Event Matching Research Articles

Earthquake event knowledge graph construction and reasoning

Efficient decision-making in earthquake emergencies plays a crucial role in ensuring individual safety, protecting personal property, and maintaining societal stability. However, traditional approaches to earthquake emergency decision-making rely on manual analysis or rule-based methods, which often struggle to fully leverage the wealth of information and uncover hidden data connections. Consequently, the efficiency of earthquake emergency decision-making is compromised. To address this issue, this study proposes a method for constructing an earthquake event knowledge graph and utilizing it for decision-making in earthquake emergencies. Firstly, specialized earthquake event knowledge ontology is developed, tailored to the unique characteristics of earthquake event data. Secondly, structured instances of earthquake event knowledge are extracted from text using transfer learning techniques, enabling the construction of the earthquake event knowledge graph. Thirdly, the earthquake event knowledge is represented as multidimensional vectors using knowledge graph representation learning technology. This facilitates the identification of similar earthquake events through inference based on vector similarity computation. In conclusion, the results of a case-based study demonstrate the effectiveness of the proposed method in providing accurate outcomes, facilitating earthquake event matching, enabling the retrieval and reuse of historical earthquake event knowledge, and serving as a valuable reference for earthquake emergency decision-making.

OEM: An operation-aware event matching algorithm for content-based Pub/Sub systems

Due to the continuous expansion of the user base and the heightened demands for quality of service (QoS), real-time data distribution has encountered significant challenges in event matching within large-scale content-based publish/subscribe (CPS) systems. Many efficient algorithms have been proposed to enhance the matching performance. However, most existing algorithms scarcely consider operation characteristics, which often leads to performance degradation due to a multitude of repetitive operations, such as comparisons, additions, and assignments. In this paper, we propose an Operation-aware Event Matching algorithm called OEM, which takes into account the efficiency of CPU operations, SIMD instructions, and multi-thread architecture. First, we introduce a bitset-based subscription cache mechanism (SCM), which enables the efficient execution of bitwise OR operations during the matching process. Furthermore, we propose two optimizations, namely an optimal skewness-aware cell grouping (OSCG) strategy and an attribute-based clustering mechanism (ACM), to enhance the efficiency of OEM. These optimizations effectively address the challenges posed by data skewness, low cache efficiency, and high dimensionality. In addition, we establish a performance analysis model that characterizes the trade-off between memory consumption and performance improvement. We conducted extensive experiments to evaluate the performance of OEM. Compared to six state-of-the-art algorithms, namely REIN, Ada-REIN, TAMA, OpIndex, PEM and fgPEM, OEM achieves an average reduction in matching time by 40.4×, 38.5×, 12.0×, 33.1×, 27.8×, and 25.5×, respectively.

Semantic matching in GUI test reuse

Reusing test cases across apps that share similar functionalities reduces both the effort required to produce useful test cases and the time to offer reliable apps to the market. The main approaches to reuse test cases across apps combine different semantic matching and test generation algorithms to migrate test cases across Android apps. In this paper we define a general framework to evaluate the impact and effectiveness of different choices of semantic matching with Test Reuse approaches on migrating test cases across Android apps. We offer a thorough comparative evaluation of the many possible choices for the components of test migration processes. We propose an approach that combines the most effective choices for each component of the test migration process to obtain an effective approach. We report the results of an experimental evaluation on 8,099 GUI events from 337 test configurations. The results attest the prominent impact of semantic matching on test reuse. They indicate that sentence level perform better than word level embedding techniques. They surprisingly suggest a negligible impact of the corpus of documents used for building the word embedding model for the Semantic Matching Algorithm. They provide evidence that semantic matching of events of selected types perform better than semantic matching of events of all types. They show that the effectiveness of overall Test Reuse approach depends on the characteristics of the test suites and apps. The replication package that we make publicly available online (https://star.inf.usi.ch/#/software-data/11) allows researchers and practitioners to refine the results with additional experiments and evaluate other choices for test reuse components.

Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China

Abstract. Accurate flood event simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatiotemporal resolutions of precipitation on flood event simulation in a large-scale catchment of China. We use high-spatiotemporal-resolution Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcing for hydrologic simulation. Three hydrological models (HBV, SWAT and DHSVM) and a data-driven model (long short-term memory (LSTM) network) are utilized for flood event simulation. Two calibration strategies are carried out, one of which targets matching of the flood events, with peak discharge exceeding 8600 m3 s−1 between January 2015 and December 2017, and the other one is the conventional strategy for matching the entire streamflow time series. The results indicate that the event-based calibration strategy improves the performance of flood event simulation compared with a conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood event simulation at a finer temporal resolution, especially in flood peak simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood event simulation.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Initial experience of synchronic description of historical musicology texts

The author discusses a small initial experience of using synchronic description in musicology texts. In the beginning, the origin of the synchrony–diachrony dichotomy introduced into linguistics by Ferdinand de Saussure is revealed. These terms and concepts from linguistics turned out to be so generative in methodological terms that they were adapted into other scientific disciplines. Starting with Saussure, the specificity of the relations of the two approaches is most deeply expressed as the relation of the system of the subject – synchrony and its evolution – diachrony. Next, the article discusses collective intelligence (Lotman) as a result of the activity and cooperation of experts of the same specialty. The productivity and relevance of using a synchronic approach for an adequate comparison of musical works, especially in the history and theory of performing arts, is emphasized. The main objective of the article was to test the use of a synchronic approach for the formation of textual information about the formation of the classical style in musical Europe of the XVIII century. The main pathos and function of the synchronic approach is "comparison". When the beginnings of events are aligned, it becomes possible to compare them (events) with each other, and the results of the comparison are attributed to the synchronization time. The synchronic approach demonstrates the continuity and monomericity of historical time. In synchrony, we see that juxtaposition and comparison overcome the natural separateness of historical events. The conclusions of the article summarize the main properties of the synchronic and diachronic approaches: synchrony reflects the system structure of the object and phenomenon, and diachrony, their evolution. For more productive event matching, synchronization of several parameters and types is necessary. This type of historical research can be described as a multifactorial synchronic analysis of historical events. Further prospects for the development of synchronic and diachronic approaches lie in their synthesis, when multiple slices of synchrony are sorted by chronology, and diachrony consists of synchronic slices of time.

An efficient confidentiality protection solution for pub/sub system

Publish/subscribe(pub/sub) systems are widely used in large-scale messaging systems due to their asynchronous and decoupled nature. With the population of pub/sub cloud services, the privacy protection problem of pub/sub systems has started to emerge, and events and subscriptions are exposed when executing event matching on untrustworthy cloud brokers. However, as the number of subscriptions increases, the effectiveness of the previous confidentiality protection approaches declines drastically. In this paper, we propose SBM (scalable blind matching), an effective confidentiality protection scheme for pub/sub systems. To the best of our knowledge, SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its scalability. In this scheme, SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA, whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching performance. The experiment demonstrates that this method has better matching performance compared to others: the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000, and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

EEM: An elastic event matching framework for content-based publish/subscribe systems

The inevitable workload fluctuations in large-scale IoT applications make it difficult to ensure the QoS of data distribution services. Usually, this problem can be solved by dynamically adjusting resource supply. However, deploying more resources not only takes time, it is also difficult to achieve cost-effective results. In this paper, we propose an elastic event matching (EEM) framework and we explore the idea of endowing matching algorithms with adaptability in performance. The strategy is to enable seamless switching between exact matching and approximate matching, achieving a trade-off between matching precision and matching speed. First, we establish a predicate skipping adjustment (PSA) mechanism which quantifies the relationship between false positives and the number of skipped predicates. In addition, we design a performance adjustment decision (PAD) algorithm according to fluctuating workloads. We implemented an EEM prototype based an Kafka, which uses an existing matching algorithm enhanced by PSA as the engine. The prototype is evaluated through a series of experiments based on both synthetic data and real-world stock traces. Experiment results show that adjusting the performance of matching algorithm at the price of a small false positive rate of less than 0.1% can shorten event latency by up to 14.34 times, which clearly demonstrates the effectiveness of the EEM framework.

