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Additions were proposed to the method of organizing the information security (IS) event management process of companies. Unlike existing solutions, the algorithm of the "Event handling" subprocess was detailed. This detailing is a complex, which includes the IS event processing substage. In addition, the proposed detailing of the "Event Handling" subprocess allows for covering the entire life cycle of an IS event. The performed research allows in practice to fill in potential gaps in information when creating a company's ISMS. An additional advantage of the proposed solution is the possibility of using this sub-process as an independent one. The proposed approach makes it possible to simplify the procedure for managing the information security of a company as a whole, as well as potentially reduce the costs of its construction for small companies and enterprises. Also, this subprocess can be considered as an independent information security management process, for example, for a company's CIS. The proposed solutions and additions, in contrast to similar studies, are characterized by invariance with respect to the methods of implementing the company's IS infrastructure solutions, and in particular its CIS. This ultimately allows, without changing the methodological tools, to scale this approach and adapt it to the ISMS of various companies. [ABSTRACT FROM AUTHOR]

Early warning systems are designed to inform the largest number of users, such as a country or a region, about a risky situation. However, in specific domains such as agriculture, it is commonly required for these alerts to be more specific according to the crops location and their properties. Consequently, the web services of these systems must be adapted. On the other hand, the enterprise services bus, with its mediation capabilities (such as message transformation and routing), and complex event processing with their monitoring characteristics, can be integrated to meet the adaptation requirements of web services at runtime. This paper presents an improvement for an early warning system for coffee production that, according to the area in which a crop is located and its phenology, manages the adaptation of alerts for coffee rust, based on the integration of an enterprise services bus and a complex events processing. [ABSTRACT FROM AUTHOR]

Abstract The domains of complex event processing (CEP) and business process management (BPM) have different origins but for many aspects draw on similar concepts. While specific combinations of BPM and CEP have attracted research attention, resulting in solutions to specific problems, we attempt to take a broad view at the opportunities and challenges involved. We first illustrate these by a detailed example from the logistics domain. We then propose a mapping of this area into four quadrants — - two quadrants drawing from CEP to create or extend process models and two quadrants starting from a process model to address how it can guide CEP. Existing literature is reviewed and specific challenges and opportunities are indicated for each of these quadrants. Based on this mapping, we identify challenges and opportunities that recur across quadrants and can be considered as the core issues of this combination. We suggest that addressing these issues in a generic manner would form a sound basis for future applications and advance this area significantly. Highlights • Complex event processing (CEP) and business process management (BPM) can mutually benefit through a convergence of formalisms and methods from both worlds. • For quadrants are discussed in detail: two for using CEP to create or extend process models and two on how process models can guide CEP. • Challenges and opportunities for this combination are identified as a sound basis for future research agenda and applications. • Identified issues bear cross-quadrant relevance and should be addressed fundamentally, not in the context of a specific application. [ABSTRACT FROM AUTHOR]

Abstract Complex Event Processing (CEP) is an event-based technology that allows us to process and correlate large data streams in order to promptly detect meaningful events or situations and respond to them appropriately. CEP implementations rely on the so-called Event Processing Languages (EPLs), which are used to implement the specific event types and event patterns to be detected for a particular application domain. To spare domain experts this implementation, the MEdit4CEP approach provides them with a graphical modeling editor for CEP domain, event pattern and action definition. From these graphical models, the editor automatically generates a corresponding Esper EPL code. Nevertheless, the generated code is syntactically but not semantically validated. To address this problem, MEdit4CEP is extended in this paper by Prioritized Colored Petri Net (PCPN) formalism, resulting in the MEdit4CEP-CPN approach. This approach provides both a novel PCPN domain-specific modeling language and a graphical editor. By using model transformations, event pattern models can be automatically transformed into PCPN models, and then into the corresponding PCPN code executable by CPN Tools. In addition, by using PCPNs we can compare the expected output with the actual output and can even conduct a quantitative analysis of the scenarios of interest. To illustrate our approach, we have conducted an air quality level detection case study and we show how this novel approach facilitates the modeling, simulation, analysis and semantic validation of complex event-based systems. [ABSTRACT FROM AUTHOR]

The article reports that the AllTrials campaign emphasized the central importance of reporting findings from trials that are negative, neutral or terminated early, as well as those with positive findings. Topics include examines that the CEP strategy is conceptually robust as reducing embolic material reaching the brain should reduce stroke and improve outcomes.

