James W M Kang

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This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of(More)
Given a spatial network and a collection of activity events (e.g., emergency requests, crime reports, accident reports, etc.), spatial network activity summarization (SNAS) finds a set of k shortest paths based on the activity events. SNAS is important for critical societal applications such as disaster response and crime analysis. SNAS is computationally(More)
Zonal co-location patterns represent subsets of feature- types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these patterns with dynamic parameters (i.e., repeated(More)
Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial(More)
Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial(More)
Developing a model that facilitates the representation and knowledge discovery on sensor data presents many challenges. With sensors reporting data at a very high frequency, resulting in large volumes of data, there is a need for a model that is memory efficient. Since sensor data is spatiotemporal in nature, the model must also support the time dependence(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as planning battlefield tactics, and tracking predator-prey interactions. However, determining suitable interest measure thresholds is a difficult(More)
Given a set of operators and a spatio-temporal network, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient data storage method that minimizes disk I/O access costs. Storing and accessing spatio-temporal networks is increasingly important in many societal applications such as transportation management and emergency(More)
Chronic neuropathic pain is a neuro-immune disorder, characterised by allodynia, hyperalgesia and spontaneous pain, as well as debilitating affective-motivational disturbances (e.g., reduced social interactions, sleep-wake cycle disruption, anhedonia, and depression). The role of the immune system in altered sensation following nerve injury is well(More)
Given a percentage-threshold and readings from a pair of consecutive upstream and downstream sensors, flow anomaly discovery identifies dominant time intervals where the fraction of time instants of significantly mis-matched sensor readings exceed the given percentage-threshold. Discovering flow anomalies (FA) is an important problem in environmental flow(More)