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
This paper presents a PhD thesis proposal in Informatics Engineering, scheduled for completion in July 2018. This PhD thesis is part of Spatio-Temporal Information Systems, with applicability in technological communication tools and visual representation of knowledge, for Digital Media (newspapers, radio and television). It is intended to maximize the efficiency and effectiveness of the value of heterogeneous, multivariate, multidimensional information characteristic of this context, produced and shared by different sources, in different formats. It is hoped that participation in this Doctoral Symposium will enrich and update the work in progress and help the preparation of the PhD thesis proposal.

Abstract
Data analysis and monitoring is currently carried out within enterprises using Business Intelligence tools that are subject to major limitations (as outlined in the state of the art analysis that we perform). Effective visualization support is a very much needed feature in Big Data applications. In this paper we examine the visualisation requirements of a real world banking application, and identify generic visualisation tasks that are essential for doing effective analysis of a complex process that produces amazingly large amounts of data. The requirements for the visualization support that we propose are modelled using an application wireframe that acts a story-board. The effectiveness of the visualization facilities that we propose is demonstrated through their application to the Big Data banking use-case.

Abstract
Usually, a Big Data system has a monitoring system for performance evaluation and error prevention. Although, there are some disadvantages in the way that these tools display the information and its targeted approach to physical components. The main goal is to study visual and interaction mechanisms that allow the representation of monitoring data in grid computing environments, providing the end-user information which can contribute objectively to the system analysis. This paper has the purpose to present the state of the art, carries out an intermediate evaluation of the current work and present the proposed solution.

Abstract
The Global Positioning System (GPS) provides geolocation to a considerable number of applications in domains such as agriculture, commerce, transportation and tourism. Operational factors such as signal noise or the lack of direct vision from the receiver to the satellites, reduce the GPS geolocation accuracy. Urban canyons are a good example of an environment where continuous GPS signal reception may fail. For some applications, the lack of geolocation accuracy, even if happening for a short period of time, may lead to undesired results. For instance, consider the damages caused by the failure of the geolocation system in a city tour-bus transportation that shows location-sensitive data (historical/cultural data, publicity) in its screens as it passes by a location. This work presents an innovative approach for keeping geolocation accurate in mobile systems that rely mostly on GPS, by using computer vision to help providing geolocation data when the GPS signal becomes temporarily low or even unavailable. Captured frames of the landscape surrounding the mobile system are analysed in real-time by a computer vision algorithm, trying to match it with a set of geo-referenced images in a preconfigured database. When a match is found, it is assumed that the mobile system current location is close to the GPS location of the corresponding matched point. We tested this approach several times, in a real world scenario, and the results achieved evidence that geolocation can effectively be improved for scenarios where GPS signal stops being available.