Highlights • The definition of specific mutation operators for an Event Processing Language. • A new system, MuEPL, which applys mutation testing to this Event Processing Language. • A quantitative evaluation of the mutants in five open-source programs. • The publication of the corresponding system as Open Source Software. Abstract The expert systems (ESs) have been developed to facilitate the users their tasks, to enhance the productivity and reduce losses. In order to replicate the behaviour of a human expert, they generate output using their stored knowledge base. In a ES, the accumulation of knowledge from different sources is a very important factor. Nowadays, we are living in a world where two crucial processes need to be perform quickly: decision-making and problem solving. The complexity of the decisions and problems lays on the different factors, situations and data that are involved. The Internet of Things (IoT) has been created to address these situations and helps the users to make correct decisions in real time according to the received data. In the concept of IoT, daily life objects are connected to each other so they can transfer data over the internet without a human to human interaction. The combination of IoT and ESs is a step further for the decision making problem, the received data from the IoT system will be sent to the ES, then the ES will process the information and send the results or decisions to the user. Given that correct decisions-making and problem solving are critical processed, these complex systems need to be tested. Mutation testing, which is a technique used in fault testing, has been examined in a range of studies where different programming languages have been used as well as in IoT expert systems evaluations. However, this technique has not been applied to an IoT programming language, which is noteworthy in the case of event processing languages (EPLs), that have been designed to address the main problems of IoT systems. Among the existing EPLs, the EPL of EsperTech is used the most often. In this paper, we apply mutation testing using MuEPL to EPL of EsperTech programs in order to simulate the common errors of the developers and to avoid the wrong decisions before moving out to ES in the IoT network. [ABSTRACT FROM AUTHOR]

The field of Complex Event Processing (CEP) deals with the techniques and tools developed to efficiently process pattern-based queries over data streams. The Semantic Web, through its standards and technologies, is in constant pursuit to provide solutions for such paradigm while employing the RDF data model. The integration of Semantic Web technologies in this context can handle the heterogeneity, integration and interpretation of data streams at the semantic level. In this paper, we propose and implement a new query language, called SPAseq, that extends SPARQL with new Semantic Complex Event Processing (SCEP) operators that can be evaluated over RDF graph-based events. The novelties of SPAseq include (i) the separation of general graph pattern matching constructs and temporal operators; (ii) the support for RDF graph-based events and multiple RDF graph streams; (iii) the expressibility of temporal operators such as Kleene+, conjunction, disjunction and event selection strategies; and (iv) the operators to integrate background information and streaming RDF graph streams. Hence, SPAseq enjoys good expressiveness compared with the existing solutions. Furthermore, we provide an efficient implementation of SPAseq using a non-deterministic automata (NFA) model for an efficient evaluation of the SPAseq queries. We provide the syntax and semantics of SPAseq and based on this, we show how it can be implemented in an efficient manner. Moreover, we also present an experimental evaluation of its performance, showing that it improves over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Complex 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. [ABSTRACT FROM AUTHOR]

Highlights • RECEP is used to process events in the context of IoT and Precision Agriculture. • The main components are: Rules Engine (RE) and Complex Event Processor (CEP). • RE allows configure dynamic rules conditioning input data from different sources. • RE control actions on actuators and notifications for end users or applications. • CEP detects patterns of simple events to take automatic decisions. • Case of use of RECEP: intelligent irrigation of an experimental banana plot. • CEP analyzes big data at high speeds using minimal computational resources. Abstract The Internet of Things (IoT) applications monitor large data flows and events in real time, some raw data is captured from devices located in wireless sensor networks (WSN) and used to make control decisions about actuators. This can be a major problem when the devices grow in number as well as the data that is captured. In this paper, we propose an architecture called "RECEP" for the dynamic processing of events generated in the context of IoT and Precision Agriculture (PA); it is made up of two components: Rules Engine (RE) and Complex Event Processor (CEP). RE allows you to configure dynamic rules conditioning input data from different sources and planning control actions on actuators, alerts, and notifications for end users or applications. The CEP component fuses the input data at the rate at which they arrive, with the rules established in the RE and it performs a prescriptive analysis that consists not only in predicting or detecting patterns of events, but in making automatic decisions. RECEP was implemented in a virtual machine with a 1.9 GHz CPU and 6 GB RAM, then it was integrated into an intelligent irrigation system of an experimental banana plot located in Machala-Ecuador. A WSN simulator was also used to generate sensor data in large quantities, the CEP was evaluated with several test cases, and results show that it consumes computational resources with a growth trend, represented by a logarithmic regression model (r-squared > 0.9); that is, the more events are processed, there is a minimum consumption of resources. It was tested for fifteen days; around 25 thousand events/s were processed. Our RECEP can be implemented in low-cost infrastructure typical of small and large banana producers. [ABSTRACT FROM AUTHOR]

