A Service-Oriented Approach to Modeling and Reusing Event Correlations

In an IoT (Internet of Things) environment, event correlations may be dynamically interwoven because events usually span over many interrelated sensors. Our previous works used frequent sequence to measure the event correlations and mined them from a statistical perspective. We also proposed a service hyperlink model to encapsulate the event correlations. With the service hyperlink model, we made a preliminary attempt to reuse valuable event correlations to facilitate IoT applications. To consummate our previous method, this paper refines it in reusing the correlations, and focuses on which event would most probably appear after a previous event has occurred. To effectively mine the event correlations, we extend the traditional motif mining algorithms by introducing time constraint. Moreover, we connect services by service hyperlinks (i.e., abstraction of event correlations) to form a directed graph. An event can be routed on this graph. We have applied our approach in anomaly warning in a coal power plant and made extensive experiments to verify the effectiveness of the approach.

[1]  Jian Yu,et al.  A Service-Based Approach to Traffic Sensor Data Integration and Analysis to Support Community-Wide Green Commute in China , 2016, IEEE Transactions on Intelligent Transportation Systems.

[2]  Shen Su,et al.  A Proactive Service Model Facilitating Stream Data Fusion and Correlation , 2017, Int. J. Web Serv. Res..

[3]  Abdulhameed Alelaiwi,et al.  A collaborative resource management for big IoT data processing in Cloud , 2017, Cluster Computing.

[4]  Boualem Benatallah,et al.  Event Correlation Analytics: Scaling Process Mining Using Mapreduce-Aware Event Correlation Discovery Techniques , 2015, IEEE Transactions on Services Computing.

[5]  Ibrahim Khalil,et al.  PEACE-Home: Probabilistic estimation of abnormal clinical events using vital sign correlations for reliable home-based monitoring , 2017, Pervasive Mob. Comput..

[6]  Céline Robardet,et al.  Graph dependency construction based on interval-event dependencies detection in data streams , 2016, Intell. Data Anal..

[7]  Ibrahim Khalil,et al.  A probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[8]  Paul W. P. J. Grefen,et al.  Correlation Mining: Mining Process Orchestrations Without Case Identifiers , 2015, ICSOC.

[9]  John F. Roddick,et al.  Sequential pattern mining -- approaches and algorithms , 2013, CSUR.

[10]  Florian Skopik,et al.  Combating advanced persistent threats: From network event correlation to incident detection , 2015, Comput. Secur..

[11]  Rui Wang,et al.  Software architecture construction and collaboration based on service dependency , 2015, 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[12]  Kavé Salamatian,et al.  Anomaly extraction in backbone networks using association rules , 2012, TNET.

[13]  Paul W. P. J. Grefen,et al.  Correlation Miner: Mining Business Process Models and Event Correlations Without Case Identifiers , 2017, Int. J. Cooperative Inf. Syst..

[14]  Shen Su,et al.  Service Hyperlink: Modeling and Reusing Partial Process Knowledge by Mining Event Dependencies among Sensor Data Services , 2017, 2017 IEEE International Conference on Web Services (ICWS).

[15]  Xuelong Li,et al.  Collective Representation for Abnormal Event Detection , 2017, Journal of Computer Science and Technology.

[16]  Krishna R. Pattipati,et al.  Fault Diagnosis of HVAC Air-Handling Systems Considering Fault Propagation Impacts Among Components , 2017, IEEE Transactions on Automation Science and Engineering.

[17]  Panos Kalnis,et al.  ACME: A scalable parallel system for extracting frequent patterns from a very long sequence , 2014, The VLDB Journal.

[18]  Boudewijn F. van Dongen,et al.  Efficient Event Correlation over Distributed Systems , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[19]  Cheng-Zhong Xu,et al.  Quantifying event correlations for proactive failure management in networked computing systems , 2010, J. Parallel Distributed Comput..

[20]  Shen Su,et al.  An Approach to Modeling and Discovering Event Correlation for Service Collaboration , 2017, ICSOC.

[21]  Fang Dong,et al.  Copula Analysis of Latent Dependency Structure for Collaborative Auto-Scaling of Cloud Services , 2016, 2016 25th International Conference on Computer Communication and Networks (ICCCN).

[22]  Dimosthenis Kyriazis,et al.  Opportunistic Collaborative Service Networks: The Facilitator for Efficient Data and Services Exchange , 2015, IoT 360.

[23]  Hans-Arno Jacobsen,et al.  Process Discovery from Dependence-Complete Event Logs , 2016, IEEE Transactions on Services Computing.

[24]  K. R. Bharani,et al.  Efficient And Accurate Discovery Of Patterns In Sequence Data Sets , 2018 .

[25]  Brahim Medjahed,et al.  A Web Service Negotiation Management and QoS Dependency Modeling Framework , 2016, TMIS.

[26]  Bo Cheng,et al.  Situation-Aware IoT Service Coordination Using the Event-Driven SOA Paradigm , 2016, IEEE Transactions on Network and Service Management.