Soukaina Filali Boubrahimi

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.

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.

Time Series Distance Density Cluster with Statistical Preprocessing.

Previous Distance Density Clustering has shown some promising results for univariate time series datasets. However, due to the nature of time series data and from using Dynamic Time Warping algorithm as the distance measure; Distance Density Clustering is not an efficient heuristic with larger datasets. In this paper we propose a preprocessing step that could augment the algorithm to the parallel case, and speed up the Distance Density Clustering process considerably. We use time series sequence feature: peak numbers, to prune impossible matchings. By doing so, we are able to form preliminary feature clusters, and further clustering is applied within each feature cluster individually. This can narrow down the amount of time series distance computations, and make Distance Density Clustering scalable.

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.