The Balanced Window-Based Load Event Optimal Matching for NILM

Load event matching is the key for event-based non-intrusive load monitoring (NILM). It aims to find the load event sequence corresponding to the appliance’s operation cycle from all detected load events. Firstly, a novel combinatorial optimization model for load event matching is established. To reduce the dimension of the estimation matrix in the proposed optimization model, the concept of balanced window (BW) is introduced to indicate a period of load operation within which events can be fully matched. Then an iterative load event optimal matching strategy based on nested BWs is proposed, where most inner BWs are processed firstly. For the event matching in one specific BW, a two-stage optimization algorithm is designed. In the first stage, a set of candidate load event sequences is generated by using the depth first search algorithm with pruning, which reduces the solution space of the combinatorial optimization problem. In the second stage, the optimal load event sequences that result in the optimal fit of the aggregated load power is selected. Comparative experiments on the publicly-accessible REDD datasets have shown that the proposed method achieves better performance against the existing unsupervised NILM methods, and is promising for practical implementation.

A slice-test demonstrator for the upgrade of the CMS drift tubes at High-Luminosity LHC

Drift Tubes (DT) detectors equip the CMS muon system barrel region serving both as offline tracking and triggering devices. Existing DT chambers will operate throughout High-Luminosity LHC, but, in order to withstand event rates and integrated doses far beyond the initial design specification, an upgrade of the current readout and trigger electronics is planned. In the upgraded system, time-to-digital converters will stream hits to new back-end boards that, beyond event matching, will perform online tracking of trigger segments exploiting the ultimate DT cell resolution. During the second LHC long shutdown, prototypes of the aforementioned electronics were installed in four DT chambers with the same azimuthal acceptance, instrumenting a demonstrator of the HL-LHC DT upgrade (DT slice-test). In this report, the motivation for such an upgrade will be highlighted, and the status of the DT slice-test operation, as well as its performance measured with cosmic muons, will be presented. Plans towards future developments of the demonstrator throughout the LHC Run-3 will also be discussed.

Concept Tree-Based Event Matching Algorithm in Publish/Subscribe Systems

Semantic-based publish/subscribe system has attracted a lot of attention in recent years due to its powerful description ability in message dissemination scenarios. As a key part of semantic-based publish/subscribe systems, event matching needs to understand the semantic meaning of subscriptions, especially the hierarchy of concepts. However, existing event matching algorithms are severely affected by the complexity of concept hierarchy trees, some even cannot run due to high memory occupation. This article proposes an event matching algorithm called CTPS (concept tree-based publish/subscribe system) to address it; specifically, we build subscription indexes on concept hierarchy trees for the first time, employ bit arrays to avoid unnecessary matches, and use faster bit operations to accelerate the matching speed. Experiments show that, compared with previous algorithms, CTPS has less memory occupation and shorter event matching time, and its performance is not sensitive to the height of the concept hierarchy tree.

Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness

Numerical Weather Prediction (NWP) is the key to precise wind power forecasting (WPF), which can be enhanced by the NWP correction and scenario partition techniques. However, on the one hand, existing NWP correction techniques may enlarge the volatility of ensemble NWP which disturbs the subsequent WPF. On the other hand, existing scenario partition techniques cannot precisely predict wind power in fluctuating scenarios by assuming NWP is totally reliable. Therefore, this paper proposes a novel NWP enhanced WPF method based on rank ensemble and probabilistic fluctuation awareness. Firstly, Rank Bayesian Ensemble (RBE) method is intended based on the stationary NWP rank, which generates a stable and accurate ensemble NWP. Secondly, a fluctuation scenarios partition framework is devised to establish a fluctuation awareness model with NWP’s credibility quantified. The framework works in a three-step manner, including characterization, matching, and inference of wind fluctuation events: respectively as Fluctuation identification and feature embedding (FIGE), Fluctuating mapping algorithm (FMA), and Probabilistic fluctuation warning (PFW). Finally, we incorporate the two enhancement techniques in a forecasting method in the ultra-short-term. A real-world wind farm with four NWP sources data validates the superiority and robustness of the proposed WPF method. The result shows that our method can reduce the four hour-ahead rooted mean square error (RMSE) by 2.16%–4.36% compared to baseline models. Meanwhile, the stability of ensemble NWP and the effectiveness of fluctuation scenario partition are also discussed.

