Event Processing Research Articles

Heart Failure Prediction Technique using Complex Event Processing

According to the WHO (World Health Organization) chronic diseases such as cancer, coronary heart disease, diabetes mellitus type 2, and chronic obstructive pulmonary diseases are among the world's most common diseases constitute because of this about 60% of all deaths occur in world. Here, we propose new health monitoring techniques to the prediction of heart failures. In this, we propose edge-computing based Complex Event Processing (CEP) techniques with the Remote Patient Monitoring (RPM) for the remote healthcare applications. This approach is based on the CEP it is combined with the statistical approach. For the extraction heart defects of patients C4.5 algorithm and, to the prediction of heart failure multilayer perceptron (MLP) model will be consider. First phase is to collects health parameters. Second phase is to process the collected data using an analysis rule. This proposed system continuously monitors heart patient and it predicts heart failures strokes based on the related symptoms. When a critical condition occurs then it alters patients and cardiologist.

Attention-based model and deep reinforcement learning for distribution of event processing tasks

Event processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actor-critic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.

Role of Business Intelligence and Knowledge Management in Solving Business Problems

The term “Business intelligence” is described as a plan or a strategy where the operations like reporting, data analysis, data mining, event processing are performed to improve the production and growth of a business enterprise or a business entity. And on the other hand, the “Knowledge management” is explained as well-organized management of resources and information within a commercial organization it can be a business too. Almost all business will have limitations and challenges which can be also known as the business problems. One of the main business problem is demand, the business plans must work according to the demand of the consumers. Analyzing the demand would provide the solutions for queries like what is the business trend? What is the need of the users? What should be the improvement make in the production? Where is the current position of the enterprise? And who all will be the competitors? For the predictive analysis a dataset of bitcoin is taken. The major aim of the study is to implement the strategies to overcome the business problems mainly the demand prediction. And the objective is to find out the relevant issues and the remedies by using knowledge management and business intelligence to the common business problems. The dataset has columns called lowest price, highest price, open price, close price, trading volume and market capital. The research methodology used is predictive analysis using PCA and K-means clustering algorithm. By this dataset predictive plots are developed as achieved results for easy analysis by using research methodology. PCA and K-means are the algorithm used for accurate prediction. The importance of study is to predict the future sale, as it is very essential for a business enterprise to find future demand so that the organization can improve production.

The ALICE Run 3 online/offline processing

The ALICE experiment has undergone a major upgrade for LHC Run 3 and will collect data at an interaction rate 50 times larger than before. The new computing scheme for Run 3 replaces the traditionally separate online and offline frameworks by a unified one, which is called O2. Processing will happen in two phases. During data taking, a synchronous processing phase performs data compression, calibration, and quality control on the online computing farm. The output is stored on an onsite disk buffer. When there is no beam in the LHC, the same computing farm is used for the asynchronous reprocessing of the data which yields the final reconstruction output. The O2 project consists of three main parts:. The Event Processing Nodes (EPN) equipped with GPUs deliver the bulk of the computing capacity and perform the majority of the reconstruction and the calibration. The First Level Processors (FLP) receive the data via optical links from the detectors and perform local processing where it is needed, which can optionally happen in the user logic of the FPGA based readout card. Between the FLP and the EPN farms the data is distributed in the network such that the EPNs receive complete collision data for the processing. The Physics and Data Processing (PDP) group develops the software framework and the reconstruction and calibration algorithms. The current O2 setup is capable of handling in real time the peak data rate foreseen for data taking of Pb–Pb collisions at 50 kHz interaction rate.

