Dustin Kempton

Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns

In this paper, we investigate using specifically-designated spatiotemporal indexing techniques for mining cooccurrence patterns from spatiotemporal datasets with evolving polygon-based representations. Previously, suggested techniques for spatiotemporal pattern mining algorithms did not take spatiotemporal indexing techniques into account. We present a new framework for mining spatiotemporal co-occurrence patterns that can use various indexing techniques for efficiently accessing data. Two well-studied spatiotemporal indexing structures, Scalable and Efficient Trajectory Index (SETI) and Chebyshev Polynomial Indexing are currently implemented and available in our framework.

Iterative refinement of multiple targets tracking of solar events

In this paper, we combine two approaches to multiple-target tracking: the first is a hierarchical approach to iteratively growing track fragments across gaps in detections, and the second is a network flow based optimization method for data association. We introduce a new parallel algorithm for initial track fragment formation as the base of the hierarchical approach. The network flow based optimization method is then utilized for the remaining levels of the hierarchy. This process is applied to solar data retrieved from the Heliophysics Event Knowledgebase (HEK). We compare our results to labeled data from the same, and show improvements over a non-hierarchical sequential approach.

Spatio-temporal interpolation methods for solar events metadata

This paper introduces three interpolation methods that enrich complex evolving region trajectories that are captured every day from numerous ground-based and space-based solar observatories. The interpolation module takes a trajectory as its input and generates an enriched trajectory with interpolated time-geometry pairs. we created three different interpolation techniques that are: MBR-Interpolation (Minimum Bounding Rectangle Interpolation), CP-Interpolation (Complex Polygon Interpolation), and FP-Interpolation (Filament Polygon Interpolation). The methods combine K-means clustering algorithm, shape signature representation, and linear interpolation to generate the missing polygons. This is the first research of this kind that attempts to address the problem of solar big data interpolation. Finally, we outline future improvements and opportunities for solar data interpolation.

Filling the Gaps in Solar Big Data: Interpolation of Solar Filament Event Instances

This paper introduces a new interpolation method that fills the gap in missing solar filament big data that are captured every day from numerous ground-based and space-based observatories. It proposes a new algorithm that takes two filament event instances and interpolates between them given a cadence. The method combines K-means clustering algorithm, time series shape representation, and linear interpolation to generate the missing filament polygons. This is the first research of this kind that attempts to address the problem of solar big data interpolation. We evaluate the proposed method using area, shape, and distance accuracy criteria. Finally, we outline future improvements and opportunities for solar data interpolation.

Mining At Most Top-Kp Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations

Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.

SOLEV: a video generation framework for solar events from mixed data sources (demo paper)

One of the main strengths of Geographical Information Systems (GIS) is the analysis of spatial and attributive data. Spatiotemporal interpolation techniques allow the expansion of the collected data to the sites where no samples are available. In the context of GIS, the data, be it interpolated or collected, are visual in nature and hard to understand in raw forms. Visualization of complex evolving region trajectories is often times used as an aid to better understand the data and its underlying patterns. In this work, we created SOLEV, a solar event video generation framework that integrates multiple data sources of solar images. This is the first framework of this kind that not only visualizes spatial solar event boundaries, but also the tracked and interpolated spatiotemporal trajectories they form over time.

An Integrated Solar Database (ISD) with Extended Spatiotemporal Querying Capabilities

Over the last decade, the volume of solar big data have increased immensely. However, the availability and standardization of solar data resources has not received much attention primarily due to the scattered structure among different data providers, lack of consensus on data formats and querying capabilities on metadata. Moreover, there is limited access to the derived solar data such as image parameters extracted either from solar images or tracked solar events. In this paper, we introduce the Integrated Solar Database (ISD), which aims to integrate the heterogeneous solar data sources. In ISD, we store solar event metadata, tracked and interpolated solar events, compressed solar images, and texture parameters extracted from high resolution solar images. ISD offers a rich variety of spatiotemporal and aggregate queries served via a web Application Program Interface (API) and visualized through a web interface.

Describing solar images with sparse coding for similarity search

In this work, we present a method of producing image descriptors that is based on max-pooling of sparse codes. We use this method on images from the Solar Dynamics Observatory (SDO). The SDO produces over 70,000 images of the Sun each day, and with so many images being archived, an efficient method for finding similar images in this ever growing dataset is critical. Our method for producing descriptors is advantageous because the results are of a reasonable size for indexing, and are more selective than other methods used in the past. We use sparse coding on learned dictionaries to produce linear decompositions of the input signals. These decompositions are unlike decompositions based on principal component analysis, as we do not impose the constraint that basis vectors be orthogonal. By removing the orthogonal constraint, we are able to more easily adapt the representation to the data, and we show that our initial retrieval results alleviate a problem found to be an issue for this dataset. Specifically, the problem of the immediate temporal neighbor being the most similar in virtually every case.

Towards Feature Selection for Appearance Models in Solar Event Tracking

Classification of solar event detections into two classes, of either the same object at a later time or an entirely different object, plays a significant role in multiple hypothesis solar event tracking. Many features for this task are produced when images from multiple wavelengths are used and compounded when multiple image parameters are extracted from each of these observations coming from NASA’s Solar Dynamics Observatory. Furthermore, each different event type may require different sets of features to accurately accomplish this task. A feature selection algorithm is required to identify important features extracted from the available images and that can do so without a high computational cost. This work investigates the use of a simple feature subset selection method based on the ANOVA F-Statistic measure as a means of ranking the extracted image parameters in various wavelengths. We show that the feature subsets that are obtained through selecting the top K features ranked in this manner produce classification results as good or better than more complicated methods based on searching the feature subset space for maximum-relevance and minimum-redundancy. We intend for the results of this work to lay the foundations of future work towards a robust model of appearance to be used in the tracking of solar phenomena.