Semantic Trajectory Data Mining: a User Driven Approach

Trajectories left behind cars, humans, birds or any other moving object are a new kind of data which can be very useful in decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user\u27s point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery

SMoT+NCS: Algorithm for Detecting Non-Continuous Stops

Several algorithms have been proposed in the last years for discovering stops in trajectories of moving objects. Some methods consider as stops the subtrajectories that i) have speed lower than the average trajectory speed, ii) present significant direction changes, iii) have gaps, or iv) intersect a given spatial region. In these approaches a time constraint should be met for the subtrajectory to be considered as a stop, and this constraint is absolute (it is met or not). Indeed, these approaches consider stops as a continuous subtrajectory. In this paper, we show that for several application domains the stops do not need to be continuous, and the time constraint should be relaxed. In summary, we present the definitions of non-continuous stops and present an algorithm to discover a new kind of stops. We evaluate the proposed algorithm with a running example and real trajectory data, comparing it to the most similar approach in the literature, the SMoT algorithm

SMoT+: Extending the SMoT Algorithm for Discovering Stops in Nested Sites

Several methods have been proposed to analyse trajectory data. However, a few of these methods consider trajectory relations with relevant features of the geographic space. One of the best-known methods that take into account the geographical regions crossed by a trajectory is the SMoT algorithm. Nevertheless, SMoT considers only disjoint geographic regions that a trajectory may traverse, while many regions of interest are contained in other regions. In this article, we extend the SMoT algorithm for discovering stops in nested regions. The proposed algorithm, called SMoT+, takes advantage of information about the hierarchy of nested regions to efficiently discover the stops in regions at different levels of this hierarchy. Experiments with real data show that SMoT+ detects stops in nested regions, which are not detected by the original SMoT algorithm, with minor growth of processing time

Retificac?a?o de registro civil de pessoas transexuais e travestis: pra?ticas transdisciplinares / Civil register rectification of transsexuals and transvestites: transdisciplinary practices

Neste estudo entenderemos a psicologia e o direito a partir do conceito de disciplina proposto por Michel Foucault. Estas duas disciplinas trabalham conjuntamente nos mais diferenciados a?mbitos buscando a promoc?a?o dos Direitos Humanos. Apresentamos aqui uma discussa?o a partir do projeto conhecido como “Direito a Identidade: viva seu nome”, que realizou a retificac?a?o de registro civil para transexuais e travestis. Ao nos depararmos com um campo onde as poli?ticas ainda na?o sa?o bem claras, como os Direitos Sexuais e de Ge?nero, somos pressionados para abrirmos nossas pra?ticas, para que possamos operar para ale?m da disciplina institui?da, em prol do acolhimento integral dos assistidos. Ao trabalhamos com o entendimento de que as questo?es de ge?nero podem ser pensadas a partir de construc?o?es sociais, tensionando os bino?mios masculino/feminino, normal/anormal, estamos tambe?m rompendo com o que esta? posto em alguns espac?os do conhecimento, que entendem a transexualidade e a travestilidade a partir de uma patologia. Para buscarmos a construc?a?o de pra?ticas e?ticas e reflexivas, nos vemos construindo espac?os de discussa?o e de transdisciplinariedade, que visam o acesso a direitos por grupos e coletivos que podem encontrar-se muitas vezes, tambe?m, a? margem das poli?ticas pu?blicas

A rule-based method for discovering trajectory profiles

The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people’s profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery.The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people's profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery