The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of...
Spatiotemporal event sequences (STESs) are the ordered series of event types whose evolving region-based instances frequently follow each other in time and are located closeby. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. As the quality of the discovered STESs is of great importance to the domain experts...
In this paper, we introduce methods for mining spatiotemporal event sequences from event datasets with evolving region objects. Spatiotemporal event sequences are the ordered lists of event types whose event instances frequently follow each other in spatiotemporal context. Two Apriori-based algorithms are designed for the task of spatiotemporal event sequence mining. We provide explanations for interestingness...
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...
An important and challenging task for modern large-scale and high-dimensional data is k-nearest neighbor (kNN) retrieval. Using iDistance as the current state-of-the-art high-dimensional indexing algorithm, this work discusses the theoretical bounds associated with a distance-based indexing technique and proposes several simple optimizations to improve the efficiency and stability of query retrieval...
With the advancements in spatiotemporal co-occurrence pattern and event sequence mining algorithms, spatiotemporal knowledge discovery from solar event datasets has been prominent in solar data mining. This work presents an efficient and extensible data access mechanism specifically designed for spatiotemporal relationships among the solar event instances. Previous indexing strategies primarily focus...
There is a need for comprehensive solutions to address the challenges of spatio-temporal data quality assessment. Emphasis is often placed on the quality assessment of individual observations from sensors but not on the sensors themselves nor upon site metadata such as location and timestamps. The focus of this paper is on the development and evaluation of a representative and comprehensive, interpolation-based...
Spatiotemporal co-occurrence pattern (STCOP) mining refers to discovering the subsets of event types whose instances frequently co-locate in a spatial context and coincide in a temporal context. STCOP mining is the spatiotemporal extension to Frequent Itemset Mining (FIM). Unlike the classical FIM approaches, which are applied on transactional databases, STCOP mining is applied on the spatiotemporal...
Sequential pattern mining from spatiotemporal data has received much attention in recent years due to its broad application domains such as targeted advertising, location prediction for taxi services, and urban planning. The characteristics of spatiotemporal sequences vary widely depending on the discovered knowledge type. Most of the recent approaches focus on the point-based spatiotemporal data...
Due to the tremendous advances in GPS and location-based web services, highly available spatiotemporal trajectory data poses an important challenge - knowledge discovery from trajectories. Knowledge discovery tasks on trajectory big data such as classification, clustering and outlier detection require a dedicated data model, which can support various utility functions and provide a robust object-relational...
Identifying regions of interest (ROIs) in images is a very active research problem as it highly depends on the types and characteristics of images. In this paper we present a comparative evaluation of unsupervised learning methods, in particular clustering, to identify ROIs in solar images from the Solar Dynamics Observatory (SDO) mission. With the purpose of finding regions within the solar images...
In this work we present an alternative approach for large-scale retrieval of solar images using the highly-scalable retrieval engine Lucene. While Lucene is widely popular among text- based search engines, significant adjustments need to be made to take advantage of its fast indexing mechanism and highly-scalable architecture to enable search on image repositories. In this work we describe a novel...
In this work, we discuss the benefits of image compression on FITS image files to perform image retrieval tasks on the enormous NASA Solar Dynamics Observatory (SDO) image repository. With the objective of making solar image files more portable and easy to distribute and archive, we test several lossless compression algorithms as well as lossy compression algorithms in order to determine the rate...
This paper introduces standard benchmarks for automated feature recognition using solar image data from the Solar Dynamics Observatory (SDO) mission. We combine general purpose image parameters extracted in-line from this massive data stream of images with reported solar event metadata records from automated detection modules to create a variety of eventlabeled image datasets. These new large-scale...
In this work we present a composite method for image parameter evaluation using Scale-Invariant Feature Transform (SIFT) descriptors and bag of words representation applied to pre-selected image parameters, with potential applications to solar data and other domains. As one of the main challenges in computer vision, image parameter evaluation has been approached from supervised and unsupervised perspectives...
In this work we present our first results on the ambitious task of providing region-based querying capabilities to our existing Solar Dynamics Observatory (SDO) content-based image-retrieval (CBIR) system. By taking advantage of precomputed image descriptors, we calculate region-based histogram signatures for our training set of previously identified solar events. With these signatures we then explore...
This paper introduces a new public benchmark dataset of solar image data from the Solar Dynamics Observatory (SDO) mission. This is the first release, which contains over 15,000 images and nearly 24,000 solar events, spanning the first six months of 2012. It combines region-based event labels from six automated detection modules, ten pre-computed image parameters for each cell over a grid-based segmentation...
Spatio-temporal co-occurring patterns represent subsets of event types that occur together in both space and time. In comparison to previous work in this field, we present a general framework to identify spatio-temporal co occurring patterns for continuously evolving spatio-temporal events that have polygon-like representations. We also propose a set of measures to identify spatio-temporal co-occurring...
In this work we report on the transfer of image parameters that produce good results for medical images to the domain of solar image analysis. Using the first solar domain-specific benchmark dataset that contains multiple types of solar phenomena we discovered during our work for constructing a content-based image retrieval (CBIR) system for NASA's Solar Dynamics Observatory (SDO) mission that we...
In this paper, we put forward our approach for answering aggregated queries over imprecise data using domain specific taxonomies. A new concept we call the weighted hierarchical hyper graph has been introduced, which helps in answering aggregated queries when dealing with imprecise databases. We assume that the existence of a knowledge base is permanent and independent of the imprecision in the database...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.