Business Process Workflow Monitoring Using Distributed CBR with GPU Computing

Ioannis Agorgianitis, Stelios Kapetanakis, Miltos Miltos Petridis, Andrew Fish

Research output: Contribution to conferencePaper

Abstract

Workflow monitoring and diagnosis can be a complex process
involving sophisticated computational intensive operations.
The ever-growing data generation and its utilisation
have increased the complexity of workflow domains leading
to an increased interest in distributed approaches for efficient
workflow monitoring. Existing work has proposed a
CBR enhancement to tackle deficiencies in areas where data
volumes increase significantly. In such areas, the notion of a
“data volume” component was proposed in an enhanced
CBR architecture. This work proceeds further by evaluating
a proposed distributed CBR lifecycle based on GPU programming
to abstract further and evaluate the hypothesis
that: increased data volumes can be tackled efficiently using
distributed case bases and processing on demand. Our proposed
approach is evaluated against previous work and it
shows promising speedup gains. This paper signposts future
research areas in distributed CBR paradigms,
Original languageEnglish
Pages495-498
Number of pages4
Publication statusPublished - 8 May 2017
EventThirtieth International FLAIRS Conference - Marco Island, Florida, United States
Duration: 22 May 201724 May 2017
Conference number: 30
http://FLAIRS-30

Conference

ConferenceThirtieth International FLAIRS Conference
Abbreviated titleFLAIRS-30
CountryUnited States
CityFlorida
Period22/05/1724/05/17
Internet address

Fingerprint

Process monitoring
Monitoring
Industry
Processing
Graphics processing unit

Cite this

Agorgianitis, I., Kapetanakis, S., Miltos Petridis, M., & Fish, A. (2017). Business Process Workflow Monitoring Using Distributed CBR with GPU Computing. 495-498. Paper presented at Thirtieth International FLAIRS Conference , Florida, United States.
Agorgianitis, Ioannis ; Kapetanakis, Stelios ; Miltos Petridis, Miltos ; Fish, Andrew. / Business Process Workflow Monitoring Using Distributed CBR with GPU Computing. Paper presented at Thirtieth International FLAIRS Conference , Florida, United States.4 p.
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Agorgianitis, I, Kapetanakis, S, Miltos Petridis, M & Fish, A 2017, 'Business Process Workflow Monitoring Using Distributed CBR with GPU Computing' Paper presented at Thirtieth International FLAIRS Conference , Florida, United States, 22/05/17 - 24/05/17, pp. 495-498.

Business Process Workflow Monitoring Using Distributed CBR with GPU Computing. / Agorgianitis, Ioannis; Kapetanakis, Stelios; Miltos Petridis, Miltos; Fish, Andrew.

2017. 495-498 Paper presented at Thirtieth International FLAIRS Conference , Florida, United States.

Research output: Contribution to conferencePaper

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T1 - Business Process Workflow Monitoring Using Distributed CBR with GPU Computing

AU - Agorgianitis, Ioannis

AU - Kapetanakis, Stelios

AU - Miltos Petridis, Miltos

AU - Fish, Andrew

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AB - Workflow monitoring and diagnosis can be a complex processinvolving sophisticated computational intensive operations.The ever-growing data generation and its utilisationhave increased the complexity of workflow domains leadingto an increased interest in distributed approaches for efficientworkflow monitoring. Existing work has proposed aCBR enhancement to tackle deficiencies in areas where datavolumes increase significantly. In such areas, the notion of a“data volume” component was proposed in an enhancedCBR architecture. This work proceeds further by evaluatinga proposed distributed CBR lifecycle based on GPU programmingto abstract further and evaluate the hypothesisthat: increased data volumes can be tackled efficiently usingdistributed case bases and processing on demand. Our proposedapproach is evaluated against previous work and itshows promising speedup gains. This paper signposts futureresearch areas in distributed CBR paradigms,

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Agorgianitis I, Kapetanakis S, Miltos Petridis M, Fish A. Business Process Workflow Monitoring Using Distributed CBR with GPU Computing. 2017. Paper presented at Thirtieth International FLAIRS Conference , Florida, United States.