Maria Luisa Sapino

Xin Wang

Gill Dobbie

Amr El Abbadi

Zhiyong Feng

Wook-Shin Han

Yingxiao Shao

Hongzhi Yin

The four-volume set LNCS 13943, 13944, 13945 and 13946 constitutes the proceedings of the 28th International Conference on Database Systems for Advanced Applications, DASFAA 2023, held in April 2023 in Tianjin, China. The total of 125 full papers, along with 66 short papers, are presented together in this four-volume set was carefully reviewed and selected from 652 submissions. Additionally, 15 industrial papers, 15 demo papers and 4 PhD consortium papers are included. The conference presents papers on subjects such as model, graph, learning, performance, knowledge, time, recommendation, representation, attention, prediction, and network.

28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

Database Systems for Advanced Applications

Jakub Lokoč

Tomáš Skopal

Ilaria Bartolini

Fabrizio Falchi

Marco Patella

This book constitutes the refereed proceedings of the 15th International Conference on Similarity Search and Applications, SISAP 2022, held in Bologna, Italy in October 2022. SISAP 2022 is an annual international conference for researchers focusing on similarity search challenges and related theoretical/practical problems, as well as the design of content-based similarity search applications. The 15 full papers presented together with 8 short and 2 doctoral symposium papers were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: Applications; Foundations; Indexing and Clustering; Learning; Doctoral Symposium.

15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings

Similarity Search and Applications

28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV

K. Selçuk Candan

Multimedia data require specialised management techniques because the representations of colour, time, semantic concepts, and other underlying information can be drastically different from one another. This textbook on multimedia data management techniques gives a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as colour, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the 'semantic gap' and present the applications of these to emerging topics, including web and social networking.

Data Management for Multimedia Retrieval

Maria Luisa Sapino

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