Querying and Mining Moving Object Databases Using Places of Interest

Abstract

The study of moving ob jects has been capturing the attention of Geographic Information System (GIS) researchers. Moving ob jects, carrying location-aware devices, produce tra jectory data in the form of a sample of (Oid , t, x, y)-tuples, that contain ob ject identifier and time-space information. Recently, the notion of stops and moves was introduced. Intuitively, if a moving ob ject spends a sufficient amount of time in a certain geographic place (which we denote a place of interest of an application), this place is considered a stop of the ob ject’s tra jectory. In-between stops, a tra jectory has moves. In this paper we study how moving ob ject data analysis can benefit from replacing raw tra jectory data by a sequence of stops and moves. We first propose a formal model and query language (denoted Lmo ) to express complex queries involving spatial data stored in a GIS, non-spatial data (stored in a data warehouse) and moving ob ject data. This query language also supports different forms of aggregation. We then study the compression of tra jectory data produced by moving ob jects, using the concepts of stops and moves. We show that stops and moves are expressible in Lmo and that there exists a fragment of this language (that can be expressed by means of regular expressions) allowing to talk about temporally ordered sequences of stops and moves. We use this fragment to perform data mining over tra- jectory data. We present an implementation and a case study, and discuss different applications of our approach.