Visual exploration of movement and event data with interactive time masks

作     者：Natalia Andrienko Gennady Andrienko Elena Camossi Christophe Claramunt Jose Manuel Cordero Garcia Georg Fuchs Melita Hadzagic Anne-Laure Jousselme Cyril Ray David Scarlatti George Vouros 

作者机构：Fraunhofer Institute IAISSankt AugustinGermany City University LondonLondonUK NATO Science and Technology OrganizationCentre for Maritime Research and ExperimentationItaly Naval Academy Research InstituteFrance CRIDA-Reference Center for ResearchDevelopment and Innovation in ATMMadridSpain Boeing Research&Technology EuropeSpain Department of Digital SystemUniversity of PiraeusGreece 

出 版 物：《Visual Informatics》 (可视信息学（英文）)

年 卷 期：2017年第1卷第1期

页      面：25-39页

核心收录：

学科分类：07[理学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：This work was supported in part by EU in project datAcron(grant agreement 687591) 

主　　题：Data visualization Interactive visualization Interaction technique 

摘      要：We introduce the concept of time mask,which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions *** a filter can be applied to time-referenced objects,such as events and trajectories,for selecting those objects or segments of trajectories that fit in one of the selected time *** selected subsets of objects or segments are dynamically summarized in various ways,and the summaries are represented visually on maps and/or other displays to enable *** time mask filtering can be especially helpful in analysis of disparate data(e.g.,event records,positions of moving objects,and time series of measurements),which may come from different *** detect relationships between such data,the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these *** describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a *** example of analysing two real world data collections related to aviation and maritime traffic,we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering.