Abstract
Modern search engines are evolving beyond ad hoc document retrieval. Nowadays, the information needs of the users can be directly satisfied through entity-oriented search, by ranking the entities or attributes that better relate to the query, as opposed to the documents that contain the best matching terms. One of the challenges in entity-oriented search is efficient query interpretation. In particular, the task of semantic tagging, for the identification of entity types in query parts, is central to understanding user intent. We compare two approaches for semantic tagging, within a single domain, one based on a Sesame triple store and another one based on a Lucene index. This provides a segmentation and annotation of the query based on the most probable entity types, leading to query classification and its subsequent interpretation. We evaluate the run time performance for the two strategies and find that there is a statistically significant speedup, of at least four times, for the index-based strategy over the triple store strategy.

Abstract
Port authorities have the need to manage diverse information within the port area under its responsibility regarding their land and sea infrastructures. Through this innovation, which adds value to the port and its activity, it is made an interconnection of strategic areas, with the provision and sharing of structured data in a georeferenced environment. This work presents an innovative platform, based on various modules and allows effective control and efficient management of operations, processes and requirements associated with any sea port. The developed modules are designed to support the activities of business processes in the following areas of the port administration: Heritage, Hydrography, Port Traffic, Dominial, Studies and Works, Safety and Environment. Most of these modules were pioneers in the integration with business process management of portuguese ports of Leixoes and Viana do Castelo.

Abstract
A large amount of duplicate data typically exists across volumes of virtual machines in cloud computing infrastructures. Deduplication allows reclaiming these duplicates while improving the cost-effectiveness of large-scale multitenant infrastructures. However, traditional archival and backup deduplication systems impose prohibitive storage overhead for virtual machines hosting latency-sensitive applications. Primary deduplication systems reduce such penalty but rely on special cluster filesystems, centralized components, or restrictive workload assumptions. Also, some of these systems reduce storage overhead by confining deduplication to off-peak periods that may be scarce in a cloud environment. We present DEDIS, a dependable and fully decentralized system that performs cluster-wide off-line deduplication of virtual machines' primary volumes. DEDIS works on top of any unsophisticated storage backend, centralized or distributed, as long as it exports a basic shared block device interface. Also, DEDIS does not rely on data locality assumptions and incorporates novel optimizations for reducing deduplication overhead and increasing its reliability. The evaluation of an open-source prototype shows that minimal I/O overhead is achievable even when deduplication and intensive storage I/O are executed simultaneously. Also, our design scales out and allows collocating DEDIS components and virtual machines in the same servers, thus, sparing the need of additional hardware.

Abstract
In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.

Abstract
The present study aims (a) to translate and adapt the Igroup Presence Questionnaire (IPQ) to the Portuguese context (semantic equivalence/ conceptual and content validity) and (b) to examine its psychometric properties (reliability and factorial validity). The sample consisted of 478 subjects (285 males and 193 females). The fidelity of the factors varied between 0.53 and 0.83. The confirmatory factor analysis results produced a 14-item version of IPQ-PT, accepting covariance between residual errors of some items of the instrument, as the best structural representation of the data analyzed. The CFA was conducted based on a three-variable model. The fit indexes obtained were X-2/df = 2.647, GFI = .948, CFI = .941, RSMEA = .059, and AIC = 254. These values demonstrate that the proposed Portuguese translation of the IPQ maintains its original validity, demonstrating it to be a robust questionnaire to measure the sense of presence in virtual reality studies. It is therefore recommended for use in presence research when using Portuguese samples.

Abstract