Distributed complex event processing with query rewriting

This study presents a novel method to develop kleene operators on a Binary Tree-Based Directed Graph (BTDG) to improve expressibility and performance of our developed complex event processing system. Complex Event Processing (CEP) systems are widely employed to notify opportunities and threats, which many of these situations are discovered through detection of multiple occurrences of the similar set of events. Many of CEP systems proposed kleene operators to detect these situations. The high expressibility of our implemented CEP system achieved by developing algorithms on kleene operator. These algorithms are designed on a BTDG as a unified basis to arrange input data and joining this input according to a given query. The performance is improved through directing events instantly to join with other events, dynamically switching to an optimal plan and late applying combination for kleene with specified size. The experiments shows kleene operator on BTDG brings rather high degree of expressiblity and performance in compare to its counterparts.

Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.

Goal of Complex Event Processing (CEP) is to identify meaningful events (such as opportunities or threats) and respond to them as quickly as possible. Traditional CEP frameworks do not have built-in security features. Any intruder may enter into the system and may send incorrect data to CEP engine. It causes to miss some patterns or it may lead to wrong prediction of patterns. Traditional CEP systems can be exploited through one or the other way. CEP system malfunctions when it is exploited. There is no built in procedure to check the health status of traditional CEP systems. Traditional CEP systems do not have procedures to take appropriate actions when system is affected or going to affect. When CEP system identifies some patterns, it will alert corresponding personnel about the occurrence of the event. Sometimes alert message may be undelivered or unnoticed by the corresponding people. Traditional CEP systems will not provide delivery assurance of alert message. These are few security challenges to existing CEP systems. In this paper Secure Complex Event Processing Framework is proposed to address above security challenges. Proposed framework is generic and it is pluggable into any existing CEP systems.

SUMMARYRecently, event processing (EP) has gained considerable attention as an individual discipline in computer science. From a software engineering perspective, EP systems still lack the maturity of well‐established software architectures. For the development of industrial EP systems, generally accepted software architectures based on proven design patterns and principles are still missing. In this article, we introduce a catalog of design patterns that supports the development of event‐driven architectures (EDAs) and complex EP systems. The design principles originate from experiences reported in publications as well as from our own experiences in building EP systems with industrial and academic partners. We present several patterns on different layers of abstractions that define the overall structure as well as the building blocks for EP systems. Architectural patterns that determine the top‐level structure of an EDA can be distinguished from design patterns that specify the basic mechanisms of EP. The practical application of the catalog of patterns is described by the pattern‐based design of a sample EDA for a sensor‐based energy control system. Finally, we propose a coherent and general reference architecture for EP derived from the proposed patterns.Copyright © 2013 John Wiley & Sons, Ltd.

In such areas as transport, healthcare, business process management, financial services, complex event processing (CEP) systems are becoming in demand. Large volumes of streams of events are generated and collected by these systems. Real-time processing of event stream data is essential. Based on current and past events, a decision is made, and forecasting future events is an alternative goal. For the fastest reaction to non-standard behavior, it is necessary to process data streams in real time, it means, as quickly as possible. That's why it is important to find new technologies which can speed up data processing. In this article expensive queries in CEP is identified, effective optimizations to improve their performance significantly is proposed. It is developed and implemented algorithm, which should decrease processing time. The results are compared with state-of-the-art CEP system.

Evaluation of Distributed Complex Event Processing (CEP) systems is a rather challenging task. To simplify this task, we developed the open simulation framework for Distributed CEP, called DCEP-Sim. The goal of this tutorial is to facilitate the process of using DCEP-Sim. Since DCEP-Sim is designed and implemented in the popular network simulator ns-3 we introduce the most important concepts of ns-3. Simulations in ns-3 are configured and executed though a main program called an ns-3 script. We use a simple example script to explain how simulations with DCEP-Sim are set up and executed. To give an idea how DCEP-Sim can be adjusted to particular needs, we explain how DCEP-Sim can be adapted (e.g., through changing the workload and the network topology) and how new Distributed CEP solutions can be added by explaining how to add a new operator to DCEP-Sim.

Complex event processing has become an important technology for big data and intelligent computing because it facilitates the creation of actionable, situational knowledge from potentially large amount events in soft realtime. Complex event processing can be instrumental for many mission-critical applications, such as business intelligence, algorithmic stock trading, and intrusion detection. Hence, the servers that carry out complex event processing must be made trustworthy. In this paper, we present a threat analysis on complex event processing systems and describe a set of mechanisms that can be used to control various threats. By exploiting the application semantics for typical event processing operations, we are able to design lightweight mechanisms that incur minimum runtime overhead appropriate for soft realtime computing.

The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, self-management and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management ( AGeCEP ) formalism. AGeCEP represents queries in a language- and technology-agnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this research has used it to design an autonomic manager and to define a selected set of self-management policies. Finally, experiments demonstrate that AGeCEP can indeed be used to develop algorithms that can be integrated into diverse CEP systems.

