EVA: An event algebra supporting complex event specification

Hinze, Annika; Voisard, Agnès

doi:10.1016/j.is.2014.07.003

Cited by 19 publications

(7 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various event composition languages exist, most of which support a basic set of pattern operators such as sequence of events, enumeration, and temporal windows over sets of events. A detailed discussion of event languages, algebras and their semantics is described by Hinze and Voisard [24]. From the description of the query model specific operators can be compiled that allow processing event streams at run-time.…”

Section: Strengthmentioning

confidence: 99%

From event streams to process models and back: Challenges and opportunities

Soffer

Hinze

Koschmider

et al. 2019

Information Systems

Self Cite

View full text Add to dashboard Cite

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 $ This paper is an outcome of discussions and collaborations that were initiated at the Dagstuhl seminar 16341 on "Integrating Process-Oriented and Event-Based Systems" 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.

show abstract

Section: Strengthmentioning

confidence: 99%

From event streams to process models and back: Challenges and opportunities

Soffer

Hinze

Koschmider

et al. 2019

Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several formalisms have been proposed to specify the execution semantics of individual stream and event processing engines, ranging from automata [16,28] to temporal logic [17], to event algebras and calculi [29,30]. While these formalisms often capture the semantics of time-based operators in general and window operators in particular, they are tailored to the specific features offered by the language they formalize and thus are not suitable to study the similarities and differences between heterogeneous stream processing systems.…”

Section: Modeling Stream Processingmentioning

confidence: 99%

Defining the execution semantics of stream processing engines

et al. 2017

View full text Add to dashboard Cite

IntroductionSeveral modern data-intensive applications need to process large volumes of data on the fly as they are produced. Examples range from credit card fraud detection systems, which analyze massive streams of credit card transactions to identify suspicious patterns, to environmental monitoring applications that continuously analyze sensor data, to click stream analysis of Web sites that identify frequent patterns of interactions. More in general, stream processing is a central requirement in today's information systems.This state of facts pushed the development of several stream processing engines (SPEs) that continuously analyze streams of data to produce new results as new elements enter the streams. Unfortunately, existing SPEs adopt different processing models and standardized execution semantics have not yet emerged. This severely hampers the usability AbstractThe ability to process large volumes of data on the fly, as soon as they become available, is a fundamental requirement in today's information systems. Modern distributed stream processing engines (SPEs) address this requirement and provide low-latency and high-throughput data stream processing in cluster platforms, offering high-level programming interfaces that abstract from low-level details such as data distribution and hardware failures. The last decade saw a rapid increase in the number of available SPEs. However, each SPE defines its own processing model and standardized execution semantics have not emerged yet. This paper tackles this problem and analyzes the execution semantics of some widely adopted modern SPEs, namely Flink, Storm, Spark Streaming, Google Dataflow, and Azure Stream Analytics. We specifically target the notions of windowing and time, traditionally considered the key distinguishing factors that characterize the behavior of SPEs. We rely on the SECRET model, introduced in 2010 to analyze the windowing semantics for the SPEs available at that time. We show that SECRET models well some aspects of the behavior of modern SPEs, and we shed light on the evolution of SPEs after the introduction of SECRET by analyzing the elements that SECRET cannot fully capture. In this way, the paper contributes to the research in the area of stream processing by: (1) contrasting and comparing some widely used modern SPEs based on a formal model of their execution semantics; (2) discussing the evolution of SPEs since the introduction of the SECRET model; (3) suggesting promising research directions to direct further modeling efforts. Affetti et al. J Big Data (2017) et al. J Big Data (2017) 4:12 and interoperability of SPEs, since a user needs to understand system-specific aspects to confront various alternatives and select the ones that better suite her needs. SURVEY PAPERPage 2 of 24 AffettiThe main factors that differentiate the behaviors of SPEs are the models of windows and time they adopt [1]. Windows enable computations that would be otherwise unfeasible on unbounded datasets such as streams. For instance, counting the number o...

show abstract

“…They are used to show the relationship among detected events. We extend them from event constructors [18,27], which consider the aggregation demands on event composition. We present three types of operators: logical operator, mathematical operator, and temporal operator.…”

Section: 3mentioning

confidence: 99%

“…EPL statement is a description of event operators that are used to derive and aggregate information from event streams. The authors in [27] elaborated twenty EPLs to compare their The above EPL syntax includes some query clauses that are introduced by Ahmad et al [29]. The select clauses are used to select all properties or to specify the list of event properties and expressions.…”

Section: Event Processing Language and Rules Event Processingmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Complex Event Processing Engine for Intelligent Data Analysis in Integrated Information Systems

Wang

Zhou

Ali

et al. 2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Novel and effective engines for data analysis in integrated information systems are urgently required by diverse applications, in which massive business data can be analyzed to enable the capturing of various situations in real time. The performance of existing engines has limited capacity of data processing in distributed computing. Although Complex Event Processing (CEP) has enhanced the capacity of data analysis in information systems, it is still a big challenging task since events are rapidly increasing in diverse applications. In this paper, a lightweight intelligent data analysis system with a novel CEP engine named LIDA-E is introduced, which employs the knowledge base with rules and an event processing algorithm for analysis. Event models as well as operators support the rules for event selection and aggregation. These operators and rules have been utilized for constructing new CEP system architecture which combines expressiveness and efficiency in analysis. It adopts the agents and filter conception explicitly to provide the event transmission mechanism efficiently. Finally, the comparison between the proposed engine and the existing engine shows that LIDA-E has 48.65% averagely reduced time cost in different tests. The experimental results demonstrate that the developed architecture has better performance in both transmitting and analyzing a large number of events.

show abstract

EVA: An event algebra supporting complex event specification

Cited by 19 publications

References 79 publications

From event streams to process models and back: Challenges and opportunities

From event streams to process models and back: Challenges and opportunities

Defining the execution semantics of stream processing engines

A Novel Complex Event Processing Engine for Intelligent Data Analysis in Integrated Information Systems

Contact Info

Product

Resources

About