Computing and Information Systems - Research Publications
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ItemAn effective and versatile distance measure for spatiotemporal trajectories(SPRINGER, 2019-05)The analysis of large-scale trajectory data has tremendous benefits for applications ranging from transportation planning to traffic management. A fundamental building block for the analysis of such data is the computation of similarity between trajectories. Existing work for similarity computation focuses mainly on the spatial aspects of trajectories, but more rarely takes into account time in conjunction with space. A key challenge when considering time is how to handle trajectories that are sampled asynchronously or at variable rates, which can lead to uncertainty. To tackle this problem, we quantify trajectory similarity as an interval, rather than a single value, to capture the uncertainty that can result from different sampling rates and asynchronous sampling. Based on this perspective, we develop a new trajectory similarity measure, Trajectory Interval Distance Estimation, which models similarity computation as a convex optimisation problem. Using two real datasets, we demonstrate that our proposed measure is extremely effective for assessing similarity in comparison to existing state of the art measures.
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ItemAn automated matrix profile for mining consecutive repeats in time series(Springer Nature, 2018-01-01)A key application of wearable sensors is remote patient monitoring, which facilitates clinicians to observe patients non-invasively, by examining the time series of sensor readings. For analysis of such time series, a recently proposed technique is Matrix Profile (MP). While being effective for certain time series mining tasks, MP depends on a key input parameter, the length of subsequences for which to search. We demonstrate that MP’s dependency on this input parameter impacts its effectiveness for finding patterns of interest. We focus on finding consecutive repeating patterns (CRPs), which represent human activities and exercises whilst tracked using wearable sensors. We demonstrate that MP cannot detect CRPs effectively and extend it by adding a locality preserving index. Our method automates the use of MP, and reduces the need for data labeling by experts. We demonstrate our algorithm’s effectiveness in detecting regions of CRPs through a number of real and synthetic datasets.
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ItemCharacteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis(Springer, 2019-01-01)The local intrinsic dimensionality (LID) model enables assessment of the complexity of the local neighbourhood around a specific query object of interest. In this paper, we study variations in the LID of a query, with respect to different subspaces and local neighbourhoods. We illustrate the surprising phenomenon of how the LID of a query can substantially decrease as further features are included in a dataset. We identify the role of two key feature properties in influencing the LID for feature combinations: correlation and dominance. Our investigation provides new insights into the impact of different feature combinations on local regions of the data.
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ItemAutomatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections(Elsevier, 2015-05)Abstract Stay points are important for recognizing significant places from a mobile user’s GPS trajectory. Such places are often located indoors and in urban canyons, where GPS is unreliable. Consequently, mapping a user’s stay point to a Place of Interest (POI) using only GPS data is particularly challenging. Our novel algorithm employs both spatio-temporal density estimation and line count inference to predict and rank a user’s POI(s) at building level accuracy from noisy time-annotated GPS data points. An experimental study demonstrates the superiority of our algorithm against several baseline approaches with a recall of 96.5% for the top 5 retrieved locations.