Complex Event Processing Applications Research Articles

Evaluating a Flow-Based Programming Approach as an Alternative for Developing CEP Applications in IoT

One of the main advantages brought by the Internet of Things (IoT) is the possibility of having large amounts of data from several sources that allow us, once analyzed, to make decisions in various domains in real time. This implies the need to be able to process large volumes of data in more or less limited processing times depending on the application domain. In this sense, complex event processing (CEP), used in conjunction with an enterprise service bus (ESB), has proven to be very efficient in multiple domains. In search for greater efficiency, some CEP engines offer the option of using flow-based programming (FBP) rather than their traditional programming using CEP together with an event bus. However, its use, while it may be more efficient, can lead to other limitations. In this article, we analyze and describe the performance and limitations of using a CEP engine with an ESB versus a CEP engine with FBP. This will allow developers to decide which option is more convenient for their IoT system depending on the application domain and its specific needs.

Complex Event Processing Methods for Greenhouse Control

In the greenhouse environment, the application of complex event processing (CEP) technology can effectively tackle the problem of recognition of the complex patterns that appeared in greenhouse conditions. In the existing research, few scholars have proposed a scheme to integrate complicated scenes within the greenhouse environment with high efficiency, convenience, and low coupling. Therefore, in order to solve the problem of hard recognition and fusion of complex patterns in the greenhouse environment, based on the characteristics of the greenhouse, this paper proposes a complex event processing method for greenhouse control. Our method has high applicability and high expansibility, including 13 types of event processing agents and 21 types of typical events involved in greenhouse automatic control. This method has the advantages of low information coupling and multi-domain integration, which can be directly used by agricultural experts and related workers and is of great significance to promote the extensive application of CEP technology in the greenhouse field. Our experiment successfully realized a greenhouse intelligent control system based on CEP technology is successfully realized in our experiment. The experimental statistics shows that the structure of the control system was accessible and effective.

CEPchain: A graphical model-driven solution for integrating complex event processing and blockchain

Blockchain provides an immutable distributed ledger for storing transactions. One of the challenges of blockchain is the particular processing of dynamic queries due to accumulating costs. Complex Event Processing (CEP) provides efficient and effective support for this in a way, however, that is difficult to integrate with blockchain. This paper addresses the research challenges of integrating blockchain with CEP. More specifically, we envision an effective development environment in which (i) event-driven smart contracts are modeled in a graphical way, which are, in turn, (ii) automatically transformed into complementary code that is deployed in both a CEP engine and a blockchain network, and then (iii) executed on off-chain CEP applications which, connected to different data sources and sinks, automatically invoke smart contracts when event pattern conditions are met. We follow a classic systems engineering approach for defining the concepts of our system, called CEPchain, which addresses the described requirements. CEPchain was evaluated using a real-world case study for vaccine delivery, which requires an unbroken cold chain. The results demonstrate that our approach can be applied without requiring experts on event processing and smart contract languages. Our contribution simplifies the design of integrated CEP and blockchain functionality by hiding implementation details and supporting efficient deployment.

Join query optimization techniques for complex event processing applications

Complex event processing (CEP) is a prominent technology used in many modern applications for monitoring and tracking events of interest in massive data streams. CEP engines inspect real-time information flows and attempt to detect combinations of occurrences matching predefined patterns. This is done by combining basic data items, also called "primitive events", according to a pattern detection plan, in a manner similar to the execution of multi-join queries in traditional data management systems. Despite this similarity, little work has been done on utilizing existing join optimization methods to improve the performance of CEP-based systems. In this paper, we provide the first theoretical and experimental study of the relationship between these two research areas. We formally prove that the CEP Plan Generation problem is equivalent to the Join Query Plan Generation problem for a restricted class of patterns and can be reduced to it for a considerably wider range of classes. This result implies the NP-completeness of the CEP Plan Generation problem. We further show how join query optimization techniques developed over the last decades can be adapted and utilized to provide practically efficient solutions for complex event detection. Our experiments demonstrate the superiority of these techniques over existing strategies for CEP optimization in terms of throughput, latency, and memory consumption.

Formalizing Complex Event Processing Systems in Maude

Complex event processing (CEP) is a cutting-edge technology for analyzing and correlating streams of information about events that happen in a system, and deriving conclusions from them. CEP permits defining complex events based on the events produced by the incoming sources, to identify complex meaningful circumstances and to respond to them as quickly as possible. Such event types and patterns are defined using event processing languages. However, as the complexity of CEP programs grows, they become difficult to understand and to prove correct. This paper proposes a formal framework for the specification of CEP applications, using rewriting logic and Maude, to allow developers to formally analyze and prove properties of their CEP programs. Several case studies are presented to illustrate the approach, as well as a discussion on the benefits of using Maude and its toolkit for modeling and analyzing CEP systems.

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.