Evaluation of Medicare Claims for the Development of Heart Failure Diagnostics

Although claims data provide a large and efficient source of clinical events, validation is needed prior to use in heart failure (HF) diagnostic development. Data from the Multisensor Chronic Evaluations in Ambulatory Heart Failure Patients (MultiSENSE) study, used to create the HeartLogic HF diagnostic, were linked with fee-for-service (FFS) Medicare claims. Events were matched by patient ID and date, and agreement was calculated between claims primary HF diagnosis codes and study event adjudication. HF events (HFEs) were defined as inpatient visits, or outpatient visits with intravenous decongestive therapy. Diagnostic performance was measured as HFE-detection sensitivity and false-positive rate (FPR). Linkage of 791 MultiSENSE subjects returned 320 FFS patients with an average follow-up duration of 0.94 years. Although study and claims deaths matched exactly (n = 14), matching was imperfect between study hospitalizations and acute inpatient claims events. Of 239 total events, 165 study hospitalizations (69%) matched inpatient claims events, 28 hospitalizations matched outpatient claims events (12%), 14 hospitalizations were study-unique (6%), and 32 inpatient events were claims-unique (13%). Inpatient HF classification had substantial agreement with study adjudication (κ = 0.823). Diagnostic performance was not different between claims and study events (sensitivity = 75.6% vs 77.6% and FPR = 1.539 vs 1.528 alerts/patient-year). HeartLogic-detected events contributed to > 90% of the HFE costs used for evaluation. Acceptable event matching, good agreement of claims diagnostic codes with adjudication, and equivalent diagnostic performance support the validity of using claims for HF diagnostic development.

Using Machine Learning to Detect Events on the Basis of Bengali and Banglish Facebook Posts

In modern times, ensuring social security has become the prime concern for security administrators. The widespread and recurrent use of social media sites is creating a huge risk for the lives of the general people, as these sites are frequently becoming potential sources of the organization of various types of immoral events. For protecting society from these dangers, a prior detection system which can effectively detect events by analyzing these social media data is essential. However, automating the process of event detection has been difficult, as existing processes must account for diverse writing styles, languages, dialects, post lengths, and et cetera. To overcome these difficulties, we developed an effective model for detecting events, which, for our purposes, were classified as either protesting, celebrating, religious, or neutral, using Bengali and Banglish Facebook posts. At first, the collected posts’ text were processed for language detection, and then, detected posts were pre-processed using stopwords removal and tokenization. Features were then extracted from these pre-processed texts using three sub-processes: filtering, phrase matching of specific events, and sentiment analysis. The collected features were ultimately used to train our Bernoulli Naive Bayes classification model, which was capable of detecting events with 90.41% accuracy (for Bengali-language posts) and 70% (for the Banglish-form posts). For evaluating the effectiveness of our proposed model more precisely, we compared it with two other classifiers: Support Vector Machine and Decision Tree.

Linking event archives to news: a computational method for analyzing the gatekeeping process

ABSTRACT Digital archives that document real-world events provide new opportunities for large-scale analyses of how news coverage represents reality. We present a method and open-source tool for linking event data to news articles, and demonstrate its application with an analysis of event and country level predictors of terrorism coverage in The Guardian from 2006 to 2018, using event data from the Global Terrorism Database (GTD). Our method builds on established techniques for calculating document similarity, and we propose a novel strategy for fine-tuning parameters of the event matching algorithm that requires no manual coding. An online appendix is provided that documents all code to replicate our analysis and reuse our tools.

