The Experts below are selected from a list of 106089 Experts worldwide ranked by ideXlab platform

Nenad Stojanovic - One of the best experts on this subject based on the ideXlab platform.

  • stream reasoning and complex Event Processing in etalis
    Social Work, 2012
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data Events remains a challenge --that is, how to integrate heterogeneous Event streams, combine them with background knowledge e.g., an ontology, and perform Event Processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex Event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming Events with respect to background knowledge. ETALIS implements two languages for specification of Event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on Processing of the temporal, static, or slowly evolving knowledge.

  • an approach for using complex Event Processing for adaptive augmented reality in cultural heritage domain experience report
    Distributed Event-Based Systems, 2012
    Co-Authors: Nenad Stojanovic, Ljiljana Stojanovic, Ana Cabrera, Tobias Schuchert
    Abstract:

    In this paper we present a novel approach for using intelligent complex Event Processing with Augmented Reality to achieve Adapted Augmented Reality in Cultural Heritage domain. Intelligent complex Event Processing enables the efficient real-time Processing of sensor data and its logic-based nature supports a declarative definition of attention situations. We use semantic technologies to correlate sensor data through modeling of the interesting situations and the background knowledge. This approach has been implemented based on the iCEP framework for intelligent Complex Event Processing. The results of the approach have been applied in the EU project ARtSENSE.

  • retractable complex Event Processing and stream reasoning
    Rules and Rule Markup Languages for the Semantic Web, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Complex Event Processing (CEP) deals with Processing of continuously arriving Events with the goal of identifying meaningful patterns (complex Events). In existing stream database approaches, CEP is manly concerned by temporal relations between Events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in Event Processing. Events are often assumed to be immutable and therefore always correct. Revision in Event Processing deals with the circumstance that certain Events may be revoked. This necessitates to reconsider complex Events which might have been computed based on the original, flawy history as soon as part of that history is corrected. In this paper, we present a novel approach for knowledge-based CEP and Stream Reasoning, including revisions of Events too. We present a rule-based language for pattern matching over Event streams with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach.

  • ep sparql a unified language for Event Processing and stream reasoning
    The Web Conference, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Streams of Events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound Events within streams of simple Events. State-of-the-art EP provides on-the-fly analysis of Event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by Event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex Events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective Event Processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

  • etalis rule based reasoning in Event Processing
    Reasoning in Event-Based Distributed Systems, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Roland Stuhmer, Nenad Stojanovic, Rudi Studer
    Abstract:

    Complex Event Processing (CEP) is concerned with timely detection of complex Events within multiple streams of atomic occurrences, and has useful applications in areas including financial services, mobile and sensor devices, click stream analysis and so forth. In this chapter, we present ETALIS Language for Events. It is an expressive language for specifying and combining complex Events. For this language we provide both a syntax as well as a clear declarative formal semantics. The execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present experimental results of our running prototype. Further on, we show how our logic rule-based approach compares with a non-logic approach in respect of performance.

Quincy J J Wong - One of the best experts on this subject based on the ideXlab platform.

  • repetitive thinking in social anxiety disorder are anticipatory Processing and post Event Processing facets of an underlying unidimensional construct
    Behavior Therapy, 2019
    Co-Authors: Quincy J J Wong, Peter M Mcevoy, Ronald M Rapee
    Abstract:

    Abstract Existing literature suggests that anticipatory Processing and post-Event Processing—two repetitive thinking processes linked to social anxiety disorder (SAD)—might be better conceptualized as facets of an underlying unidimensional repetitive thinking construct. The current study tested this by examining potential factor structures underlying anticipatory Processing and post-Event Processing. Baseline data from two randomized controlled trials, consisting of 306 participants with SAD who completed anticipatory Processing and post-Event Processing measures in relation to a speech task, were subjected to confirmatory factor analysis. A bifactor model with a General Repetitive Thinking factor and two group factors corresponding to anticipatory Processing and post-Event Processing best fit with the data. Further analyses indicated an optimal model would include only the General Repetitive Thinking factor (reflecting anticipatory Processing and a specific aspect of post-Event Processing) and Post-Event Processing group factor (reflecting another specific aspect of post-Event Processing that is separable), providing evidence against a unidimensional account of repetitive thinking in SAD. Analyses also indicated that the General Repetitive Thinking factor had moderately large associations with social anxiety and life interference (rs = .43 to .47), suggesting its maladaptive nature. The separable Post-Event Processing group factor only had small associations with social anxiety (rs = .16 to .27) and was not related to life interference (r = .11), suggesting it may not, in itself, be a maladaptive process. Future research that further characterises the bifactor model components and tests their utility has the potential to improve the conceptualisation and assessment of repetitive thinking in SAD.