Key Technology of Internet of Things Middleware and Computer Event Matching Algorithm

In order to solve the problem of complex event pattern in big data and strengthen research on key technologies of the Internet of Things and computer time matching algorithms, this paper studies the problem based on Hadoop clustering algorithm. Firstly, based on the subtype attribute of event type, the maximum value is selected as the final attribute value of the event after weighting the event. Secondly, cluster analysis is conducted on the Internet of Things flow dataset through the relationship between complex events. Finally, the simulation test is carried out on the simulation dataset of complex event relationship, and the simulation test of clustering algorithm comparison is carried out. The experimental results show that the clustering accuracy of different datasets is above 85%, and the clustering accuracy of causality reaches 96.07% when the dataset is 5G. Therefore, the algorithm proposed has high feasibility, good stability, and high speed and effectiveness for complex event processing. This study has certain practical significance for solving the problem of complex event pattern in big data.

Prototype hardware design and testing of the global common module for the global trigger subsystem of the ATLAS phase-II upgrade

The High-Luminosity Large Hadron Collider (HL-LHC) will deliver more than ten times the integrated luminosity of the previous runs combined. Meeting its stricter throughput requirements poses new challenges to the Trigger and Data Acquisition (TDAQ) systems of the LHC experiments. Introduced in the framework of the ATLAS experiment’s HL upgrade, the Global Trigger (GT) is a new subsystem which will perform offline-like algorithms on full-granularity calorimeter data. The implementation of the GT’s functionality is firmware-focused and is composed of three layers: multiplexing (or data aggregating), global event processing, and demultiplexing interface to the central trigger processor. Each layer will be composed of several, similar nodes, hosted on replicas of identical hardware, the Global Common Module (GCM), an ATCA front board which is designed to be adopted throughout the entire GT subsystem. This article proceeds from the TWEPP 2021 conference and presents the GCM hardware design, performed in 2020, and focuses on some key results of its extensive testing performed in 2021.

PTSD is associated with impaired event processing and memory for everyday events

Current theories of posttraumatic stress disorder (PTSD) propose that memory abnormalities are central to the development and persistence of symptoms. While the most notable memory disturbances in PTSD involve memory for the trauma itself, individuals often have trouble remembering aspects of everyday life. Further, people with PTSD may have difficulty segmenting ongoing activity into discrete units, which is important for our perception and later memory of the activity. The current study investigated whether PTSD diagnosis and symptom severity predicted event segmentation and memory for everyday activities. To do so, 63 people with PTSD and 64 controls with a trauma history watched, segmented, and recalled videos of everyday activities. Viewers with higher PTSD symptom severity showed lower agreement on locations of event boundaries and recalled fewer fine-grained actions than did those with lower symptom severity. These results suggest that PTSD symptoms alter event segmentation, which may contribute to subsequent memory disturbances.

System Design for Online Foreign Language Education Based on Blockchain Technology.

This study aims to solve the problem that the traditional online foreign language teaching system focuses on function development, ignoring system security, and has certain risks. An online foreign language education system is designed and developed based on the blockchain technology. First, the blockchain technology and key technologies of system design are described in detail. Second, the overall technical architecture of the system, functional modules, and business logic of each module are designed. Finally, the basic performance of the system is tested. The results show that the system can realize the user's unrestricted office work and zero maintenance of the system. The separation of presentation logic and business logic facilitates the development and maintenance of the system. The system mainly includes six functional modules: user management, course management, course order, course study, course certificate, and credit authentication. These modules are guaranteed for daily teaching use. The event processing success rate of the six functional modules of the system is greater than 99%, and the processing success rate is relatively high. The central processing unit (CPU) usage and memory usage are both below 30%. The host throughput of the six major modules is greater than 100 times/s when processing services. The average response time on the terminal side is maintained below 0.5 s. The average response time of business-side processing is maintained below 0.4 s, which is in line with the standard. The event processing success rate of the constructed system is 10.75% higher than that of other systems, and the average response time, CPU usage, and memory usage are 53.38%, 51.49%, and 50% lower than other systems, respectively. Therefore, the proposed system has better performance. To sum up, the designed system has excellent throughput, event processing capability, response speed, and low CPU and memory occupancy when processing business and is suitable for promotion and use in foreign language online education in colleges and universities. The use of the proposed system can improve its overall teaching efficiency and quality. The purpose is to provide important technical support for the improvement of the security of the online foreign language teaching system.

