variation, which makes them reliable models. On the other hand, Support Vector Machines generate predictions with acceptable agreement; however, their accuracy depends on the characteristics of the input data. Finally, DepenPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

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This article illustrates the application of the Gaia methodology to the development of a system for Selective Dissemination of Information on the Web, as well as the integration of Gaia with AUML. This allows a concrete design closer to implementation and the developer offers a better insight into the multiagent system that is being built. Gaia is an agent-oriented organisational methodology that generates an abstract model based on roles describing the system agents, their services and interactions. AUML, as an agent-based extension of the graphical language UML, uses that model as input and breaks it down into a series of diagrams that facilitates its implementation.