Abstract
The organizations' demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data quality problems) are tailored for data sources with the same schema and sharing the same data model (e.g., relational model). On the other hand, these approaches are highly dependent on a domain expert to specify the data cleaning operations. This paper extends a previously proposed data cleaning methodology that reuses cleaning knowledge specified for other data sources. The methodology is further detailed/refined by specifying the requirements that a data cleaning operations vocabulary must satisfy. Ontologies in RDF/OWL are proposed as the data model for an abstract representation of the data schemas, no matter which data model is used (e.g., relational; graph). Existing approaches, methods and techniques that support the implementation of the proposed methodology, in general, and specifically of the data cleaning operations vocabulary are also presented and discussed in this paper. Copyright

Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain’s specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource’s content, user’s preferences, users’ social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources’ semantic information. The recommendation engine allows the promotion and discovery of unknownunknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources. © Springer International Publishing AG 2016.

Abstract

Abstract
Decision Making is one of the most important activities of the human being. Nowadays decisions imply to consider many different points of view, so decisions are commonly taken by formal or informal groups of persons. Groups exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. Smart Meeting Rooms (SMR) are the election spaces where groups can perform these activities in a local or distributed setting. SMR definition has evolved and in this article we present its evolution side by side with several projects that have been developed during the last decade. We also present the features and evaluation methods present in literature and we make a discussion on the open issues. © 2015 SERSC.

Abstract
Ambient intelligence (AmI) paradigm has been the catalyst for the creation of several types of smart spaces, and the smart meeting rooms (SMR) is one of them. One of the main aims of AmI spaces is the development of middleware able to provide context aware computing specifically in SMR face-to-face, and distributed/asynchronous meeting settings are envisaged. This paper describes OLAVAmIa context aware middleware system which was tested in LAID environment, a smart meeting room. OLAVAmI allows video production focusing on the speaker, an audio-to-text conversion service, and a multimedia database of meetings produced in an autonomous way. To experiment OLAVAmI usage and functionalities, two tools present in LAID test bed were used. These AmI intelligent layer tools cover two application scenarios: a ubiquitous group decision-making and a service reposition in power systems. The results and insights are presented in this article.

Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. In order to aid users in the resource-sharing process, the adoption of an entity that recommends users with access policies for their resources is proposed, by the analysis of (i) resource content, (ii) user preferences, (iii) users' social networks, (iv) semantic information, (v) user feedback about recommendation actions and (vi) provenance/ traceability information gathered from action sensors. A hybrid recommendation engine capable of performing collaborative-filtering was adopted and enhanced to use semantic information. Such recommendation engine translates user and resources' semantic information and aggregates those with other content, using a collaborative filtering technique. Recommendation of access policies over resources promotes the discovery of known-unknown and unknown-unknown resources to other users that could not even know about the existence of such resources. Evaluation to such recommender system is performed.