Technologies have been used as a way to improve electronic health records (EHR). Systems that involve the opinion of potential users in design phase have more efficiency, acceptance, and satisfaction of the users. The objective of this paper is to analyze the degree of user satisfaction in the use of an EHR for gestational telemonitoring through usability questionnaire, Attrakdiff. Eight users were selected for the evaluation of EHR. Users agreed that the EHR was task oriented, attractive and very technical, but at the same time simple. The EHR was considered inventive, creative, very good, pleasant and captivating. However, it was not very innovative. The overall evaluation of the system indicated users' satisfaction. Therefore, focusing on the needs and emotions of the user while interacting with a product is a key factor for the success of the software product. When a product meets or exceeds user expectations, it provides a positive experience.

Traditionally, legacy Health applications use software architecture models that make it difficult to reuse components. Reusability is an essential attribute in the software lifecycle, as it improves the quality of applications and reduces maintenance and development costs. This paper proposes the cloud tool Microservice4EHR, which dynamically generates reusable components from existing software artifacts (e.g., graphical interfaces), while conforming to the standards used in the healthcare domain. A software architecture based on Connectors and Microservice components is specified and made tangible by means of three algorithms. The use of both components is applied to a real-world scenario (a Brazilian blood donation center) and serves as an example. As a result, it is possible to notice that Health applications achieve greater reusability when they employ the microservice architecture. Thus, Microservice4EHR enables the use of reusable components in Health application architectures (for both new and legacy systems), increasing software reusability in this context.

A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajectories into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajectories. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semi-supervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are presented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth.

People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can be performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the top-k most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can be saved and how the ranking function can be tuned to enforce pro-environment behaviors.

Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event.

ComeWithMe is an activity oriented carpooling service that enlarges the candidate destinations of a ride request by considering alternative places where the desired activity can be performed. It is based on the observation that individuals often move towards a place to perform an activity while the activity is often not strictly associated with a single place, as one may go for shopping or eating to many different locations. Activity-oriented carpooling hugely increases the number of rides matching a query, thus introducing requirements on system responsiveness and ranking effectiveness that are not common to traditional carpooling services. The demoed system implements the ComeWithMe service in almost its entirety, and includes the back-end and a user-friendly mobile application for smart-phones aimed at achieving users' acceptance and usability.

The increasing availability of positioning data fostered a number of new applications where the knowledge about mobility patterns is essential. However, the research conducted so far on mobility analysis focused on the geometric aspect at the expenses of the semantics of the movement. In this paper, we offer a new vision of semantic trajectory data warehouse that combines the pure geometrical features in terms of temporal and geographical coordinates with more semantic-related contextual information such as the goal of the movement and the transportation means of the moving object, or the activity performed by the moving entity. The conceptual model proposed is called SWOT and is capable of combining these aspects, thus providing a considerable improvement in answering semantic enriched mobility queries.

An important problem in the knowledge discovery of trajectories is segmentation in subparts (subtrajectories). Existing algorithms for trajectory segmentation generally use explicit criteria to create segments. In this article, we propose segmenting trajectories using a novel, unsupervised approach, in which no explicit criteria are predetermined. To achieve this, we apply the Minimum Description Length (MDL) principle, which can measure homogeneity in the trajectory data by computing the similarities between landmarks (i.e. representative points of the trajectory) and the points in their neighborhood. Based on the homogeneity measurements, we propose an algorithm named Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation (GRASP-UTS), which is a meta-heuristic that builds segments by modifying the number and positions of landmarks. We perform experiments with GRASP-UTS in two real-world datasets, using segment purity and coverage metrics to evaluate its efficiency. Experimental results demonstrate that GRASP-UTS correctly segmented sample trajectories without predetermined criteria, by computing similarities between landmarks and other trajectory points.

In recent years, the exponential growth of positioning-enabled devices have allowed us to study the mobility behavior of individuals analyzing their collected tracks. In this context, a small, but steadily increasing part of the literature is looking at the semantic aspects of mobility. This paper presents a contribution to this trend, and is concerned with the definition of semantic regularity profiles. We based our methodology on the entropy of both spatial and temporal frequency of visits of individuals to places to perform an activity. This allows us to define the concept of semantic regular or irregular user behavior identifying users who are more or less loyal to the same places in contrast to the flexibility in visiting different places to perform an activity. We experiment on a crowdsensed trajectory dataset annotated by the visited Points of Interest which represent the activity performed. Analysis evidence that the regularity depends on the particular activity to be performed.

The current commercial and academic OLAP tools do not process XML data that make use of XLink. To develop OLAP systems for helping in the analysis of such data, this paper proposes an analytical query language, namely LMDQL (Link-based and Multidimensional Query Language). Before presenting LMDQL, a multidimensional data model based on XLink is given. Then, the LMDQL syntax is detailed together with its operators and a discussion on its processor architecture. Finally, a case study based on XBRL Dimensions documents is given. XBRL can be seen as a standard used in the publication of financial data over internet and contains linkbases based on XLink.

A number of studies have been developed in recent years aimed at integrating pertinent concepts and technologies for analytical multidimensional (OLAP) and geographic (GIS) processing environments. This type of integrated environment has been identified as SOLAP (Spatial OLAP). However, due to the fact that these two technologies were conceived with different purposes in mind, the interaction of the two environments is not an easy task and even with so much research being developed, there remain unresolved issues that merit exploration. One such issue refers to aggregation functions for measures. These functions are currently used in the definition of multidimensional and geographic data cubes. The aim of this paper is to present a set of aggregation functions for geographic measures. We also show these functions in practice, by taking into account their use with a SOLAP architecture prototype. This SOLAP prototype is based on a model for Geographic Data Warehouse (GDW), a data cube model and a geographic multidimensional query language.