Frequent spatio-temporal patterns in trajectory data warehouses

Abstract

Abstract. In this paper we present an approach for storing and aggregating spatio-temporal patterns by using a Trajectory Data Warehouse (TDW). In particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval. We resort to a TDW, based on a data cube model, having spatial and temporal dimensions, discretized according to a hierarchy of regular grids, and whose facts are sets of trajectories which intersect the spatiotemporal cells of the cube. The idea is to enrich such a TDW with a new measure: frequent patterns obtained from a data-mining process on trajectories. As a consequence these patterns can be analysed by the user at various levels of granularity by means of OLAP queries. The research issues discussed in this paper are (1) the extraction/mining of the patterns to be stored in each cell, which requires an adequate projection phase of trajectories before mining; (2) the spatio-temporal aggregation of patterns to answer roll-up queries, which poses many problems due to the holistic nature of the aggregation function.

Ad-hoc OLAP on Trajectory Data

“…Hence, different definitions demand different data cubes (as it was shown in Figure 2). An extension of [16] can be found in [13], which discusses storing and aggregation issues for frequent spatiotemporal patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval.…”

Frequent itemset mining of uncertain data streams using the damped window model

“…Since its introduction, FIM has been the subject of numerous studies [4,5] and it has also played an important role in the mining of other patterns (e.g., interesting rules [11], associative classification rules [13], spatio-temporal patterns [15], maximal * Corresponding author: C.K.-S. Leung. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Spatio-temporal Similarity Measure for Network Constrained Trajectory Data

“…Numerous spatio-temporal data with location and time attributes are able to be acquired from GPS devices, RFID sensors, RADAR or satellites. 1 Generally, it is assumed that a moving object moves in the X-Y plane and the traversed path is a set of line segments in (x, y, t) space. These paths are defined as trajectories.…”

“…However, raw position information in trajectories of moving objects can not be stored and maintained for a long time due to the huge volume or privacy reasons. 1 In this paper, we focus on efficient spatio-temporal feature extraction and similarity analysis of network constrained trajectories. Meanwhile, semantics as stops 3 is introduced and refined for trajectory partition, which has been proved to be useful for improving the similarity measure effect to some extent.…”