Abstract
Our research in progress project aims the design of a method for the development of Dublin Core Application Profiles (Me4DCAP). This paper describes Me4DCAP V0.2, an early version of a method for the development of DCAP. The development of Me4DCAP was based on a Design Science Research methodological approach and has as starting points the Singapore framework for DCAP and the Rational Unified Process; integrates knowledge from: (i) the practices of the metadata community concerning DCAP development, and (ii) software development processes and techniques, focusing on the early stages of the processes that deal with data modeling. Me4DCAP establishes a way for the development of a DCAP. It establishes which are the activities, when they should take place, how they interconnect, and which deliverables they will bring about; it also suggests which techniques should be used to build these deliverables. © DCMI 2013.

Abstract
We present a novel mathematical model and a mathematical programming based approach to deliver superior quality solutions for the single machine capacitated lot sizing and scheduling problem with sequence-dependent setup times and costs. The formulation explores the idea of scheduling products based on the selection of known production sequences. The model is the basis of a matheuristic, which embeds pricing principles within construction and improvement MIP-based heuristics. A partial exploration of distinct neighborhood structures avoids local entrapment and is conducted on a rule-based neighbor selection principle. We compare the performance of this approach to other heuristics proposed in the literature. The computational study carried out on different sets of benchmark instances shows the ability of the matheuristic to cope with several model extensions while maintaining a very effective search. Although the techniques described were developed in the context of the problem studied, the method is applicable to other lot sizing problems or even to problems outside this domain.

Abstract

Abstract
The use of mobile phones to make payments is already a wide-spreading reality. While some mobile payment solutions achieved a considerable success and are already in use, others failed in the pilot phase. Nevertheless, there is an area where mobile payments have been quite successful: mobile ticketing in public transport. In fact, there are several advantages of mobile ticketing over traditional ticketing systems, such as queue avoidance, ubiquitous and remote access to payment, and the lack of need to carry coins and cash. This paper intends to propose a mobile payment system to be implemented in the Public Transport of Metropolitan Area of Oporto. After defining the payment ticketing model, a prototype was developed and tested by a sample of users. These tests allowed gathering some feedback about the feasibility of the system as well as useful insights about the concept, new in public transport in Portugal. The findings attained so far suggest that users considered the system extremely useful, since it is more convenient than traditional systems, improving the travelling process and experience. It was also clear that users valued the integration of additional and complementary services with mobile payments, such as real-time traffic information, maps and schedules. There are also several barriers to the adoption of such a system elicited by users, such as premium price, complex interfaces and perceived risks, such as security and privacy concerns.

Abstract
This position paper proposes a framework based on a feature clustering method using Emergent Self-Organizing Maps over streaming data (Ubi-SOM) and Ramex-Forum - a sequence pattern mining model for financial time series modeling based on observed instantaneous and long term relations over market data. The proposed framework aims at producing realistic monte-carlo based simulations of an entire portfolio behavior over distinct market scenarios, obtained from models generated by these two approaches. © 2013 IJCAI.

Abstract
The Web has profoundly changed the way we access information. The increase in the amount of information available and the easy access to the Web have contributed to the popularity of search in this medium. While this is true in several domains, it is particularly significant in the health domain. In fact, search for health information is the third most popular online activity and it is performed by almost 3 out of 4 American Internet users. Besides its popularity, health web search is particularly important to inform health consumers, encouraging them to become more participatory in their health management. In the health domain, the context surrounding the search is extremely rich and we believe it can contribute to improve system's performance. Although information retrieval has made significant progresses supporting its retrieval methods solely on the query and document collection, it has been recognized that context should be explored. Context can, for example, be used to disambiguate a query, to retrieve documents adjusted to the expertise of the searcher or to adjust documents to the patient's medical record. In this dissertation we investigate the effects of context features in consumer health information retrieval. In addition, we propose and evaluate strategies to use context features in query formulation support. The popularity of web search on the health domain made us focus on Web retrieval. We opt to concentrate on health consumers due to the lack of research focusing on this specific public. The thesis behind our work is that consumer health information retrieval is affected by context features, which can be used in query formulation support to improve retrieval performance. In three exploratory studies, we analyze how a large number of features affect different outcomes of the retrieval process. Based on findings from these studies and on the importance of query formulation support in a domain where terminology can be a barrier and translations to other languages are not always obvious, we explored the impact of query translations in users with different characteristics. Based on the assumption that a query using a language that is popular on the Web may easily reach high-quality contents, one study analyzes the effects of translating a query to the English language in users with different levels of English proficiency. Findings show that users having, at least, elementary English proficiency benefit from English query suggestions. The other studies focus on query terminology translation, that is, translation between lay and medico-scientific terminology, considering users' health literacy and topic familiarity. Although several strategies have been previously proposed to overcome the terminology gap between health consumers and web documents, none considers users health literacy and topic familiarity. Findings suggest that users with inadequate health literacy and users who are unfamiliar with the topic should be provided with recommendations of lay queries. On the other hand, users with higher health literacy or higher topic familiarity should be given alternative queries using medico-scientific terminology. Based on the above findings, we developed a query suggestion system that, using domain information gathered from an existing consumer health vocabulary, identifies the medical concepts included in the query and returns four types of suggestions combining the Portuguese or English languages with the lay or medico-scientific terminologies. We found that suggestions offered by the system had a good acceptance, with English suggestions being preferred to Portuguese ones in basic and proficient users and medico-scientific suggestions being preferred to lay ones in higher levels of health literacy. We concluded that a retrieval system including the implemented suggestion strategy without any kind of personalization tends to be better than a system without suggestions with respect to precision, correctness of the resulting knowledge and also of its incorrectness. We also concluded that this system tends to be slightly worse in terms of motivational relevance. Of these, only the incorrectness difference is significant. This is extremely relevant in the health domain where incorrect information can have serious consequences. Moreover, we also found that the personalization of this system to users' English proficiency and health literacy, biasing users towards the suggestions more beneficial to them, outperforms the system without personalization, in terms of medical accuracy of the obtained knowledge.