An Optimized KDD Process for Collecting and Processing Ingested and Streaming Healthcare Data

Mavrogiorgou, Argyro; Kiourtis, Athanasios; Manias, George; Kyriazis, Dimosthenis

doi:10.1109/icics52457.2021.9464551

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…The first part is the average similarity between the sentence and all other sentences in the Knowledge Solution. This study calculates the similarity Sim B between each pair of sentences using Formula (14). The second part is the proportion of all vocabulary in the Knowledge Conclusions included in the keywords k w .…”

Section: ) Conclusion-generating Methods Based On Key Sentence Extrac...mentioning

confidence: 99%

“…Several scholars proposed classic methods in this field. Mavrogiorgou et al (2021) [14] proposed a KDD approach that concentrates on the selection, preprocessing, and transformation of healthcare data and verified its applicability in various healthcare scenarios with a prototype. Belcastro et al (2022) [15] presented a knowledge discovery method comprising three modules: discovering user mobility patterns, estimating public opinion, and discovering social media discussion topics.…”

Section: Introductionmentioning

confidence: 99%

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A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

2023

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In large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable of returning hierarchical, systematized knowledge results. The method can match the knowledge demands according to the keyword input by users and then present the knowledge supplies corresponding to the knowledge demands as results to the users. Firstly, a knowledge structure named Knowledge Demand is designed to represent the genuine needs of social media users. This knowledge structure measures the popularity of topic combinations in the Topic Map, so the topic combinations with high popularity are regarded as the main content of the Knowledge Demands. Secondly, the proposed method designs a hierarchical and systematic knowledge structure, named Knowledge Supply, which provides Knowledge Solutions matched with the Knowledge Demands. The Knowledge Supply is generated based on the Knowledge Element Repository, using the BLEU similarity matrix to retrieve Knowledge Elements with high similarity, and then clustering these Knowledge Elements into several knowledge schemes to extract the Knowledge Solutions. The organized Knowledge Elements and Knowledge Solutions are the presentation of each Knowledge Supply. Finally, this research crawls posts in the “Autohome Forum” and conducts an experiment by simulating the user’s actual knowledge search process. The experiment shows that the proposed method is an effective knowledge retrieval method, which can provide users with hierarchical and systematized knowledge.

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Section: ) Conclusion-generating Methods Based On Key Sentence Extrac...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

2023

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“…Big Data processing is a group of techniques for accessing large amounts of data in order to extract meaningful information for decision support and provision [48,49]. Big Data processing employs a range of methods, such as wordcount and string matching, which can be distributed across vast clusters of processing units [50].…”

Section: Big Data Processingmentioning

confidence: 99%

EverAnalyzer: A Self-Adjustable Big Data Management Platform Exploiting the Hadoop Ecosystem

et al. 2023

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Big Data is a phenomenon that affects today’s world, with new data being generated every second. Today’s enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users’ processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform’s efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments.

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Simulafed: an enhanced federated simulated environment for privacy and security in health

Rivas,

Fernandez-Basso,

Morcillo-Jimenez

et al. 2024

Computing

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An Optimized KDD Process for Collecting and Processing Ingested and Streaming Healthcare Data

Cited by 7 publications

References 17 publications

A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

EverAnalyzer: A Self-Adjustable Big Data Management Platform Exploiting the Hadoop Ecosystem

Simulafed: an enhanced federated simulated environment for privacy and security in health

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