Web and Big Data

Jiajia Li

ISBN 10: 9819557216 / ISBN 13: 9789819557219

Published by Springer Nature Singapore Mär 2026, 2026

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -The four-volume set LNCS constitutes the refereed proceedings of the 9th International Joint Conference on Web and Big Data, APWeb-WAIM 2025, held in Shenyang, China, during August 28 30, 2025.The 136 full papers and 15 short papers presented in these proceedings were carefully reviewed and selected from 472 submissions. The papers are organized in the following topical sections:Part I: Data Mining I; Machine Learning I; Information Retrieval and Knowledge Management I; Graph Data Management andAnalytics I; Complex Data Management.Part II: Complex Data Management; Spatial and Temporal Data Management; Data Privacy and Trusted AI; Data Management on New Hardwares; Query Processing and Optimization; Data Mining II.Part III: Data Mining II; Machine Learning II; Information Retrieval and Knowledge Management II; Graph Data Management andAnalytics II; Big Data Management.Part IV: Big Data Management; LLM for Data Management; Information Retrieval; Demonstration Paper; Industry Paper. 371 pp. Englisch.

Seller Inventory # 9789819557219

Bibliographic Details

• Title: Web and Big Data
• Publisher: Springer Nature Singapore Mär 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 9819557216
• ISBN 13: 9789819557219
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 22D millimeters 235 millimeters width by 155 millimeters height by 22 millimeters depth
• Weight: 593 grams

About this title

Synopsis

The four-volume set LNCS constitutes the refereed proceedings of the 9th International Joint Conference on Web and Big Data, APWeb-WAIM 2025, held in Shenyang, China, during August 28–30, 2025.

The 136 full papers and 15 short papers presented in these proceedings were carefully reviewed and selected from 472 submissions. The papers are organized in the following topical sections:

Part I: Data Mining I; Machine Learning I; Information Retrieval and Knowledge Management I; Graph Data Management andAnalytics I; Complex Data Management.

Part II: Complex Data Management; Spatial and Temporal Data Management; Data Privacy and Trusted AI; Data Management on New Hardwares; Query Processing and Optimization; Data Mining II.

Part III: Data Mining II; Machine Learning II; Information Retrieval and Knowledge Management II; Graph Data Management andAnalytics II; Big Data Management.

Part IV: Big Data Management; LLM for Data Management; Information Retrieval; Demonstration Paper;  Industry Paper.