Shashi Shekhar

TIST Journal 2024 Journal Article

Physics-Based Abnormal Trajectory Gap Detection

• Arun Sharma
• Subhankar Ghosh
• Shashi Shekhar

Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns, such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object's possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this article, we propose a space time-aware gap detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a dynamic region merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique.

TIST Journal 2022 Journal Article

Analyzing Trajectory Gaps to Find Possible Rendezvous Region

• Arun Sharma
• Shashi Shekhar

Given trajectory data with gaps, we investigate methods to identify possible rendezvous regions. The problem has societal applications such as improving maritime safety and regulatory enforcement. The challenges come from two aspects. First, gaps in trajectory data make it difficult to identify regions where moving objects may have rendezvoused for nefarious reasons. Hence, traditional linear or shortest path interpolation methods may not be able to detect such activities, since objects in a rendezvous may have traveled away from their usual routes to meet. Second, user detecting a rendezvous regions involve a large number of gaps and associated trajectories, making the task computationally very expensive. In preliminary work, we proposed a more effective way of handling gaps and provided examples to illustrate potential rendezvous regions. In this article, we are providing detailed experiments with both synthetic and real-world data. Experiments on synthetic data show that the accuracy improved by 50 percent, which is substantial as compared to the baseline approach. In this article, we propose a refined algorithm Temporal Selection Search for finding a potential rendezvous region and finding an optimal temporal range to improve computational efficiency. We also incorporate two novel spatial filters: (i) a Static Ellipse Intersection Filter and (ii) a Dynamic Circle Intersection Spatial Filter. Both the baseline and proposed approaches account for every possible rendezvous pattern. We provide a theoretical evaluation of the algorithms correctness and completeness along with a time complexity analysis. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves the area pruning effectiveness and computation time over the baseline technique. We also performed experiments based on accuracy and precision on synthetic dataset on both proposed and baseline techniques.

TIST Journal 2021 Journal Article

Significant DBSCAN+: Statistically Robust Density-based Clustering

• Yiqun Xie
• Xiaowei Jia
• Shashi Shekhar
• Han Bao
• Xun Zhou

Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.

TIST Journal 2021 Journal Article

Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

• Jayant Gupta
• Carl Molnar
• Yiqun Xie
• Joe Knight
• Shashi Shekhar

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN ) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.

TIST Journal 2020 Journal Article

Discovering Interesting Subpaths with Statistical Significance from Spatiotemporal Datasets

• Yiqun Xie
• Xun Zhou
• Shashi Shekhar

Given a path in a spatial or temporal framework, we aim to find all contiguous subpaths that are both interesting (e.g., abrupt changes) and statistically significant (i.e., persistent trends rather than local fluctuations). Discovering interesting subpaths can provide meaningful information for a variety of domains including Earth science, environmental science, urban planning, and the like. Existing methods are limited to detecting individual points of interest along an input path but cannot find interesting subpaths. Our preliminary work provided a Subpath Enumeration and Pruning (SEP) algorithm to detect interesting subpaths of arbitrary length. However, SEP is not effective in avoiding detections that are random variations rather than meaningful trends, which hampers clear and proper interpretations of the results. In this article, we extend our previous work by proposing a significance testing framework to eliminate these random variations. To compute the statistical significance, we first show a baseline Monte-Carlo method based on our previous work and then propose a Dynamic Search-and-Prune (D-SAP) algorithm to improve its computational efficiency. Our experiments show that the significance testing can greatly suppress the noisy detections in the output and D-SAP can greatly reduce the execution time.

TIST Journal 2019 Journal Article

Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity

• Zhe Jiang
• Arpan Man Sainju
• Yan Li
• Shashi Shekhar
• Joseph Knight

Class ambiguity refers to the phenomenon whereby similar features correspond to different classes at different locations. Given heterogeneous geographic data with class ambiguity, the spatial ensemble learning (SEL) problem aims to find a decomposition of the geographic area into disjoint zones such that class ambiguity is minimized and a local classifier can be learned in each zone. The problem is important for applications such as land cover mapping from heterogeneous earth observation data with spectral confusion. However, the problem is challenging due to its high computational cost. Related work in ensemble learning either assumes an identical sample distribution (e.g., bagging, boosting, random forest) or decomposes multi-modular input data in the feature vector space (e.g., mixture of experts, multimodal ensemble) and thus cannot effectively minimize class ambiguity. In contrast, we propose a spatial ensemble framework that explicitly partitions input data in geographic space. Our approach first preprocesses data into homogeneous spatial patches and uses a greedy heuristic to allocate pairs of patches with high class ambiguity into different zones. We further extend our spatial ensemble learning framework with spatial dependency between nearby zones based on the spatial autocorrelation effect. Both theoretical analysis and experimental evaluations on two real world wetland mapping datasets show the feasibility of the proposed approach.

IS Journal 2015 Journal Article

Intelligent Shelter Allotment for Emergency Evacuation Planning: A Case Study of Makkah

• KwangSoo Yang
• Apurv Hirsh Shekhar
• Faizan Ur Rehman
• Hatim Lahza
• Saleh Basalamah
• Shashi Shekhar
• Imtiaz Ahmed
• Arif Ghafoor

Given maps of an evacuee population, shelter destinations, and a transportation network, the goal of intelligent shelter allotment (ISA) is to assign routes, exits, and shelters to evacuees for quick and safe evacuation. ISA is important due to emergency planning and response applications, but it's also challenging due to movement conflicts and transportation-network choke points. Current practices based on the nearest exit or shelter (NES) paradigm and computational approaches such as capacity-constrained route planning (CCRP) have shortcomings in meeting these challenges. The authors of this article propose a novel approach--Crowd-separated Allocation of Routes, Exits and Shelters (CARES)--based on the idea of spatial anomaly avoidance. Experiments case study regarding Hajj in Makkah, Saudi Arabia, show that CARES meets both challenges by providing faster evacuation than NES and much lesser evacuee movement conflict risk than CCRP.

TCS Journal 2001 Journal Article

Optimal placement of data replicas in distributed database with majority voting protocol

• Shashi Shekhar
• Weili Wu

In a distributed database system, data replicas are placed at different locations of a network to achieve the high data availability in presence of link failure. Optimal placements of data replicas have been studied extensively in the literature for various protocol. In this paper, we present a sufficient and necessary condition for optimality of a placement of odd number of data replicas in a ring network with majority voting protocol. As a corollary, we give a proof of a recent conjecture of Hu et al. (manuscript, 1999). Moreover, we also give a simple algorithm to find optimal placements in tree network with majority voting protocol.

IJCAI Conference 1995 Conference Paper

Deadline Compliance, Predictability, and On-line Optimization in Real-Time Problem Solving

• Babak Hamidzadeh
• Shashi Shekhar

IJCAI Conference 1989 Conference Paper

Minimizing Response Times in Real Time Planning and Search

• Shashi Shekhar
• Soumitra Dutta