We present in this paper logical tools for verification of Complex Event Processing (CEP) system at design time, and maintenance at runtime. Although the framework is general enough for most applications, we focus on its use in Business Process Management (BPM).

With the increasing adoption of an event-based perspective in many organizations, the demands for automatic processing of events are becoming more sophisticated. Although complex event processing systems can process events in near real-time, these systems rely heavily upon human domain experts. This becomes an issue in application areas that are rich in specialized domain knowledge and background information, such as clinical environments. We utilize a framework of four techniques to enhance complex event processing with domain knowledge from ontologies to address this issue. We realize this framework in our novel approach of ontology-supported complex event processing, which stands in contrast to related approaches and emphasizes the strengths of current advances in the individual fields of complex event processing and ontologies. Experimental results from the implementation of our approach based on a state-of-the-art system show its feasibility and indicate the direction for future research.

Complex event processing is a key technology for analyzing event patterns in real-time data streams. It has been widely applied in IoT monitoring, financial risk control, smart manufacturing and other time-sensitive fields. Query patterns serve as the core matching mechanism in complex event processing systems. These patterns are primarily defined through selection policies, predicate constraints and timestamps to establish complex event detection rules. The selection policy is particularly important as it determines the sequential relationships between events. This directly affects the system's event matching efficiency and accuracy. However, in practical applications, due to different business requirements for event sampling frequencies, event streams often contain numerous consecutive events of the same type representing identical states. While all these events participate in complex event matching, traditional processing methods generate large numbers of semantically redundant partial matches. Analysis shows that only a very small proportion of these intermediate results eventually form complete matches, creating serious computational redundancy issues. This redundancy not only significantly increases system resource consumption but also reduces overall processing efficiency due to ineffective computations. To address this critical problem, this paper innovatively proposes a cache-based optimized selection policy. The core concept of this policy is to effectively reduce redundant matching results by extracting representative events from continuous event sequences. Experimental results show that this policy can significantly decrease the redundant matching rate while alleviating system load, providing new insights for optimizing complex event processing systems.

Owing to Big Data popularity, the metrics Variety, Volume and Velocity (V3), are gaining importance in large scale data intensive applications. Complex Event Processing (CEP) is an efficient solution for identifying events of interest on data streams arriving from geographically distributed heterogeneous sources in near-real time. CEP is capable of handling large variety of data with high computational velocity and delivers better solution compared to other existing techniques. However, handling large volume data streams still poses challenge in CEP systems. Hence, a middleware is required to manage huge streams of data on scalable distributed environment. This paper proposes Scalable Complex Event Processing (SCALACEP) framework for managing voluminous data streams. This paper addresses challenges such as state management and efficient rule distribution for design of distributed framework for CEP using the proposed SCALACEP. This paper also proposes a novel way of indexing CEP rules based on Geometric series. The proposed indexing is used for CEP Rule allocation and CEP rule pruning in the SCALACEP system. SCALACEP is evaluated and compared with other existing systems in the constrained virtualized environment and found to give better results in terms of throughput and reduction in Latency and Multicast.

Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of only flat sequence queries. In this paper, we introduce our nested CEP query language NEEL for expressing nested queries composed of sequence, negation, AND and OR operators. Thereafter, we also define its formal semantics. Subtle issues with negation and predicates within the nested sequence context are discussed. An E-Analytics system for processing nested CEP queries expressed in the NEEL language has been developed. Lastly, we demonstrate the utility of this technology by describing a case study of applying this technology to a real-world application in health care.

Fog computing is envisioned to enable profound applications in the Internet of Things (IoT). A key characteristic of such applications is the need to exchange vital information between distinct IoT devices in the form of event notifications, e. g., traffic conditions when performing traffic monitoring. Complex event processing (CEP) is a powerful paradigm to overcome the information gap from observing primary sensor data by IoT devices to delivering event notifications to the IoT application users. However, to perform CEP in a highly dynamic IoT environment, e. g., involving mobile and heterogeneous devices, require an extremely flexible design of a CEP system to adaptively meet the changing requirements and conditions in which the CEP system is executed. In this article, we show on the use case of CEP, “how to increase flexibility in a fog-cloud computing environment building on a methodology known as mechanism transitions”. In particular, we state and analyze two exemplary IoT use cases to show the potential of mechanism transitions. We identify and discuss possible promising mechanism transitions in the context of CEP. We perform an experimental study for operator placement and show how transitions help to adapt to conflicting performance objectives.

Complex event processing (CEP) is a new computing paradigm that extracts meaningful information from a sequence of events in real-time application domains. Existing software-based CEP systems, however, suffer from poor event processing performance because such real-time application domains require high performance. Recent promising approaches would seem to be use of FPGAs in order to accelerate event processing performance. This chapter presents an efficient complex event processing framework, designed to process a large number of sequential events on FPGAs. Key to the success of our work is logic automation generated with our C-based event language. With this language, we have achieved both higher event processing performance and higher flexibility for application designs than those with SQL-based CEP systems. Evaluations on an FPGA-based NIC show that we have achieved 12.3 times better event processing performance than does CPU software in a financial trading application.