Abstract
Weather and sea-related forecasts provide crucial insights for the practice of nautical sports such as surf and kite surf, and mobile devices are appropriate interfaces for the visualization of meteorology and operational oceanography data. Data are collected and processed by several agencies and are often obtained from forecast models. Their use requires adaptation and refinement prior to visualisation. We describe a set of semantic data services using standard common vocabularies and interoperable interfaces following the recommendations of the INSPIRE directive. NautiCast, a mobile application for forecast delivery illustrates the adaptation of data at two levels: 1) semantic, with the integration of data from different sources via standard vocabularies, and 2) syntactic, with the manipulation of the spacial and temporal resolution of data to get effective mobile communication. Copyright 2016 ACM.

Abstract
A considerable number of domains deal with large and complex volumes of temporal data. The management of these volumes, from capture, storage, search, transfer, analysis and visualization, still provides interesting challenges. One critical task is the efficient retrieval of data (raw data or intermediate results from analytic tools). Previous work proposed the TravelLight method which reduced the turnaround time and improved interactive retrieval of data from large temporal datasets by exploring the temporal consistency of records in a database. In this work we propose improvements to the method by adopting a new paradigm focused in the management of time intervals instead of solely in data items. A major advantage of this paradigm shift is to enable the separation of the method implementation from any particular temporal data source, as it is autonomous and efficient in the management of retrieved data. Our work demonstrates that the overheads introduced by the new paradigm are smaller than prior overall overheads, further reducing the turnaround time. Reported results concern experiments with a temporally linear navigation across two datasets of one million items. With the obtained results it is possible to conclude that the improvements presented in this work further reduce turnaround time thus enhancing the response of interactive tasks over very large temporal datasets.

Abstract
Many tasks dealing with temporal data, such as interactive browse through temporal datasets, require intensive retrieval from the database. Depending on the user's task, the data retrieved may be too large to fit in the local memory. Even if it fits, the time taken to retrieve the data may compromise user interaction. This work proposes a method, TravelLight, which improves interactive traveling across very large temporal datasets by exploring the temporal consistency of data items. The proposed method consists of two algorithms: the data retrieval and the memory management algorithm, both contributing to improve memory usage and, most important, to reduce the turnaround time. Results are reported concerning experiments with a temporally linear navigation across two datasets of one million items, which differ in the average time span of items. With the obtained results it is possible to conclude that the proposed method reduces turnaround time thus enhancing the response of interactive tasks over very large temporal datasets.

Abstract
The development of a single window system for collaborative Multimodal Logistics services with offer for optimized and integrated door-to-door services is a complex endeavor. The concept was previously worked on by a set of European projects whose results were taken as best practices and lessons learned from the logistics sector. In this current paper we present a background and the major problems faced by the logistics sector and how IT can address them, and the existing expectations for collaborative real time door-to-door logistics Services. Next, we present the main results, consisting of an innovative system addressing these expectations - the Logistics Single Window. Finally we draw the conclusions from the results and present how the results innovate when compared with current state of the art. © 2017 IEEE.

Abstract
The Societas Europaea Herpetologica (SEH) decided in 2006 through its Mapping Committee to implement the New Atlas of Amphibians and Reptiles of Europe (NA2RE: http://na2re.ismai.pt) as a chorological database system. Initially designed to be a system of distributed databases, NA2RE quickly evolved to a Spatial Data Infrastructure, a system of geographically distributed systems. Each individual system has a national focus and is implemented in an online network, accessible through standard interfaces, thus allowing for interoperable communication and sharing of spatial-temporal data amongst one another. A Web interface facilitates the access of the user to all participating data systems as if it were one single virtual integrated data-source. Upon user request, the Web interface searches all distributed data-sources for the requested data, integrating the answers in an always updated and interactive map. This infrastructure implements methods for fast actualisation of national observation records, as well as for the use of a common taxonomy and systematics. Using this approach, data duplication is avoided, national systems are maintained in their own countries, and national organisations are responsible for their own data curation and management. The database could be built with different representation levels and resolution levels of data, and filtered according to species conservation matters. We present the first prototype of NA2RE, composed of the last data compilation performed by the SEH (Sillero et al., 2014). This system is implemented using only open source software: PostgreSQL database with PostGIS extension, Geoserver, and OpenLayers.

Abstract
Index structures are fast-access methods. In the past, they were often used to minimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Examples of software that deals with this fact are in-memory databases and mobile device applications. Within this scope, this paper focuses on index structures to store, access and delete interval-based time-dependent (temporal) data from very large datasets, in the most efficient way. Index structures for this domain have specific characteristics, given the nature of time and the requirement to index time intervals. This work presents an open-source time-efficiency focused variant of the original Interval B+ tree. We designate this variant Improved Interval B+ tree (I2B+ tree). Our contribution adds to the performance of the delete operation by reducing the amount of traversed nodes to access siblings. We performed an extensive analysis of insert, range queries and deletion operations, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split and node order). Results of the experiments validate the logarithmic performance of these operations and propose the best-observed tree parameter ranges.