  • anticipatory Processing and post Event Processing in social anxiety disorder an update on the literature
    Australian Psychologist, 2016
    Co-Authors: Quincy J J Wong
    Abstract:

    Two repetitive thinking processes that have been proposed in prominent maintenance models of social anxiety disorder (SAD) are anticipatory Processing and post-Event Processing. Research into these two processes has steadily increased over the last 20 years. This review highlights the main lines of existing research on anticipatory Processing and post-Event Processing, including studies on the nature of these processes, their association with social anxiety, the predictors, and consequences of these processes, as well as how these processes respond to treatments for SAD. The review also highlights some of the conceptual and methodological issues that have prEvented the literature on anticipatory Processing and post-Event Processing from being more integrated and focused. Finally, the review draws together some new directions in terms of theory and research to further advance the field.

Darko Anicic - One of the best experts on this subject based on the ideXlab platform.

  • stream reasoning and complex Event Processing in etalis
    Social Work, 2012
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data Events remains a challenge --that is, how to integrate heterogeneous Event streams, combine them with background knowledge e.g., an ontology, and perform Event Processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex Event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming Events with respect to background knowledge. ETALIS implements two languages for specification of Event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on Processing of the temporal, static, or slowly evolving knowledge.

  • retractable complex Event Processing and stream reasoning
    Rules and Rule Markup Languages for the Semantic Web, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Complex Event Processing (CEP) deals with Processing of continuously arriving Events with the goal of identifying meaningful patterns (complex Events). In existing stream database approaches, CEP is manly concerned by temporal relations between Events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in Event Processing. Events are often assumed to be immutable and therefore always correct. Revision in Event Processing deals with the circumstance that certain Events may be revoked. This necessitates to reconsider complex Events which might have been computed based on the original, flawy history as soon as part of that history is corrected. In this paper, we present a novel approach for knowledge-based CEP and Stream Reasoning, including revisions of Events too. We present a rule-based language for pattern matching over Event streams with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach.

  • ep sparql a unified language for Event Processing and stream reasoning
    The Web Conference, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Streams of Events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound Events within streams of simple Events. State-of-the-art EP provides on-the-fly analysis of Event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by Event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex Events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective Event Processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

  • etalis rule based reasoning in Event Processing
    Reasoning in Event-Based Distributed Systems, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Roland Stuhmer, Nenad Stojanovic, Rudi Studer
    Abstract:

    Complex Event Processing (CEP) is concerned with timely detection of complex Events within multiple streams of atomic occurrences, and has useful applications in areas including financial services, mobile and sensor devices, click stream analysis and so forth. In this chapter, we present ETALIS Language for Events. It is an expressive language for specifying and combining complex Events. For this language we provide both a syntax as well as a clear declarative formal semantics. The execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present experimental results of our running prototype. Further on, we show how our logic rule-based approach compares with a non-logic approach in respect of performance.

  • a rule based language for complex Event Processing and reasoning
    Web Reasoning and Rule Systems, 2010
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Roland Stuhmer, Nenad Stojanovic, Rudi Studer
    Abstract:

    Complex Event Processing (CEP) is concerned with timely detection of complex Events within multiple streams of atomic occurrences. It has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. Numerous approaches in CEP have already been proposed in the literature. Event Processing systems with a logic-based representation have attracted considerable attention as (among others reasons) they feature formal semantics and offer reasoning service. However logic-based approaches are not optimized for run-time Event recognition (as they are mainly query-driven systems). In this paper, we present an expressive logic-based language for specifying and combining complex Events. For this language we provide both a syntax as well as a formal declarative semantics. The language enables efficient run time Event recognition and supports deductive reasoning. Execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present the performance results showing the competitiveness of our approach.

