The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency.

As a ubiquitous form of data in the natural world, time has been widely used in many domains such as the military, finance, medical treatment, and environment. Temporal data models that are used to model the dynamic development process of data are constantly proposed, such as temporal database and temporal XML. With the rapid development and popularization of the Web, the requirement of dealing with diverse data efficiently is becoming more important. As a new generation of data exchange language, JSON (JavaScript Object Notation) has been widely used because it is lightweight, has fast parsing, and has high transmission efficiency. In this paper, we propose a novel temporal JSON data model. Based on the proposed temporal JSON data model, we further propose two temporal JSON query languages, t-JSONPath, and t-JSONiq, which are the temporal extensions to JSONPath and JSONiq, respectively. With the proposed temporal JSON data model, we demonstrate our temporal JSON query languages with typical temporal query examples and evaluate their query response times.

Shapelets, also known as motifs, are time series sequences that have the property of discriminating between time series classes. Lately, shapelets studies have gained a lot of momentum due to their interpretable nature. As opposed to traditional time series classifiers, shapelet-based learners provide a visual representation of the pattern that triggers the classification decision. One of the most challenging issues of shapelet-based classifiers is the generation of a large number of shapelet outputs. To the best of our knowledge, this is the first effort that addresses the high numerosity problem of mined shapelets issue by mining the minimal set of discriminative shapelets for time series data. We propose a new shapelet mining learner, 1DCNN, that has the property of learning shapelets of different lengths using a black-box neural network model. 1DCNN optimizes the entire classification schema by learning the shapes of the representative patterns. Our proposed model uses network pruning to sparsify the network and keep only the most discriminative shapelets without compromising the classification accuracy. We validated our model using 59 real-world time series datasets from the UCR repository. Our experimental results show the effectiveness and efficiency of our approach in comparison with other competing baselines models. For fairness purposes, we did not compare 1DCNN with ensemble based approaches that encapsulates many learners. Our results show that the performance of our model is superior to all other baselines pertaining to the shapelet-based classifier category, with up to 95% less Floating Points Operations per Second (FLOPs) required by the network.

Space weather encapsulates the impact of variable solar activity on the vicinity of Earth and elsewhere in the solar system. A major agent of space weather, with significant effort already devoted to its prediction, is solar flares. Most existing analysis in this direction focus on the instantaneous (point-in-time) magnitude of various pre-flare parameters in flare host locations, solar active regions. Nonetheless, a recent trend places data-intensive studies, focusing on the pre-flare time series of these parameters, to the forefront. We take on this task in this study, focusing on the shape of pre-flare active region parameter time series by introducing a data-driven class profiling and clustering of these time series. We rely on data provided by the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark dataset. Our results indicate some potentially interesting temporal patterns that are unrelated to parameter magnitudes and may be used, both in tandem and independently from magnitudes, for future flare forecasting efforts. Our analysis also provides flexibility to define custom flare classes relying on pre-flare time series behavior and relate them to the existing, conventional NOAA / GOES flare classes.

Since its introduction to the computer science community, the Dynamic Time Warping (DTW) algorithm has demonstrated good performance with time series data. While this elastic measure is known for its effectiveness with time series sequence comparisons, the possibility of pathological warping paths weakens the algorithms potential considerably. Techniques centering on pruning off impossible mappings or lowering data dimensions such as windowing, slope weighting, step pattern, and approximation have been proposed over the years to reduce the possibility of pathological warping paths with Dynamic Time Warping. However, because the current DTW improvement techniques are mostly global methods, they are either limited in effect or limit the warping path excessively. We believe segmenting time series at significant feature points will alleviate some of the pathological warpings, and at the same time allowing us to obtain more intuitive warpings. Our heuristic approaches the problem from the human perspective of sequence comparison: by identifying global similarity before local similarities. We use easily identifiable peaks as the significant feature. The final distance is the DTW distance sum of all segments of time series. In this paper, we explore the impact of different peak identification parameters on Dynamic Time Warping and demonstrate how segmentation can help to avoid pathological warpings.

Combining a set of classification algorithms is a powerful technique in improving the accuracy of individual classifiers. There are two main paradigms in combining classifiers: classifier selection, where each classifier is considered as an expert in some local area of the feature space, and classifier fusion, where all classifiers are trained over the entire feature space and they are considered as competitive and complementary to each other. In this paper, we propose a new ensemble technique, NeuroEnsemble, that follows the classifier fusion paradigm applied on time series data. The Neuro-Ensemble exploits the idea that different classifiers participating in the ensemble have varying degrees of expertise on learning different class labels and it optimizes the ensemble using a shallow Multi-Layer Perceptron (MLP) based meta-learner to capture the expertise of individual classifiers. Every neuron in the MLP represents a classifier that contributes with a vote and performs activation and state computations. This work is the first attempt to train a neural network for learning the expertise of each classifier in an ensemble and optimize the entire classification schema based on class-level expertise weights. We validated our Neuro-Ensemble on 43 real-world time series datasets from the UCR repository. Our experimental results show the effectiveness and efficiency of our approach in comparison with individual baseline learners and ensemble techniques.

Solar flare prediction is an important task because of their potential impacts on both space and terrestrial infrastructure. This prediction task can be modeled as a binary classification between flaring and non-flaring Active Regions. Previous works on flare prediction focused on representing flaring and non-flaring Active Region examples in vector space, where the feature space was found from the Active Region magnetic field parameters. We extract time series samples of these Active Region parameters and present a flare prediction method based on the k-NN classification of the univariate time series. We find that, for our classification task, using a statistical summarization on the time series of a single Active Region parameter, called total unsigned current helicity, outperforms the use of all Active Region parameters at a single instant of time. Additionally, we present a data model of the flaring/non-flaring Active Regions using multivariate time series.

Space Weather is of rising importance in scientific discipline that describes the way in which the Sun and space impact a myriad of activities down on Earth as well as the safety of the space crew members on board of the space stations. Consequently, it is imperative to better quantify the risk of future space weather events. Most of the flare prediction models in literature use physical parameters of the potentially flaring active regions during a limited interval to gain insights on whether a flare will happen or not. This limits our perception of how an event evolves for an extended duration across multiple parameters. In this paper we followed a data-driven approach to address the problem of flare prediction from a multivariate time series analysis perspective and attempt to cluster potential flaring active regions by applying Distance Density clustering on individual parameters and further organize the clustering results into a multivariate time series decision tree. We compared different data extraction priors and spans, and ranked the importance for different parameters through univariate clustering. To the best of our knowledge, this is the first attempt to predict solar flares using a tree structure.

Scientists have observed the occurrence of two distinctive subsets in Interplanetary Coronal Mass Ejections (ICMEs): magnetic clouds (MCs) and non-magnetic clouds (non-MCs). While we are aware of some of the distinctive features of MCs and non-MCs, we cannot draw a precise line between them. Features such as large magnetic field, low plasma-beta, low proton temperature, etc. suggest when an ICME event is also an MC event, however, this categorization is far from an automated process. In addition to being time-consuming, the results differ depending on the precision of definition. In this paper, we approach the MC and non-MC class distinction from a data analysis perspective and show a data-driven taxonomy of ICME events. We use a time series dataset from the Ulysses spacecraft combined with a list of labeled MC and non-MC events. The time series data are hierarchically clustered with Euclidean distance and Dynamic Time Warping algorithm, and we compare our MC and non-MC clusters with the results from classifications generated by domain experts.