In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system. [ABSTRACT FROM AUTHOR]

The increasing on-chip temperature has become a critical issue in the multiprocessor system on chip (MPSoC). It does not only disturb the reliability and performance of the system but it also increases the power consumption. Load balancing is a common scheduling technique to address the thermal issues where the load is transferred to relatively less used cores. However, during the transfer of workload, the destination cores may be in sleep mode and thus take some time to switch to the running mode. It results in wastage of some processing time. This paper presents a thermal-aware load balancing technique that avoids thermal emergencies while eliminating the switching delays. The proposed technique estimates the time taken by a task set to reach the temperature threshold values with the help of offline recorded thermal profiles of datasets. Moreover, cores are selected in a round-robin manner to equilibrate thermal gradients. The technique is based on Global Earliest Deadline First (GEDF) scheduling algorithm and considers ambient temperature, thermal cycles and energy consumption. The proposed technique is tested in a simulation environment comprising of scheduling and thermal simulation model. The results show that the technique reduces the average temperature up to 17%, thermal cycles up to 15%, energy consumption up to 20% and lowers the temporal and spatial gradients as compared to the commonly used predictive thermal-aware and thermal balancing techniques. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields. Note to Practitioners—Though directly applicable to general business process discovery, this paper is motivated by our collaboration with the company HEVA (Lyon, France) and health practitioners on patient care pathway discovery. The French hospitalization database that contains hospitalization history of all patients is used for this purpose and our goal is to determine the most meaningful process model of the patient hospitalization history. The hierarchical event structure of this paper provides a natural way of representing relationship between hospitalization events and is readily obtainable from the classical ICD-10 codes. The formal mathematical model and our optimization algorithm allow the end users to best balance between the faithfulness of the process model and its complexity. A case study of cardiovascular patients is presented to show the capability of the proposed approach to clearly capture the major patient pathways before and after the implementation of defibrillators. The results of this paper are highly valuable for doctors and public health decision makers, as crucial information is provided on patient care pathways for any selected cohort. [ABSTRACT FROM AUTHOR]

The computing of synaptic currents occupies a major part of computational cost when simulating a large scale spiking neural network. Based on the observation that the probability of a neuron receiving at least one spike from any synapses during a very tiny simulation time step is very small, we propose a time-driven algorithm corrected by an event-driven process (a hybrid time-event-driven algorithm) which consists of two procedures of computation. In the first procedure of the synaptic current computation, we suppose that the neuron in question receives no spike during the simulation time step, and thereby propose a time-driven method of joint decay process to reduce the computational complexity of the synaptic current. In the second procedure of the computation, we suppose that the neuron in question receives spikes during the simulation time step, and propose an event-driven local correction process to correct the total synaptic current that is calculated in the first procedure of the computation. We design a data structure of circular two-dimensional array for storing both conductance coefficients related with presynaptic neurons and correcting conductance related with postsynaptic neurons. Furthermore, in order to realize the local correction process quickly and effectively, we propose a new event-processing method to realize the local correction process based on the data structure of circular two-dimensional array. By theoretically comparing with that of traditional time-driven algorithm, it is found that the proposed time-event-driven algorithm reduces computational cost of synaptic current substantially. The simulation results further show the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the Big Data era, large volumes of data are continuously and rapidly generated from sensor networks, social network, the Internet, etc. Predicting from online event stream is an important task since users usually need to predict some future states and take some actions in advance. Many applications need online prediction models which can evolve automatically with data distribution drift and algorithms which can support single-pass processing of data, which are still faced with many challenges. In this paper, the authors propose a predictive complex event processing method based on evolving Bayesian networks. The Bayesian model is designed based on event type and time with inference method based on Gaussian mixture model and EM algorithm. When learning the structure of Bayesian network from event streams, this method supports calculating score metric incrementally when new data is arrived or edges in the network are changed. Evolving Bayesian network structure is supported based on hill-climbing method. The system can continuously monitor the Bayesian network model and modify it if it is found to be not appropriate for the new incoming data. The method of this paper is evaluated in road traffic domain with both real application data and data produced by a simulated transportation system. The total percentage error is 8.12% for real data and 7.78% for simulated data, while the best result for other methods is 11.79% for real data and 14.59% for simulated data. The experimental evaluations show that this method is effective for predictive complex event processing and it outperforms other popular methods when processing traffic prediction in intelligent transportation systems. [ABSTRACT FROM AUTHOR]

