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 fulfil.Such a filter can be applied to time-referenced...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 fulfil.Such 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 intervals.The 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 exploration.The 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 sources.To 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 conditions.We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool.By 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.展开更多
基金This work was supported in part by EU in project datAcron(grant agreement 687591).
文摘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 fulfil.Such 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 intervals.The 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 exploration.The 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 sources.To 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 conditions.We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool.By 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.
