Event Processing Techniques Research Articles

Applying MAPE-K control loops for adaptive workflow management in smart factories

Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM). This is especially the case in cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops are widely used for self-management in these environments and describe four phases for approaching this challenge: Monitor, Analyze, Plan, and Execute. In this paper, we present an architectural solution as well as implementation proposals for using MAPE-K control loops for adaptive workflow management in smart factories. We use Complex Event Processing (CEP) techniques and the process execution states of a Workflow Management System (WfMS) in the monitoring phase. In addition, we apply automated planning techniques to resolve detected exceptional situations and to continue process execution. The experimental evaluation with a physical smart factory shows the potential of the developed approach that is able to detect failures by using IoT sensor data and to resolve them autonomously in near real time with considerable results.

Deployment Approaches in Distributed Complex Event Processing

Big Data technologies have traditionally focused on processing human-generated data, while neglecting the vast amounts of data generated by Machine-to-Machine (M2M) interactions and Internet-of-Things (IoT) platforms. These interactions generate real-time data streams that are highly structured, often in the form of a series of event occurrences. In this paper, we aim to provide a comprehensive overview of the main research issues in Complex Event Processing (CEP) techniques, with a special focus on optimizing the distribution of event handlers between working nodes. We introduce and compare different deployment strategies for CEP event handlers. These strategies define how the event handlers are distributed over different working nodes. In this paper we consider the distributed approach, because it ensures, that the event handlers are scalable, fault-tolerant, and can handle large volumes of data.

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.

IoDEP: Towards an IoT-Data Analysis and Event Processing Architecture for Business Process Incident Management

IoT is becoming a hot spot area of technological innovations and economic development promises for many in-dustries and services. This new paradigm shift affects all the enterprise architecture layers from infrastructure to business. Business Process Management (BPM) is a field among others that is affected by this new technology. To assist data and events ex-plosion resulting, among others, from IoT, data analytic processes combined with event processing techniques, examine large data sets to uncover hidden patterns, unknown correlations between collected events, either at a very technical level (incident/anomaly detection, predictive maintenance) or at business level (customer preferences, market trends, revenue opportunities) to provide improved operational efficiency, better customer service and competitive advantages over rival organizations. In order to capitalize the business value of data and events generated by IoT sensors, IoT, Data Analytics and BPM need to meet in the middle. In this paper, we propose an end-to-end IoT-BPM integration architecture (IoDEP: IoT-Data-Event-Process) for a proactive business process incident management. A case study is presented and the obtained results from our experimentations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions.

A microservice architecture for real-time IoT data processing: A reusable Web of things approach for smart ports

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture.

The recent growth of the Internet of Things’ services and applications has increased data processing and storage requirements. The Edge computing concept aims to leverage the processing capabilities of the IoT and other devices placed at the edge of the network. One embodiment of this paradigm is Fog computing, which provides an intermediate and often hierarchical processing tier between the data sources and the remote Cloud. Among the major benefits of this concept, the end-to-end latency can be decreased, thus favoring time-sensitive applications. Moreover, the data traffic at the network core and the Cloud computing workload can be reduced. Combining the Fog computing paradigm with Complex Event Processing (CEP) and data fusion techniques has excellent potential for generating valuable knowledge and aiding decision-making processes in the Internet of Things’ systems. In this context, we propose a multi-tier complex event processing approach (sensor node, Fog, and Cloud) that promotes fast decision making and is based on information with 98% accuracy. The experiments show a reduction of 77% in the average time of sending messages in the network. In addition, we achieved a reduction of 82% in data traffic.

Digital Avatars: Promoting Independent Living for Older Adults

Population ageing, together with the desire to maintain an autonomous lifestyle, poses today’s societies with a challenge that technological advances can help considerably to cope with. The widespread use of smartphones and their increasing computing power and storage capacity make them the ideal tool to achieve this goal. In this paper, we present Digital Avatars, a software framework adapted to the needs of older adults who wish to preserve their lifestyle, but who require assistance through technology. Building on previous work on the People as a Service model, Digital Avatars takes advantage of a smartphone’s capabilities and services to collect information about the people who own them. To do this, it applies Complex Event Processing techniques extended with uncertainty to infer the habits, preferences, and needs of the device owner to build with them an enhanced virtual profile of the user. These virtual profiles are the mechanism for monitoring the quality of life of older adults: analyzing their patterns of activity, reminding them of medication schedules, or detecting risky situations that generate alerts to relatives, caregivers, or the community health system.

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study.

