We are pleased to present a special issue of Data Science and Engineering (DSE), which contains a collection of six extended papers from the DASFAA 2023 conference.

The International Conference on Database Systems for Advanced Applications (DASFAA) is a well-established international conference series that provides a forum for technical presentations and discussions among database researchers, developers, and users from academia, business, and industry, which showcases state-of-the-art research and development activities in the general areas of database systems, Web information systems, and their advanced applications. The conference's long history has established the event as the premier research conference in the database area.

DASFAA 2023, which is the 28th DASFAA conference, was held in Tianjin during April 17–20, 2023, and attracted a total of 652 research paper submissions. The conference program committee selected 125 full research papers (acceptance ratio of 19.2%) and 66 short papers to be presented at the conference and published in the conference proceedings [1,2,3,4]. The conference program also included keynote presentations by Sihem Amer-Yahia (CNRS, France), Kyuseok Shim (Seoul National University, South Korea), Angela Bonifati (Lyon1 University, France), and Jianliang Xu (Hong Kong Baptist University, Hong Kong, China).

The six extended papers for this special issue were selected from among all the accepted papers by the special issue guest editors Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Yingxiao Shao, and Hongzhi Yin, based on the relevance to the journal and the reviews of the conference version of the papers. The authors were asked to revise the conference paper for journal publication and in accordance with customary practice of adding more than 30% new materials. The revised papers again went through the review process in accordance with DSE guidelines and are finally presented to the readers in the present form.

The six extended papers in this special issue cover a variety of topics related to data science and engineering. In the first paper, “A Neural Inference of User Social Interest for Item Recommendation”, authors propose a neural inference method by mining user social interest for item recommendation. The second paper, “Deep Learning-Based Bloom Filter for Efficient Multi-key Membership Testing”, authors present a hybrid method that combines machine learning techniques with the Bloom filter. The third paper, “Combining Graph Contrastive Embedding and Multi-Head Cross-Attention Transfer for Cross-Domain Recommendation”, authors construct a framework that can combine graph contrastive embedding and multi-head cross-attention transfer for cross domain recommendation. The fourth paper, “Fully Dynamic Contraction Hierarchies with Label Restrictions on Road Networks”, authors investigate the maintenance problem of a novel index structure in dynamic road networks. The fifth paper, “Efficient Network Representation Learning via Cluster Similarity”, authors propose a novel method that computes the representations of the clusters from similarities between clusters to improve the efficiency and accuracy of network representation learning. The sixth paper, “Learning with Small Data: Subgraph Counting Queries”, authors study a specific problem of subgraph isomorphism counting in a paradigm of meta-learning to address the initial small training data issue in deep learning.

We hope that the readers enjoy this special issue. We would like to acknowledge the work done by all authors and their willingness to contribute their papers for this special issue. We thank all the reviewers for their expert comments and assistance in timely reviews. Finally, a note of thanks is to DSE editors in chief Bin Cui and Timos Sellis for their guidance and support in this process.