Large-scale Analysis of Event Data

With the availability of numerous sources and the development of sophisticated text analysis and information retrieval techniques, more and more spatio-temporal data are extracted from texts such as news documents or social network data. Temporal and geographic information obtained this way often form some kind of event, describing when and where something happened. An important task in the context of business intelligence and document exploration applications is the correlation of events in terms of their temporal, geographic or even semantic properties. In this paper we discuss the tasks related to event data analysis, ranging from the extraction of events to determining events that are similar in terms of space and time by using skyline processing and clustering. We present a framework implemented in Apache Spark that provides operators supporting these tasks and thus allows to build analysis pipelines.