i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry

Kim, Lise; Yahia, Esma; Segonds, Frédéric; Véron, Philippe; Mallet, Antoine

doi:10.1016/j.compind.2021.103527

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Five of the publications are sorted into the production area, which emerges as the predominant domain. A distinction is observed between usecases leveraging simulated data (e.g., IDs 6, 24, and 25) and those extracting insights from operational environments (e.g., IDs 4,7,12,14,35,and 36). Paper ID 14 is a special use-case as it originates from the agricultural domain.…”

Section: A Rq1: What Is the Current State Of The Art For Rca Regardin...mentioning

confidence: 99%

Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

Koval,

Knollmeyer,

Mathias

et al. 2024

IEEE Access

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Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that knowledge graphs, association rule mining, and deep learning algorithms are at the forefront of root cause analysis technology. Notably, the use of neural networks has become increasingly prominent in recent literature, offering significant improvements in analyzing complex data sets. These technologies facilitate a more nuanced and faster analysis by leveraging large-scale data integration and automated learning capabilities. We establish that the integration of these advanced technologies not only speeds up the root cause analysis process but also enhances the accuracy and depth of analysis, surpassing traditional methods that rely heavily on manual interpretation. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of "Production IT." Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.

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Section: A Rq1: What Is the Current State Of The Art For Rca Regardin...mentioning

confidence: 99%

Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

Koval,

Knollmeyer,

Mathias

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Technique name Parameter settings i-Dataquest 23 Text extractor-Apache Tika and Tesseract NLP-Stanford core NLP Python library-neo4j library Fuzzy logic 18 Tool used-NP2Vec Parameter tuned using grid search where the value ranges between 0.2-0.4, word embedding dimension-300, number of association rules used for each query-50, confidence threshold-0.8, and support threshold-1.0 OFIE 19 Word2Vec, error measure: Levenshtein distance, fuzzy error tolerance rate, [1][2][3][4] fuzzy error types used: insertion, deletion, and substitution, and packages used: REGEX, fuzzywuzzy, and fuzzy REGEX.…”

Section: Ta B L E 3 Experimental Settingsmentioning

confidence: 99%

“…Kim et al 23 developed i‐Dataquest, a heterogeneous information retrieval tool based on data graphs, for the manufacturing industry. The i‐Dataquest prototype was built using Neo4J for graph system development, StanfordNLP for natural language processing, and ConceptNet for lexical resource management.…”

Section: Related Workmentioning

confidence: 99%

Hybrid optimized query expansion strategy for semantic information retrieval using spatial bound whale and binary moth flame optimization algorithm

Kumar

Sharma

2022

Concurrency and Computation

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Summary The standard information retrieval systems mainly extract documents based on the relative keywords and this method is not effective since the related information can be only identified by extracting the semantics present in the text. Hence to overcome this problem, a semantic‐based query retrieval model is formulated in this article for query processing, identification, extraction, expansion, sorting, and filtering. A keyword expansion approach using the hybrid spatial bound whale optimization algorithm‐binary moth flame optimization algorithm is proposed in this work. In this way, the complexity associated with insufficient query information is overcome. The efficiency of the proposed model in extracting the top k relevant information is evaluated using the experiments conducted in the TREC data using different performance metrics such as precision@k, recall@k, mean reciprocal rank@k, mean average precision, and normalized discounted cumulative gain (NDCG@k). The proposed model achieves improved outcomes when compared to different state‐of‐art techniques such as i‐Dataquest, fuzzy logic, ontological framework for information extraction, and personal knowledge management in terms of precision@k, recall@k, mean reciprocal rank@k, exact match, F1‐score, and NDCG@k. The proposed model gives a mean reciprocal rank@k score of 0.8901, 0.8947, 0.8958, and 0.9014 for the k‐values 1, 3, 5, and 10, respectively. The MAP@k score for the top‐10 result suggestion retrieved is 0.39056. The exact match score of the proposed model for the newsgroup, SQuAD 1.0, and SQUAD 2.0 is 93.25, 94.65, and 95.25.

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KARMEN: A Knowledge Graph Based Proposal to Capture Expert Designer Experience and Foster Expertise Transfer

Camara,

Véron,

Segonds

et al. 2024

IFIP Advances in Information and Communication Technology

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i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry

Cited by 9 publications

References 35 publications

Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

Hybrid optimized query expansion strategy for semantic information retrieval using spatial bound whale and binary moth flame optimization algorithm

KARMEN: A Knowledge Graph Based Proposal to Capture Expert Designer Experience and Foster Expertise Transfer

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