A Common Tracking Software Project

The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory.

The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query language: finite automata model, matching tree model, directed acyclic graph model, and Petri net model. In addition, the worst-case response time of the event stream processing system is an important indicator of evaluating the performance of the system. Firstly, this paper proposed a core algorithm of the temporal and spatial event stream processing program based on STEP by Petri net model. Secondly, we proposed a novel method to estimate the worst-case response time of the event stream processing system, which is based on stochastic Petri net and queuing theory. Finally, through the simulation experiment based on queuing theory, this paper proves that the data stream processing system based on STEP has good dynamic performance in processing the spatiotemporal data stream in Internet of vehicles.

Real-time wildfire detection with semantic explanations

Wildfire detection is an indispensable component of many resilient platforms, preventing environmental disasters from damaging life. Online detection, which refers to situations in which wildfire events need to be detected in real time, is an essential tool, as the consequences of wildfires might already be irreversible by the time they are detected. Recently, wildfire detection methods based on machine learning and deep learning have been proposed. However, most of these methods take raw data as the input and then aggregate it into predictive features, which requires all of the historical data or a large part of a stream and violates the timeliness requirement of online detection. Moreover, these methods scarcely discuss the explainability (e.g., how the model comes up with the final detection). The lack of explainability reduces human trust in the system and may hinder further applications of the system, especially in high-stakes situations where decisions can have significant consequences. Unlike existing works that treat the timeliness and explainability problems separately, we propose a real-time wildfire detection system that is built upon the streaming capability of complex event processing and the expressiveness of semantic annotation. The system continuously processes raw data streams, transforms them into semantic events, and learns CEP queries that are both predictive and interpretable for humans at the same time. Experiments on four real datasets and one synthetic dataset show that our approach outperforms the baselines in terms of efficiency, accuracy, explainability, and adaptivity.

The effects of domain knowledge and event structure on event processing.

Research suggests that domain knowledge facilitates memory for domain-specific information through two mechanisms: differentiation, which involves the ability to identify meaningful, fine-grained details within a sequence, and unitization, which involves binding individual components from a sequence into functional wholes. This study investigated the extent to which individuals engaged in differentiation and unitization when parsing continuous events into discrete, meaningful units (i.e., event segmentation) and recalling them. Participants watched and segmented basketball videos. They then rewatched the videos and provided descriptions afterward. Videos were coded for the presence of higher order goals (A2 actions) and the individual sub-actions that comprised them (A1 actions). Results suggested that event segmentation behavior for participants with less knowledge was more aligned with changes in basic actions (A1 actions) than for participants with greater knowledge. When describing events, participants with greater knowledge were more likely than participants with less knowledge to use statements that reflected unitization.

Integrating process management and event processing in smart factories: A systems architecture and use cases

The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.

A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks

Emergency event monitoring is an important application of wireless sensor networks (WSNs). In the traditional cloud-assisted WSNs, the monitored data needs to be sent back to the cloud for processing. The round-trip of huge and burst data causes long delay and high energy consumption. With the increase of the computing capability of sensor nodes, local processing of events can be accomplished through cooperative computing between sensor nodes. However, if a node continues to undertake cooperative computing tasks, the energy consumption of nodes will be unbalanced and the network lifetime will be shortened. Sleep scheduling is an effective method to achieve energy balance, which can activate idle nodes alternately to participate in cooperative computing. In this paper, in order to make WSN more efficient to complete the local processing of events, we build a new system model based on node cooperative computing, and propose a multi-node Q learning-based cooperative computing node selection algorithm to obtain the sleep scheduling strategy. Simulation results show that compared with the classical algorithms, the proposed algorithm can complete more event processing, improve the reliability, and prolong the lifetime of WSNs.