Paul Fodor - One of the best experts on this subject based on the ideXlab platform.

  • stream reasoning and complex Event Processing in etalis
    Social Work, 2012
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data Events remains a challenge --that is, how to integrate heterogeneous Event streams, combine them with background knowledge e.g., an ontology, and perform Event Processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex Event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming Events with respect to background knowledge. ETALIS implements two languages for specification of Event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on Processing of the temporal, static, or slowly evolving knowledge.

  • retractable complex Event Processing and stream reasoning
    Rules and Rule Markup Languages for the Semantic Web, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Complex Event Processing (CEP) deals with Processing of continuously arriving Events with the goal of identifying meaningful patterns (complex Events). In existing stream database approaches, CEP is manly concerned by temporal relations between Events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in Event Processing. Events are often assumed to be immutable and therefore always correct. Revision in Event Processing deals with the circumstance that certain Events may be revoked. This necessitates to reconsider complex Events which might have been computed based on the original, flawy history as soon as part of that history is corrected. In this paper, we present a novel approach for knowledge-based CEP and Stream Reasoning, including revisions of Events too. We present a rule-based language for pattern matching over Event streams with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach.

  • ep sparql a unified language for Event Processing and stream reasoning
    The Web Conference, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Nenad Stojanovic
    Abstract:

    Streams of Events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound Events within streams of simple Events. State-of-the-art EP provides on-the-fly analysis of Event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by Event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex Events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective Event Processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

  • etalis rule based reasoning in Event Processing
    Reasoning in Event-Based Distributed Systems, 2011
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Roland Stuhmer, Nenad Stojanovic, Rudi Studer
    Abstract:

    Complex Event Processing (CEP) is concerned with timely detection of complex Events within multiple streams of atomic occurrences, and has useful applications in areas including financial services, mobile and sensor devices, click stream analysis and so forth. In this chapter, we present ETALIS Language for Events. It is an expressive language for specifying and combining complex Events. For this language we provide both a syntax as well as a clear declarative formal semantics. The execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present experimental results of our running prototype. Further on, we show how our logic rule-based approach compares with a non-logic approach in respect of performance.

  • a rule based language for complex Event Processing and reasoning
    Web Reasoning and Rule Systems, 2010
    Co-Authors: Darko Anicic, Paul Fodor, Sebastian Rudolph, Roland Stuhmer, Nenad Stojanovic, Rudi Studer
    Abstract:

    Complex Event Processing (CEP) is concerned with timely detection of complex Events within multiple streams of atomic occurrences. It has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. Numerous approaches in CEP have already been proposed in the literature. Event Processing systems with a logic-based representation have attracted considerable attention as (among others reasons) they feature formal semantics and offer reasoning service. However logic-based approaches are not optimized for run-time Event recognition (as they are mainly query-driven systems). In this paper, we present an expressive logic-based language for specifying and combining complex Events. For this language we provide both a syntax as well as a formal declarative semantics. The language enables efficient run time Event recognition and supports deductive reasoning. Execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present the performance results showing the competitiveness of our approach.

Mathias Weske - One of the best experts on this subject based on the ideXlab platform.

  • monitoring of business processes with complex Event Processing
    Business Process Management, 2013
    Co-Authors: Susanne Bulow, Michael Backmann, Nico Herzberg, Thomas Hille, Andreas Meyer, Benjamin Ulm, Tsun Yin Wong, Mathias Weske
    Abstract:

    Business process monitoring enables a fast and specific overview of the process executions in an enterprise. Traditionally, this kind of monitoring requires a coherent Event log. Yet, in reality, execution information is often heterogeneous and distributed. In this paper, we present an approach that enables monitoring of business processes with execution data, independently of the structure and source of the Event information. We achieve this by implementing an open source Event Processing platform combining existing techniques from complex Event Processing and business process management. Event Processing includes transformation for abstraction as well as correlation to process instances and BPMN elements. Monitoring rules are automatically created from BPMN models and executed by the platform.