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Detecting abnormal climate events across temporal and spatial scales is crucial to the understanding of local and regional climate trends. Existing methods often depend on prior knowledge about the timing, location, or duration of such events, limiting their versatility. This study introduces ClimBurst, an approach to detect climate bursts (unusually high or low values of climate variables) without prior assumptions about their temporal duration. ClimBurst offers the ability to: (a) identify climate bursts of any duration within the time series of single locations, (b) link climate bursts across neighboring locations, and (c) analyze the spatio‐temporal propagation of these anomalies. Applying ClimBurst to sea surface temperature data from the Mediterranean Sea (1960–2021) shows some detected hot bursts and anomalies coincide in time with known severe marine heatwaves. ClimBurst also shows how detected hot (cold) bursts are spatio‐temporally connected and this connected bursts have increased (decreased) in duration, intensity, spatial extent and frequency historically.

This paper presents parallel solutions (developed based on two state-of-the-art algorithms iSAX and sketch) for evaluating k nearest neighbor queries on large databases of time series, compares them based on various measures of quality and time performance, and offers a tool that uses the characteristics of application data to determine which algorithm to choose for that application and how to set the parameters for that algorithm. Specifically, our experiments show that: (i) iSAX and its derivatives perform best in both time and quality when the time series can be characterized by a few low-frequency Fourier Coefficients, a regime where the iSAX pruning approach works well. (ii) iSAX performs significantly less well when high-frequency Fourier Coefficients have much of the energy of the time series. (iii) A random projection approach based on sketches by contrast is more or less independent of the frequency power spectrum. The experiments show the close relationship between pruning ratio and time for exact iSAX as well as between pruning ratio and the quality of approximate iSAX. Our toolkit analyzes typical time series of an application (i) to determine optimal segment sizes for iSAX and (ii) when to use Parallel Sketches instead of iSAX. Our algorithms have been implemented using Spark, evaluated over a cluster of nodes, and have been applied to both real and synthetic data. The results apply to any databases of numerical sequences, whether or not they relate to time.

Background Approximately half of the Iranian population are women, and they play a vital role in the home. The women’s attitude can play a critical role in the safety of homes. Best of our knowledge, there is not a valid and reliable instrument to measure their attitude toward home safety. So, the present study aimed to design a psychometrics tool to assess women’s attitudes toward home safety. Methods The researchers designed an instrument based on the home safety concept as the first instrument to measure housewives’ attitudes toward home safety. The developed instrument distributed among 686 women in Tabriz health centers. Content validity, confirmatory, and exploratory factor analysis were used to examine the construct validity, and Cronbach’s alpha and test-retest were employed to examine the reliability and reproducibility of the instrument. Results In the face validity section, the impact score of all items was determined to be above 1.5. In the content validity section, 4 items were excluded from the 39 questionnaire items due to low Content Validity Ratio (CVR). The mean CVR of all items was 0.842. By conducting exploratory factor analysis, it was found that the questionnaire has six dimensions. Three questions were removed from the study due to lack of connection with other items. Also, Cronbach’s alpha coefficient of the questionnaire is equal to 0.924, which indicates the appropriate reliability of the instrument . Conclusions This study aimed to develop a questionnaire to assess the safety attitudes of housewives toward home safety. It was found that the prepared tool has acceptable validity and reliability.

Matrix Profile (MP) has been proposed as a powerful technique for knowledge extraction from time series. Several algorithms have been proposed for computing it, e.g., STAMP and STOMP. Currently, MP is computed based on 1NN search in all subsequences of the time series. In this paper, we claim that a kNN MP can be more useful than the 1NN MP for knowledge extraction, and propose an efficient technique to compute such a MP. We also propose an algorithm for parallel execution of kNN MP by using multiple cores of an off-the-shelf computer. We evaluated the performance of our solution by using multiple real datasets. The results illustrate the superiority of kNN MP for knowledge discovery compared to 1NN MP.

The discovery of informative itemsets is a fundamental building block in data analytics and information retrieval. While the problem has been widely studied, only few solutions scale. This is particularly the case when (1) the data set is massive, calling for large-scale distribution, and/or (2) the length k of the informative itemset to be discovered is high. In this paper, we address the problem of parallel mining of maximally informative k -itemsets ( miki ) based on joint entropy. We propose PHIKS ( P arallel H ighly I nformative K ̲ -Item S et), a highly scalable, parallel miki mining algorithm. PHIKS renders the mining process of large-scale databases (up to terabytes of data) succinct and effective. Its mining process is made up of only two efficient parallel jobs. With PHIKS, we provide a set of significant optimizations for calculating the joint entropies of miki having different sizes, which drastically reduces the execution time, the communication cost and the energy consumption, in a distributed computational platform. PHIKS has been extensively evaluated using massive real-world data sets. Our experimental results confirm the effectiveness of our proposal by the significant scale-up obtained with high itemsets length and over very large databases.

Frequent itemset mining presents one of the fundamental building blocks in data mining. However, despite the crucial recent advances that have been made in data mining literature, few of both standard and improved solutions scale. This is particularly the case when (1) the quantity of data tends to be very large and/or (2) the minimum support is very low. In this paper, we address the problem of parallel frequent itemset mining (PFIM) in very large databases and study the impact and effectiveness of using specific data placement strategies in a massively distributed environment. By offering a clever data placement and an optimal organization of the extraction algorithms, we show that the arrangement of both the data and the different processes can make the global job either completely inoperative or very effective. In this setting, we propose two different highly scalable, PFIM algorithms, namely P2S (parallel-2-steps) and PATD (parallel absolute top-down). P2S algorithm allows discovering itemsets from large databases in two simple, yet efficient parallel jobs, while PATD renders the mining process of very large databases more simple and compact. Its mining process is made up of only one parallel job, which dramatically reduces the running time, the communication cost and the energy power consumption overhead in a distributed computational platform. Our different proposed approaches have been extensively evaluated on massive real-world data sets. The experimental results confirm the effectiveness and scalability of our proposals by the important scale-up obtained with very low minimum supports compared to other alternatives.

Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones).

Combining Grid and P2P technologies can be exploited to provide high-level data sharing in large-scale distributed environments. However, this combination must deal with two hard problems: the scale of the network and the dynamic behavior of the nodes. In this paper, we present our solution in APPA (Atlas Peer-to-Peer Architecture), a data management system with high-level services for building large-scale distributed applications. We focus on data availability and data discovery which are two main requirements for implementing large-scale Grids. We have validated APPA’s services through a combination of experimentation over Grid5000, which is a very large Grid experimental platform, and simulation using SimJava. The results show very good performance in terms of communication cost and response time.

Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current metrics are insufficient and inadequate for the context of event detection. There is a demand for metrics that incorporate both the concept of time and temporal tolerance for neighboring detections. This paper introduces SoftED metrics, a new set of metrics designed for soft evaluating event detection methods. They enable the evaluation of both detection accuracy and the degree to which their detections represent events. They improved event detection evaluation by associating events and their representative detections, incorporating temporal tolerance in over 36\\% of experiments compared to the usual classification metrics. SoftED metrics were validated by domain specialists that indicated their contribution to detection evaluation and method selection.