Online correlation for unlabeled process events: A flexible CEP-based approach

Process mining is a sub-field of data mining that focuses on analyzing timestamped and partially ordered data. This type of data is commonly called event logs. Each event is required to have at least three attributes: case ID, task ID/name, and timestamp to apply process mining techniques. Thus, any missing information need to be supplied first. Traditionally, events collected from different sources are manually correlated. While this might be acceptable in an offline setting, this is infeasible in an online setting. Recently, several use cases have emerged that call for applying process mining in an online setting. In such scenarios, a stream of high-speed and high-volume events continuously flow, e.g. IoT applications, with stringent latency requirements to have insights about the ongoing process. Thus, event correlation must be automated and occur as the data is being received.We introduce an approach that correlates unlabeled events received on a stream. Given a set of start activities, our approach correlates unlabeled events to a case identifier. Our approach is probabilistic. That implies a single uncorrelated event can be assigned to zero or more case identifiers with different probabilities. Moreover, our approach is flexible. That is, the user can supply domain knowledge in the form of constraints that reduce the correlation space. This knowledge can be supplied while the application is running. We realize our approach using complex event processing (CEP) technologies. We implemented a prototype on top of Esper, a state of the art industrial CEP engine. We compare our approach to baseline approaches. The experimental evaluation shows that our approach outperforms the throughput and latency of the baseline approaches. It also shows that using real-life logs, the accuracy of our approach can compete with the baseline approaches.

Bat4CEP: a bat algorithm for mining of complex event processing rules

Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel bat-inspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.

In Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual‐Mode Memristor

Artificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO2/HfO2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems.

Managerial Accounting for Decision-Making and Planning

Managerial Accounting for Decision-Making and Planning

How Movie Events Engage Childrens’ Brains to Combine Visual Attention with Domain-Specific Processing Involving Number and Theory of Mind in a Cinematic Arena

Using functional magnetic resonance imaging, we tested the hypothesis that cinematic structure shapes variation in social-cognitive brain activity. Using our film, we completed an exploratory analysis of how activations in the temporal-parietal junction (TPJ), and the intraparietal sulcus (IPS) are shaped by variations in insert shots (e.g., shots showing objects that a character has looked at), and by character entrances and exits. We found that IPS and TPJ consistently responded to insert shots, and the correlation between TPJ and IPS responses significantly predicted the prevalence of belief inferences during the sequence. In addition, TPJ responded significantly to entrances and exits of characters. We also completed a qualitative analysis of moments during a sequence that induced relative peaks in TPJ and IPS responding. These analyses not only demonstrate that consistent brain responses can distinguish among meaningful variations in cinematic events but also that these analyses confirm and refine our understanding of the apparent specializations for visual attention and domain-specific event processing in parietal attention networks.

Multi-level Hierarchical Complex Behavior Monitoring System for Dog Psychological Separation Anxiety Symptoms.

An increasing number of people own dogs due to the emotional benefits they bring to their owners. However, many owners are forced to leave their dogs at home alone, increasing the risk of developing psychological disorders such as separation anxiety, typically accompanied by complex behavioral symptoms including excessive vocalization and destructive behavior. Hence, this work proposes a multi-level hierarchical early detection system for psychological Separation Anxiety (SA) symptoms detection that automatically monitors home-alone dogs starting from the most fundamental postures, followed by atomic behaviors, and then detecting separation anxiety-related complex behaviors. Stacked Long Short-Term Memory (LSTM) is utilized at the lowest level to recognize postures using time-series data from wearable sensors. Then, the recognized postures are input into a Complex Event Processing (CEP) engine that relies on knowledge rules employing fuzzy logic (Fuzzy-CEP) for atomic behaviors level and higher complex behaviors level identification. The proposed method is evaluated utilizing data collected from eight dogs recruited based on clinical inclusion criteria. The experimental results show that our system achieves approximately an F1-score of 0.86, proving its efficiency in separation anxiety symptomatic complex behavior monitoring of a home-alone dog.