Dataset Information

Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis.

ABSTRACT: Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

SUBMITTER: Bezerra A

PROVIDER: S-EPMC6631682 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis.

Bezerra Aguinaldo A Silva Ivanovitch I Guedes Luiz Affonso LA Silva Diego D Leitão Gustavo G Saito Kaku K

Sensors (Basel, Switzerland) 20190620 12

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in ...[more]

PMID: 31226811

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Project description:BackgroundValue sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors.ObjectivesThe aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets.MethodsWe developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors.ResultsThirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets-while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients.ConclusionThe proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

Dataset Information

Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis.

Publications

Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis.

Similar Datasets

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