Design and Implementation of SMS Based Anomalous Event Mitigation Process for Complex Event Processing Application

<p class="TTPAbstract">This paper describes the design and implementation of SMS based event mitigation for Complex Event Processing (CEP) application. The CAISER<sup>TM</sup>'s CEP platform were used to develop event processing systems which detects and identifies complex events based on patterns of previous and current lower order events. CAISER<sup>TM</sup> then generates mitigation action for anomalous events and executes them via 3 types of SMS based notification. An implementation of the SMS based event mitigation in a CEP based Server Farm Monitoring system is also described in this paper. The performance of the event mitigation process using SMS is evaluated and described in this paper.</p>

Design and Implementation of SMS Based Anomalous Event Mitigation Process for Complex Event Processing Application

<p class="TTPAbstract">This paper describes the design and implementation of SMS based event mitigation for Complex Event Processing (CEP) application. The CAISER<sup>TM</sup>'s CEP platform were used to develop event processing systems which detects and identifies complex events based on patterns of previous and current lower order events. CAISER<sup>TM</sup> then generates mitigation action for anomalous events and executes them via 3 types of SMS based notification. An implementation of the SMS based event mitigation in a CEP based Server Farm Monitoring system is also described in this paper. The performance of the event mitigation process using SMS is evaluated and described in this paper.</p>

Application of Complex Event Processing in the Express Business Automation

Complex Event Processing (CEP) is a promising technology that deals with event stream in a cenralized manner. But in some application scenario such as the express delivery business, business-related data is scattered in the network and must be obtained by request. For the purpose of resolving the difficulty to utilize and process such distributed infomation, the paper comprehensively utilizes various advanced technologies to design an automation Internet of Things (IoT) framework which can provide the rich-semantic complex events to the senior application. Firstly, this paper proposes the framework which can realize the automatic recognition of the parcel and positive request for the associated semantic data. Secondly, a hierarchical modularized ontology is established for the sake of sharing the background knowledge. Finally, we have implemented a prototype system with the proposed object and semantic event model and detection method. Test results from simulation data indicate that the system can operation stably and response quickly to express delivery business.

High-performance complex event processing over hierarchical data

While Complex Event Processing (CEP) constitutes a considerable portion of the so-called Big Data analytics, current CEP systems can only process data having a simple structure, and are otherwise limited in their ability to efficiently support complex continuous queries on structured or semistructured information. However, XML-like streams represent a very popular form of data exchange, comprising large portions of social network and RSS feeds, financial feeds, configuration files, and similar applications requiring advanced CEP queries. In this article, we present the XSeq language and system that support CEP on XML streams, via an extension of XPath that is both powerful and amenable to an efficient implementation. Specifically, the XSeq language extends XPath with natural operators to express sequential and Kleene-* patterns over XML streams, while remaining highly amenable to efficient execution. In fact, XSeq is designed to take full advantage of the recently proposed Visibly Pushdown Automata (VPA), where higher expressive power can be achieved without compromising the computationally attractive properties of finite state automata. Besides the efficiency and expressivity benefits, the choice of VPA as the underlying model also enables XSeq to go beyond XML streams and be easily applicable to any data with both sequential and hierarchical structures, including JSON messages, RNA sequences, and software traces. Therefore, we illustrate the XSeq's power for CEP applications through examples from different domains and provide formal results on its expressiveness and complexity. Finally, we present several optimization techniques for XSeq queries. Our extensive experiments indicate that XSeq brings outstanding performance to CEP applications: two orders of magnitude improvement is obtained over the same queries executed in general-purpose XML engines.

RFID-enabled complex event processing application framework for manufacturing

In order to face up with classic manufacturing challenges such as high work in progress (WIP) inventories, complexity in production planning and scheduling, and low labour and machine utilisation, many manufacturing companies made their efforts in implementing RFID (Radio Frequency Identification Devices) throughout the manufacturing workshops. Through this way, all production data in manufacturing fields can be obtained in real time, and it improves the flexibility and responsivity to the changing market for the companies. However, at the same time the RFID deployment also introduces a new challenge which requires an effective and efficient method to handle the large amounts of events. This paper proposes an application framework for a real-time Complex Event Management System (CEMS) based on RFID equipments deployment. With the use of Complex Event Processing (CEP) technologies, this system allows users to obtain interested and meaningful information from large numbers of primitive events captured from the RFID devices deployed in manufacturing shop-floor in real time. This paper presents the RFID deployment infrastructure first, and then system design of the CEMS is proposed.

Architecture, implementation and application of complex event processing in enterprise information systems based on RFID

Enterprises have to be increasingly agile and responsive to address the challenges posed by the fast moving market. With the software architecture evolving into service-oriented architecture, and the adoption of Radio Frequency Identification (RFID), event processing can fit well in enterprise information systems in terms of facilitation of event aggregation into high level actionable information, and event response to improve the responsiveness. To make it more applicable, the architecture of event processing in enterprise information systems is proposed; event meta model is put forth, and the rules are defined. To improve the detect efficiency, classification and partition of event instance is utilized. We have implemented the event processing mechanism in enterprise information systems based on RFID, including the data structures, optimization strategies and algorithm that is considered as one of the contributions. The performance evaluations show that the method is effective in terms of scalability and the capability of event processing. Complex event processing can improve operation efficiency and discover more actionable information, which is justified by the application.

Generating real-time complex event-processing applications

We propose to exploit the technology for complex event processing (CEP) embodied in the rule-based engine known as IBM Active Middleware Technology™ and extend it to the development of real-time CEP applications. Specifically, we propose to develop a framework that includes an integrated development environment (IDE) for defining rules, and, given a set of rules, generates code for a CEP application and enables us to determine time bounds on the response of this application to a set of supported events. In particular, the IDE helps determine a time bound for the execution time of the code corresponding to each rule. The calculation of time bounds is based on a set of benchmark measurements to be performed on the target hardware and involves code segments corresponding to basic operations. Although we assume the code generation phase produces Java™ code, the same approach can be applied to any other suitable programming language. In support of a feasibility argument for the proposed approach, we present some preliminary experimental results obtained on a partially implemented tool.