Continuous natural language processing pipeline strategy

Pölöskei, István

doi:10.1109/saci51354.2021.9465571

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Then, the data processing batch jobs are triggered to perform the analysis on the cold path in line with the lambda architecture. For the hot path, the streaming data are cached in a NoSQL document database to provide a less accurate but real-time data source for reporting purposes [18].…”

Section: Actions Takenmentioning

confidence: 99%

Spark-Based Digital Factory Design

Pölöskei

2022

Acta Electrotechnica Et Informatica

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Big data processing often uses the paradigm of parallelism by computing directly on top of the distributed data storage. The existing big data workflows unify the data processing practices to utilize the cloud’s native computational potentials to offer advanced machine learning and BI capabilities. Spark is an open-source massively parallel in-memory data processing framework, the current state-of-the-art. The primary approach is to break down the job into granular-level executed tasks, enabling parallelization. In the discussed case study, through IoT – cloud solutions, the plant data can be converted into an analyzable form to let the farther machine learning modules produce added value. To maximize the efficiency of the processing and accumulation, cloud-based components are introduced. Based on the data insights, the appropriate operative actions can be taken. The cost and performance optimization methods were also discussed in the study. Through achieving higher degree of digitalization, the control over the production increased.

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Section: Actions Takenmentioning

confidence: 99%

Spark-Based Digital Factory Design

Pölöskei

2022

Acta Electrotechnica Et Informatica

View full text Add to dashboard Cite

show abstract

Continuous natural language processing pipeline strategy

Cited by 1 publication

References 16 publications

Spark-Based Digital Factory Design

Spark-Based Digital Factory Design

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