Optimizing Database Performance in Complex Event Processing through Indexing Strategies

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.

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) 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.

The nature of data in enterprises and on the Internet is changing. Data used to be stored in a database first and queried later. Today timely processing of new data, represented as events, is increasingly valuable. In many domains, complex event processing (CEP) systems detect patterns of events for decision making. Examples include processing of environmental sensor data, trades in financial markets and RSS web feeds. Unlike conventional database systems, most current CEP systems pay little attention to query optimisation. They do not rewrite queries to more efficient representations or make decisions about operator distribution, limiting their overall scalability. This paper describes the Next CEP system that was especially designed for query rewriting and distribution. Event patterns are specified in a high-level query language and, before being translated into event automata, are rewritten in a more efficient form. Automata are then distributed across a cluster of machines for detection scalability. We present algorithms for query rewriting and distributed placement. Our experiments on the Emulab test-bed show a significant improvement in system scalability due to rewriting and distribution. 1.

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.

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.

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.

Complex Event Processing (CEP) systems extract interest situations from event flows based on event detection patterns. However, local event processing for distributed Internet of Things (IoT) has not been discussed yet. Besides, it is complex or impossible to discover such patterns in some applications of IoT. In this article, we design a complex event service to process event flows based on IoT resource models, which does not depend on existing patterns, and deals with both discrete events and continuous variables. To improve the CEP performance, local IoT resources are used for local event processing, and a lazy exchange method is designed to realize the collaborated event processing between network edges and a data center. Our evaluation shows that our solution is feasible and effective.

Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing

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.

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 (or Composite) event processing systems have become more popular in a number of areas.Non-deterministic finite automata (NFA) are frequently used to evaluate CEP queries.However, it is complex or difficult to use the traditional NFA-based method to process patterns with conjunction and negation.In this paper, we proposed a new CEP system LogCEP using pushdown automaton to support efficient processing of conjunction and negation.First, the semantic and query language specification of LogCEP system are presented.Then, an automaton named LogPDA is proposed for query processing in LogCEP system.LogPDA construction method describes how to convert a query to LogPDA automation.The LogPDA execution approach describes how to detect the specified pattern using LogPDA.Meanwhile, most of previous NFA-based optimizations can be employed to improve the evaluation efficiency.Finally, our simulation based experimental results show that our method not only extended the expressibility and processing capability but also didn't lead to efficiency decreasing.

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.

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).