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

Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

In this paper we explore a unique, high-value spatio-temporal dataset that results from the fusion of three data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed), the corresponding fish catch reports (i.e., the quantity and type of fish caught), and relevant environmental data. The result of that fusion is a set of semantic trajectories describing the fishing activities in Northern Adriatic Sea over two years. We present early results from an exploratory analysis of these semantic trajectories, as well as from initial predictive modeling using Machine Learning. Our goal is to predict the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation useful for fisheries management. Our predictive results are preliminary in both the temporal data horizon that we are able to explore and in the limited set of learning techniques that are employed on this task. We discuss several approaches that we plan to apply in the near future to learn from such data, evidence, and knowledge that will be useful for fisheries management. It is likely that other centers of intense fishing activities are in possession of similar data and could use the methods similar to the ones proposed here in their local context

Preface

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A Framework for Context-aware Trajectory

The recent advances in technologies for mobile devices, like GPS and mobile phones, are generating large amounts of a new kind of data: trajectories of moving objects. These data are normally available as sample points, with very little or no semantics. Trajectory data can be used in a variety of applications, but the form as the data are available makes the extraction of meaningful patterns very complex from an application point of view. Several data preprocessing steps are necessary to enrich these data with domain information for data mining. In this chapter,we present a general framework for context-aware trajectory data mining. In this framework we are able to enrich trajectories with additional geographic information that attends the application requirements. We evaluate the proposed framework with experiments on real data for two application domains: traffic management and an outdoor gam

AUTOMATISE: Multiple Aspect Trajectory Data Mining Tool Library

With the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings

Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Warehouse

The effective analysis and understanding of huge amount of mobility data have been a hot research topic in the last few years. In this paper, we introduce Mob-Warehouse, a Trajectory Data Warehouse which goes a step further to the state of the art on mobility analysis since it models trajectories enriched with semantics. The unit of movement is the (spatio-temporal) point endowed with several semantic dimensions including the activity, the transportation means and the mobility pat- terns. This model allows us to answer the classical Why, Who, When, Where, What, How questions providing an aggregated view of different aspects of the user movements, no longer limited to space and time. We briefly present an experiment of Mob-Warehouse on a real dataset

Semantic trajectories modeling and analysis

Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility datamanagement, and a survey of the approaches and techniques for: (i) constructing trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the article surveys the new privacy issues that arise due to the semantic aspects of trajectories