In most dangerous events, involving many people in large buildings, rescue workers need to intervene in a timely and targeted manner in order to help most number of people and secure the environments without wasting resources. This work presents an Internet of Things(IoT)-based framework, aiming at monitoring environmental parameters in order to alert rescuers when they exceed some alarm thresholds. A hardware infrastructure driven by a software layer adds flexibility and adaptability to the Complex Event Processing engine and to a rule engine-based reflective middleware that manages and analyzes raw data in conjunction with a knowledge base modeling the application domain. [ABSTRACT FROM AUTHOR]

Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run "online", but enables also "offline" (re-)execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person's condition. [ABSTRACT FROM AUTHOR]

Event processing is one of the cornerstone technologies in bridging physical world and cyber system together. Although event-based processing system has been widely used in various applications, however, consistency of event processing is still an open issue need further exploration. The inconsistency problem produces inaccurate detection result and corrupts the system correctness. In this paper, we propose a semantic approach for event modeling and detection model transformation with a semantic calculus system. Particularly, we first propose a complex event semantic model, OntoEvent, and define several key operators and properties to describe the logic, temporal and attribute relations in complex events. Second, we propose the concept of event constraint and elaborate the occurrence, temporal, and attribute functions to formalize the semantic implications in OntoEvent model. On that basis, we present the extraction rules and establish a calculus mechanism for constraints based on axioms. With these works, an automata-based detection model, named OntoCEP (Ontology-based Complex Event Detection), and a pipelined procedure for the assembly from constraints to OntoCEP model is proposed. The procedure is composed of several sequential phases and the consistency in each assembly phase is proved. Therefore, we establish a semantic-consistent mapping mechanism from event to detection model in the form of constraints. Experiments and evaluations prove that our approach ensure the consistency with event and detection models. Besides, our detection model consumes less computational resources and outperforms other selected benchmarked models in terms of computational efficiency and processing capability. [ABSTRACT FROM AUTHOR]

This paper presents a cross-layer reactive monitoring approach for Cloud computing environments. Based on complex event processing (CEP) methodology, our proposal monitors and analyzes performance metrics across Cloud layers to detect and repair performance-related problems. The approach utilizes novel CEP analysis rules and a new action manager framework. The proposed analysis rules are derived from a comprehensive analysis of the interactions between Cloud layers. The results of this study are used to reduce the number of monitored parameters, define the analysis rules and identify the causes of performance-related problems. Our novel action manager framework assigns a set of repair actions to each performance-related problem and checks the success of the applied action. The results of several experiments indicate that the time needed to fix a performance-related problem is reasonably short. They also show that the CPU overhead of using our approach is negligible. Moreover, experimental results demonstrate the merits of our approach in terms of speeding up the repair and reducing the number of triggered alarms compared to baseline methods. [ABSTRACT FROM AUTHOR]

As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new pieces of knowledge. And since many IoT applications require almost real-time reactivity to stimulus of the environment, such information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time reasoning based on the concepts of Semantic Stream and Fact Stream, as a natural extension of Complex Event Processing (CEP) and RDF (graph-based knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information; (b) the processing of streams can be implemented using CEP; (c) it is general enough to be applied to any Data Stream Management System (DSMS). We describe a scenario about patients flux monitoring in a hospital as an example of prospective application. Last, we present challenges and prospects on using machine learning and induction algorithms to learn abstractions and reasoning rules from a continuous data stream. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the parallel machine makespan minimization problem with the general sum of processing time based learning or aging effects. First, we prove that an optimal solution to the single machine case can be found by priority rules. Next, for the considered parallel machine problem, we construct the exact dynamic programming algorithm that can operate on real-valued job processing times, which is the only exact algorithm for the analysed problem. The computational analysis confirms that it can solve optimally moderate problem instances. [ABSTRACT FROM AUTHOR]

Complex Event Processing (CEP), which can identify patterns of interest from a large number of continuous data steam, is becoming more and more popular in manufacturing process monitoring. CEP rules are specified manually by domain expert, which is a limiting factor for its application in manufacturing enterprises. How to analysis historical data and automatically generate CEP rules is becoming a challenge research. This paper proposed a model of autoCEP for online monitoring in product manufacturing, which can automatically generate CEP rules based on association rules mining in key processes. First, the key quality factors in manufacturing process were extracted by grey entropy correlation analysis. Then, association rules mining method based on product process constraints was used to find the association rules between key factors and product quality. At last, the extracted rules are algorithmically transformed into CEP rules. The experimental results show the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]