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Pregled bibliografske jedinice broj: 1034134

Latent Process Discovery Using Evolving Tokenized Transducer


Krleža, Dalibor; Vrdoljak, Boris; Brčić, Mario
Latent Process Discovery Using Evolving Tokenized Transducer // IEEE access, 7 (2019), 169657-169676 doi:10.1109/ACCESS.2019.2955245 (međunarodna recenzija, članak, znanstveni)


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Naslov
Latent Process Discovery Using Evolving Tokenized Transducer

Autori
Krleža, Dalibor ; Vrdoljak, Boris ; Brčić, Mario

Izvornik
IEEE access (2169-3536) 7 (2019); 169657-169676

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Anomaly detection ; Knowledge acquisition ; Learning automata ; Machine learning ; Pattern recognition ; Sequences

Sažetak
Today organizations capture and store an abundant amount of data from their interaction with clients, internal information systems, technical systems and sensors. Data captured this way comprises many useful insights that can be discovered by various analytical procedures and methods. Discovering regular and irregular data sequences in the captured data can reveal processes performed by the organization, which can be then assessed, measured and optimized, to achieve overall better performance, lower costs, resolve congestions, find potentially fraudulent activities and similar. Besides process discovery, capturing data sequences can give additional behavioral and tendency insights for various observations in the organization, such as sales dynamic, customer behaviour and similar. The issue is that most of the captured data intertwine multiple processes, customers, cases, products in a single data log or data stream. In this article, we propose an evolving tokenized transducer (ETT), capable of learning data sequences from a multi-contextual data log or stream. The proposed ETT is a semi-supervised relational learning method that can be used as a classifier on an unknown data log or stream, revealing previously learned data sequences. The proposed ETT was tested on multiple synthetic and real-life cases and datasets, such as dataset of retail sales sequences, hospital process log involving septic patient treatment and BPI challenge 2019 dataset. Test results are successful, revealing ETT as a prominent process discovery method.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
KK.01.2.1.01.0041
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Dalibor Krleža (autor)

Avatar Url Mario Brčić (autor)

Avatar Url Boris Vrdoljak (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Krleža, Dalibor; Vrdoljak, Boris; Brčić, Mario
Latent Process Discovery Using Evolving Tokenized Transducer // IEEE access, 7 (2019), 169657-169676 doi:10.1109/ACCESS.2019.2955245 (međunarodna recenzija, članak, znanstveni)
Krleža, D., Vrdoljak, B. & Brčić, M. (2019) Latent Process Discovery Using Evolving Tokenized Transducer. IEEE access, 7, 169657-169676 doi:10.1109/ACCESS.2019.2955245.
@article{article, author = {Krle\v{z}a, Dalibor and Vrdoljak, Boris and Br\v{c}i\'{c}, Mario}, year = {2019}, pages = {169657-169676}, DOI = {10.1109/ACCESS.2019.2955245}, keywords = {Anomaly detection, Knowledge acquisition, Learning automata, Machine learning, Pattern recognition, Sequences}, journal = {IEEE access}, doi = {10.1109/ACCESS.2019.2955245}, volume = {7}, issn = {2169-3536}, title = {Latent Process Discovery Using Evolving Tokenized Transducer}, keyword = {Anomaly detection, Knowledge acquisition, Learning automata, Machine learning, Pattern recognition, Sequences} }
@article{article, author = {Krle\v{z}a, Dalibor and Vrdoljak, Boris and Br\v{c}i\'{c}, Mario}, year = {2019}, pages = {169657-169676}, DOI = {10.1109/ACCESS.2019.2955245}, keywords = {Anomaly detection, Knowledge acquisition, Learning automata, Machine learning, Pattern recognition, Sequences}, journal = {IEEE access}, doi = {10.1109/ACCESS.2019.2955245}, volume = {7}, issn = {2169-3536}, title = {Latent Process Discovery Using Evolving Tokenized Transducer}, keyword = {Anomaly detection, Knowledge acquisition, Learning automata, Machine learning, Pattern recognition, Sequences} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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