A situation-driven framework for dynamic security management

We present a dynamic security management framework where security policies are specified according to situations. Situation-based policies easily express complex dynamic security measures, are closer to business, and simplify the policy life cycle management. Situations are specified using complex event processing techniques. The framework is supported by a modular event–based infrastructure where a dedicated situation manager maintains active situations allowing the command center to take dynamic situation–based authorization and obligation decisions. The whole framework has been implemented and showed good performance by simulation. Finally, we detail two real experiments.

Business process monitoring via decentralized complex event processing

ABSTRACTComplex event processing (CEP) techniques have been used for business process (BP) monitoring of large organizations with high number of complex BPs and high rates of BP instance generations resulting in high number of monitoring rules and high rates of events. To circumvent the scale limitation of centralized CEP techniques, we present a decentralized CEP mechanism called dBPM that uses both task parallelism by decomposition and distribution of monitoring rules, and data parallelism by partitioning and dispatching of events. We show that dBPM is more scalable than a general distributed CEP mechanism, called VISIRI, by dynamically adapting to workload changes.

A Review on Complex Event Processing in RFID System

The motivation of this paper is to review some of the leading research issues encountered by current Complex Event Processing (CEP) techniques. General CEP system undoubtedly laid a certain degree of uncertainties due to some unforeseen reasons such as inaccurate measurements through network failures or unpredicted interference from the system failures. A variety of traditional to the most current techniques was disclosed with some features, which are rarely considered in typical CEP problems. Besides, this paper also discusses a broad review of modern and future solutions, including techniques beyond mainstreams in complex event processing. Based on the critical analysis of prior techniques and solutions, therefore, we further the research and propose a solution for a new type of event processing engine especially in RFID for future reference.

Fast inference of deep neural networks in FPGAs for particle physics

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

Towards an IoT and Big Data Analytics Platform for the Definition of Diabetes Telecare Services

Diabetes is considered as one of chronic disease that requires patient self-monitoring processes and constant care. Patients need to perform frequently a control of glucose level in the blood in order to prevent hypoglycemia episodes. However, efficient detection of hypoglycemia could be done by checking other vital signs. The gaps in the manual monitoring and heavy control process prevent some patients to daily control and follow the progress of their disease; therefore, several metabolic problems could be noticed and need to be taken seriously into consideration. Therefore, tools for efficient disease control are required to allow self-monitoring of blood glucose and other vitals and measures (e.g., activities, dietary). This will allow filling the gap between patients and carers with the main objective to enhance comfort of patients and reduce the treatment costs. The work presented in this paper aims to develop a platform that combines IoT and complex event processing techniques for hypoglycemia detection. A prototype is introduced together with preliminary results to show its effectiveness for the definition of diabetes telecare services, mainly for hypoglycemia detection.

Event driven and semantic based approach for data processing on IoT gateway devices

Internet of things (IoT) applications rely on networks composed of a set of heterogeneous sensors and smart devices, which have the capability to constantly monitor the surroundings and gather data. This heterogeneity is reflected in raw data collected by such type of systems. Additionally, these data are continuously streaming; thus leading to huge volumes of heterogeneous data, which are further transferred to centralized platforms for processing. Consequently, two main challenges have arisen. First, the heterogeneity aspect of IoT data makes high-level IoT applications’ task of interpreting such data and detecting events in the real world more complex. Second, sending sensory data to a centralized platform leads to some issues, such as extensive consumption of IoT devices’ limited resources, network traffic overloading, and latency, which might negatively impact the response time especially in systems that were designed to handle critical situations. In this paper, we propose a decentralized approach for IoT data processing, by delegating this task to distributed edge devices (Gateways) taking into consideration their limited resources and network bandwidth. To accomplish this, we proposed a two-layer data processing approach that employs a hyped model encompassed of complex event processing (CEP) and semantic web (SW) techniques. While the first is proposed for performing aggregation and classification tasks, we use the latter for performing semantic filtering and annotation tasks. We have evaluated the feasibility of our approach to process sensory data in the context of Air Quality Monitoring scenario using an experimentation involving established ontologies. Several benchmarks are considered such as overall runtime, data size, and response time.

Provenance aware run‐time verification of things for self‐healing Internet of Things applications

SummaryWe propose a run‐time verification mechanism of things for self‐healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule‐based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.

고성능 데이터 발간/구독 미들웨어의 이벤트, 버퍼 처리 기술 및 성능 분석

DDS(Data Distribution Service)는 유연성, 확장성, 실시간 통신 환경을 지원하는 통신 미들웨어이다. 본 논문에서는 DDS 미들웨어의 성능을 향상시키기 위한 방법들을 제안한다. DDS 미들웨어 내부 동작과 관련된 세부 이벤트를 정의하고, 이벤트 구동형 구조에 적용하기 위해 하나의 DDS 메시지를 의미 있는 서브메시지 단위로 분해함으로써 처리 복잡도를 낮출 수 있다. 제안하는 히스토리캐시 관리 기법은 DDS의 특성 상 상태접근과 임의접근이 빈번하게 발생한다는 사실을 이용한다. 제안한 방법들을 본 연구팀이 개발한 EchoDDS에 적용하여 성능을 향상시켰다.

