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Web and Big Data [electronic resource] : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6–8, 2023, Proceedings, Part III / edited by Xiangyu Song, Ruyi Feng, Yunliang Chen, Jianxin Li, Geyong Min.

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Material type:

TextSeries: Lecture Notes in Computer Science ; 14333Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XVII, 524 p. 204 illus., 160 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9789819723874

Subject(s):

-- Digital techniques

Additional physical formats: Printed edition:: No title; Printed edition:: No titleLOC classification:

QA76.9.B45

Online resources:

Resursa online

In: Springer Nature eBookSummary: The 4-volume set LNCS 14331, 14332, 14333, and 14334 constitutes the refereed proceedings of the 7th International Joint Conference, APWeb-WAIM 2023, which took place in Wuhan, China, in October 2023. The total of 138 papers included in the proceedings were carefully reviewed and selected from 434 submissions. They focus on innovative ideas, original research findings, case study results, and experienced insights in the areas of the World Wide Web and big data, covering Web technologies, database systems, information management, software engineering, knowledge graph, recommend system and big data.

Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)

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The 4-volume set LNCS 14331, 14332, 14333, and 14334 constitutes the refereed proceedings of the 7th International Joint Conference, APWeb-WAIM 2023, which took place in Wuhan, China, in October 2023. The total of 138 papers included in the proceedings were carefully reviewed and selected from 434 submissions. They focus on innovative ideas, original research findings, case study results, and experienced insights in the areas of the World Wide Web and big data, covering Web technologies, database systems, information management, software engineering, knowledge graph, recommend system and big data.

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