Publications


Spatiotemporal Range Pattern Queries on Large-scale Co-movement Pattern Datasets

Shahab Helmi and Farnoush Banaei-Kashani

IEEE BigData '17

Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals, and vehicles. In particular, mining patterns from co-movements of objects (such as players of a sports team, joints of a person while walking, and cars in a transportation network) can lead to the discovery of interesting patterns (e.g., offense tactics of the sports team, gait signature of the person, and driving behaviors causing heavy traffic). With our prior work, we proposed efficient algorithms to mine frequent co-movement patterns from trajectory datasets. In this paper, we focus on the problem of efficient query processing on massive co-movement pattern datasets generated by such pattern mining algorithms. Given a dataset of frequent co-movement patterns, various spatiotemporal queries can be posed to retrieve relevant patterns among all generated patterns from the pattern dataset. We term such queries “pattern queries”. Co-movement patterns are often numerous due to combinatorial complexity of such patterns, and therefore, co-movement pattern datasets grow very large, rendering naive execution of the pattern queries ineffective. In this paper, we propose novel index structures and query processing algorithms for efficient answering of two families of range pattern queries on massive co-movement pattern datasets, namely, spatial range pattern queries and temporal range pattern queries. Our extensive empirical studies with three real datasets have demonstrated the efficiency of the proposed methods.

Dowlonad Slides and Cite: Will be updated soon!


Efficient Processing of Spatiotemporal Pattern Queries on Historical Frequent Co-Movement Pattern Datasets

Shahab Helmi and Farnoush Banaei-Kashani

MATES '17 Proceedings of the 43rd VLDB International Conference on Mobility Analytics for Spatio-temporal and Social Data

Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals, and vehicles. In particular, mining patterns from co-movements of objects (such as movements by players of a sports team, joints of a person while walking, and cars in a transportation network) can lead to the discovery of interesting patterns (e.g., offense tactics of a sports team, gait signature of a person, and driving behaviors causing heavy traffic).
Given a dataset of frequent co-movement patterns, various spatial and spatiotemporal queries can be posed to retrieve relevant patterns among all generated patterns from the pattern dataset. We term such queries, pattern queries. Co-movement patterns are often numerous due to combinatorial complexity of such patterns, and therefore, co-movement pattern datasets often grow very large in size, rendering naive execution of the pattern queries ineffective. In this paper, we propose the FCPIR framework, which offers a variety of index structures for efficient answering of various range pattern queries on massive co-movement pattern datasets, namely, spatial range pattern queries, temporal range (time-slice) pattern queries, and spatiotemporal range pattern queries.

Dowlonad Slides and Cite:


Mining frequent episodes from multivariate spatiotemporal event sequences

Shahab Helmi and Farnoush Banaei-Kashani

IWGS '16 Proceedings of the 7th ACM SIGSPATIAL International Conference on GeoStreaming

Thanks to recent prevalence of location sensors, collecting massive spatiotemporal datasets containing moving object trajectories has become possible, providing an exceptional opportunity to derive interesting insights about behavior of the moving objects such as people, animals and vehicles. In particular, mining patterns from interdependent co-movements of objects in a group/team (such as players of a sports team, ants of a colony in search of food, and cars in a congested downtown district) can lead to the discovery of interesting patterns (e.g., offense tactics and strategies of a sports team). Various trajectory mining, and in particular frequent episode mining (FEM), approaches have been proposed to discover such patterns from trajectory datasets. However, the existing FEM approaches neither are applicable to multivariate spatial (MVS) event sequences nor consider and leverage all spatial features of the input data. In this paper, we first introduce a Apriori property which extends the well-known Apriori property to consider the spatial properties of the input data. We present a data preprocessing technique that leverages the aforementioned Spatial Apriori to reduce the search space of our problem by filtering out irrelevant events from a given MVS event sequence. Second, we present the MVS-FEM framework which efficiently discovers co-movements patterns from MVS datasets. The efficiency of our proposed solutions is evaluated using a real dataset.

Dowlonad Slides and Cite:


Read More and Download The Paper