Mining moving object data

Mining Fuzzy Moving Object Clusters

Recent improvements in positioning technology have led to a much wider availability of massive moving object data. One of the objectives of spatiotemporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving o...

Data Warehousing & Mining Techniques on Moving Object Databases

Swarm: Mining Relaxed Temporal Moving Object Clusters

Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may act...

OLAP for moving object data

In this paper, we present an OLAP framework for moving object data. We introduce a new operator GROUP_TRAJECTORIES for group-by operations on moving object data and present two implementation alternatives for computing groups of moving objects for group-by aggregation: group by overlap and group by intersection. We also present an interactive OLAP environment for resolution drill-down/roll-up o...

Towards Object-Relational Data Mining

Mining data of complex data types (e.g. spatial, multimedial, etc.) is deemed an important research frontier in data mining. These data types are often currently modelled according to the object-relational data model. In this paper we face the problem of de ning a general framework for mining object-relational databases instead of concentrating on speci c forms of data mining tailored to speci ...