A Framework to Support Continuous Range Queries Over Multi-Attribute Trajectories

jianqiu Xu, Zhifeng Bao, Hua Lu

Abstract:

Emerging applications over spatio-temporal trajectories require representing the data from diverse aspects. We study multiattribute
trajectories each of which consists of a sequence of time-stamped locations and a set of attributes characterizing diverse
aspects. We investigate continuous range queries over multi-attribute trajectories. Such a query returns trajectories whose attributes
contain expected values and whose locations are always within a distance threshold to the query trajectory during the entire
overlapping time period. To efficiently answer the query, an optimal method of partitioning the trajectories is proposed and an index
structure is developed to support the combined search using both spatio-temporal parameters and attribute values. Query algorithms
and auxiliary structures are developed, accompanied with optimization strategies and thorough theoretical analysis. Using both real
and synthetic datasets, we carry out comprehensive experiments in a prototype database system to evaluate the efficiency and
scalability of our designs. The experimental results show that our approach outperforms six alternative approaches by a factor of 5-50x
on large datasets.