Issues in complex event processing: Status and prospects in the Big Data era

Research issues in complex event processing (CEP) emphasizing on query optimization.Cover deterministic probabilistic models, centralized distributed settings.Issues for CEP optimization over Big Data enabling cloud computing platforms.Predictive Analytics and CEP in cloud platforms even with dispersed resource pools. Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions and Internet-of-Things (IoT) platforms. Such interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, with an emphasis on query optimization aspects. Our study expands on both deterministic and probabilistic event models and spans from centralized to distributed network settings. In that, we cover a wide range of approaches in the CEP domain and review the current status of techniques that tackle efficient query processing. These techniques serve as a starting point for developing Big Data oriented CEP applications. Therefore, we further study the issues that arise upon trying to apply those techniques over Big Data enabling technologies, as is the case with cloud platforms. Furthermore, we expand on the synergies among Predictive Analytics and CEP with an emphasis on scalability and elasticity considerations in cloud platforms with potentially dispersed resource pools.

Foundations for Streaming Model Transformations by Complex Event Processing

Streaming model transformations represent a novel class of transformations to manipulate models whose elements are continuously produced or modified in high volume and with rapid rate of change. Executing streaming transformations requires efficient techniques to recognize activated transformation rules over a live model and a potentially infinite stream of events. In this paper, we propose foundations of streaming model transformations by innovatively integrating incremental model query, complex event processing (CEP) and reactive (event-driven) transformation techniques. Complex event processing allows to identify relevant patterns and sequences of events over an event stream. Our approach enables event streams to include model change events which are automatically and continuously populated by incremental model queries. Furthermore, a reactive rule engine carries out transformations on identified complex event patterns. We provide an integrated domain-specific language with precise semantics for capturing complex event patterns and streaming transformations together with an execution engine, all of which is now part of the Viatra reactive transformation framework. We demonstrate the feasibility of our approach with two case studies: one in an advanced model engineering workflow; and one in the context of on-the-fly gesture recognition.

Quantum efficiency measurements in the swept charge device CCD236

The e2v technologies plc. CCD236 is a Swept Charge Device(SCD) designed as a large area (20 mm × 20 mm) soft X-ray detectorfor spectroscopy in the range 0.8 keV to10 keV. It benefits from improvements in design over the previousgeneration, the e2v CCD54, such as: a 4 times increased detector area, areduction in split X-ray events due to the 100 μm × 100 μm`pixel' size, and improvements to radiation hardness. The CCD236 will beused in India's Chandrayaan-2 Large Soft X-ray Spectrometer (CLASS)instrument and China's Hard X-ray Modulation Telescope (HXMT).Measurements of the Quantum Efficiency (QE) have been obtained relative to aNIST calibrated photodiode over the energy range 0.2 keV to 1.9 keV, usingthe BESSY II X-ray synchrotron in Berlin. Two X-ray event countingmethods are described and compared, and QE for soft X-ray interaction isreported. Uniformity of QE across the device is also investigated atenergies between 0.52 keV and 1.5 keV in different areas of the detector.This work will enable the actual number of photons incident on the detectorsto be calculated, thus ensuring that the CCD236 detectors provide valuablescientific data during use. By comparing the QE methods in this paper withthe event processing techniques to be used with CLASS, an estimate of theinstrument-specific QE for CLASS can be provided.

Current trends of corporate performance reporting tools and methodology design of multifactor measurement of company overall performance

Enterprises normally have a different approach to performance reporting. Performance reporting, and unfortunately reporting globally, at small and middle enterprises are not widely supported or popular. It perceived as something what enterprises must do, not what they like to do. The reason could be because its time consuming for them or it gives not a good (or none) feedback to the enterprises. But there are also some of the enterprises, that have their performance reporting as normal part of the corporate culture and want to have reporting as part of their information system (ideally automated). The first step of successful implementation of the automated performance reporting into the information system is to formalize the business rule from natural language expression. Current trends of corporate performance reporting and performance evaluation, i.e., the measurement of environmental, social, economic and governance performance of a company, and corporate sustainable reporting, are discussed in the paper. The relationship between corporate performance and reporting is an important issue; and the design of modern and advanced methods to identify key performance indicators is discussed here along with the possibility of the utilization of XBRL taxonomy or/and Complex event processing (CEP) techniques and methods.