VID-WIN: Fast Video Event Matching With Query-Aware Windowing at the Edge for the Internet of Multimedia Things

Efficient video processing is a critical component in many IoMT applications to detect events of interest. Presently, many window optimization techniques have been proposed in event processing with an underlying assumption that the incoming stream has a structured data model. Videos are highly complex due to the lack of any underlying structured data model. Video stream sources such as CCTV cameras and smartphones are resource-constrained edge nodes. At the same time, video content extraction is expensive and requires computationally intensive Deep Neural Network (DNN) models that are primarily deployed at high-end (or cloud) nodes. This paper presents VID-WIN, an adaptive 2-stage allied windowing approach to accelerate video event analytics in an edge-cloud paradigm. VID-WIN runs parallelly across edge and cloud nodes and performs the query and resource-aware optimization for state-based complex event matching. VID-WIN exploits the video content and DNN input knobs to accelerate the video inference process across nodes. The paper proposes a novel content-driven micro-batch resizing, queryaware caching and micro-batch based utility filtering strategy of video frames under resource-constrained edge nodes to improve the overall system throughput, latency, and network usage. Extensive evaluations are performed over five real-world datasets. The experimental results show that VID-WIN video event matching achieves ~2.3X higher throughput with minimal latency and ~99% bandwidth reduction compared to other baselines while maintaining query-level accuracy and resource bounds.

Probing the Southern Cascadia Plate Interface With the Dense Amphibious Cascadia Initiative Seismic Array

AbstractFault coupling is vital in determining the amount of strain that is accumulated along faults. The magnitude and location of stored elastic strain energy in highly coupled regions has important implications for understanding the full range of slip behavior at plate boundary faults, as well as earthquake and tsunami hazards. We use the temporary dense amphibious array of seismometers offered by the Cascadia Initiative to create a high‐resolution catalog of events to examine the spatio‐temporal behavior of earthquakes near the plate interface. The data show that in southern Cascadia the plate interface updip of the geodetically locked region is nearly devoid of seismicity, therefore likely highly coupled and accumulating strain. The catalog reveals events that are clustered at the downdip edge of the highly coupled megathrust that correlate in time with nearby strain transient observations. Template matching of events in the cluster using permanent stations of the Northern California Seismic Network over a 10‐year period between 2010 and 2020 indicates that this cluster is unique in space and time. Its activity only during the strain transient provides support for the utility of seismic observations in the identification of strain transients.

Query-driven video event processing for the internet of multimedia things

Advances in Deep Neural Network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to perform near-real-time video event detection in a distributed setting. GNOSIS follows a serverless approach where its component acts as independent microservices and can be deployed at multiple nodes. GNOSIS uses a declarative query-driven approach where users can write customize queries for spatiotemporal video event reasoning. The system converts the incoming video streams into a continuous evolving graph stream using machine learning (ML) and DNN models pipeline and applies graph matching for video event pattern detection. GNOSIS can perform both stateful and stateless video event matching. To improve Quality of Service (QoS), recent work in GNOSIS incorporates optimization techniques like adaptive scheduling, energy efficiency, and content-driven windows. This paper demonstrates the Occupational Health and Safety query use cases to show the GNOSIS efficacy.

A Sports Training Video Classification Model Based on Deep Learning

A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.

A multiple-pattern complex event matching model based on merge sharing for massive event streams

Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event matching models. Aiming to solve the problem, a multiple-pattern complex event matching model based on merge sharing is proposed in this paper. The achievement of the paper lies in the fact that a multiple-pattern complex event matching model based on merge sharing is presented to successfully realize the quick matching of related primitive events for multiple complex events from the massive event streams. Specifically, in our scheme, we successfully use merge sharing technology to merge all the same prefixes, suffixes or subpatterns existing in single-pattern matching models into shared ones and to construct a multiple-pattern complex event matching model. As a result, our proposed matching model in this paper can effectively solve the above problem. The experimental results show that our proposed matching model in this paper outperforms the existing single-pattern matching models in model construction and related events matching for massive event streams.