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A Survey on Modern Deep Learning Techniques for Traffic Volume Prediction

Zixin Qin; Mengxiang Wang; Huaijie Zhu dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Traffic volume prediction plays a crucial role in various aspects of transportation management and urban planning, such as traffic light control, traffic flow regulation. Research in this area dates back to the 1970s, mainly exploring statistical models and traditional machine learning…

Efficient Label-Constrained Path Queries on Time-Dependent Graphs via LP-Tree Decomposition

Yishu Wang; Jinlong Chu; Ye Yuan dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Shortest path queries are among the most fundamental operations in graph processing. Prior studies have extensively examined label-constrained queries on static graphs with fixed edge weights. However, real-world networks exhibit temporal dynamics and often contain complex heterogeneous…

On-Demand Augmentation for Long-Tail Data Through Subset- and Topic-Driven Sparsity Identification Framework

Kun Wu; Yaxi Hou; Shan Yang dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Large-scale, multi-dimensional mixed datasets are characterized by the pervasive "long-tail distribution." This phenomenon results in data sparsity in subspaces defined by multi-dimensional attribute combinations. This sparsity severely hinders data-driven analysis and insights. Existing…

Dynamic Region Co-location Pattern Discovery: An Efficient Approach Based on Generalized Maximal Row Instances

Qian Zou; Lizhen Wang; Peizhong Yang dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Regional co-location pattern mining is used to discover co-location patterns that are not prevalent globally but are prevalent in local regions. Although several regional co-location pattern mining methods have been developed, they have the following limitations: (1) they cannot capture…

AdamMAD: Improved Second-Moment of Adam for Enhancing Stability and Convergence

Weiwei Cheng; Bin Wang; Jian Li dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract In deep learning, the choice of optimizer plays an important role in the training efficiency and final performance of models. Adam is widely used because of its good convergence and stability. However, Adam may exhibit oscillations and fluctuations in the training when the gradient varies…

IBformer: Inductive Bias is Necessary for Multivariate Time Series Forecasting

Haoyuan Shi; Xiangfu Meng; Yongqin Zhang dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Transformers use a powerful self-attention mechanism to model remote dependency capabilities, showing great potential in various long-term time series prediction tasks. However, the original Transformer was not proposed for the time series prediction tasks and did not make any assumptions…

Limit Order Book Event Stream Prediction with Diffusion Model

Zetao Zheng; Guoan Li; Decui Liang dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Limit order book (LOB) is a dynamic, event-driven system that records real-time market demand and supply for a financial asset in a stream of data. Event stream prediction in LOB refers to forecasting both the timing and the type of events. The challenge lies in modeling the time-event…

WALDEN: Workload-Aware Learned Tree Index

Hongtao Sun; Chen Jason Zhang; Xinyu Zhang dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract The rapid development of learned indexes brings a surge in the indexing performance of database management systems. It treats an index as a learned model and learns a mapping between keys and their values’ positions in the database. Existing works mainly focus on improving the regression…

A Survey of Generative Techniques for Spatial-Temporal Data Mining

Qianru Zhang; Haixin Wang; Honggang Wen dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract With the continued digitization of social and industrial processes, diverse and increasingly massive volumes of spatial-temporal data are being accumulated， which enable the use of data mining to fuel important applications. Thus, neural network-based techniques have been developed to…

Enumerating Cliques on k-Partite Graphs

Faming Li; Shengli Qiu; Baoling Ning dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract We study the clique problem on partite graphs. As a special type of clique, cliques on k-partite graphs have extensive applications in areas such as gene data analysis, data classification, protein function determination, recommendation systems, and social network analysis. However…

Unsupervised Anomaly Detection on Attributed Multiplex Heterogeneous Networks

Yuehang Cao; Kai Wang; Xuan Wei dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract The network data model can effectively capture relationships between entities, making it useful for applications such as social networks and e-commerce systems. Anomaly detection, a critical task in network analysis, focuses on identifying unusual patterns that deviate from expected…

Fine-Grained Disentanglement for Alleviating Inconsistencies in Cross-Modal Hashing Retrieval

Tieying Li; Xiaochun Yang; Bin Wang dkk.

Data Science and Engineering, Vol. 11 No. 3 · 2026 · Inggris · DOAJ Scopus

Abstract Cross-modal hashing retrieval faces fundamental challenges from modality-modality (M-M) and modality-label (M-L) inconsistencies inherent in multimodal data. Existing methods rely on coarse-grained disentanglement to address these inconsistencies, but suffer from inaccurate semantic…

Evaluating Regulatory Compliance in Maturity Models for Patient-Centred Health Data Sharing: A Literature Review

Martin Breidenbach; Sandra Geisler

Data Science and Engineering, Vol. 11 No. 2 · 2026 · Inggris · DOAJ Scopus

Abstract The European Health Data Space (EHDS) aims to enable secure, standardized, and cross-border use of health data for both care and research. While maturity models (MMs) are widely applied to support digital transformation in healthcare, their alignment with EHDS-specific legal and strategic…

Data Platforms for Multi-organizational Settings: A Systematic Literature Review with Use Cases and a Reference Architecture

Ali Imran Jehangiri; Ulf Lotzmann; Philipp Räder dkk.

Data Science and Engineering, Vol. 11 No. 2 · 2026 · Inggris · DOAJ Scopus

Abstract Data has become a key asset in today’s digital economies. To handle vast amounts of diverse data with distinct ownership from different contexts and organizational settings, data platforms—along with the concept of data ecosystems—have recently become a research field of high scholarly…

Special Issue Editorial on “Data Spaces and Data Governance”

Nicola Bena; Salvatore Distefano; Luigi Romano dkk.

Data Science and Engineering, Vol. 11 No. 2 · 2026 · Inggris · DOAJ Scopus

Relationship-based Access Control for Data Spaces

Nikos Fotiou; Chalima Dimitra Nassar Kyriakidou; Athanasia Maria Papathanasiou dkk.

Data Science and Engineering, Vol. 11 No. 2 · 2026 · Inggris · DOAJ Scopus

Abstract Data spaces are an emerging concept with significant potential to enable a data-centric economy by fostering seamless and secure data sharing across diverse stakeholders. These environments are designed to unlock the value of data by ensuring interoperability and collaboration, which are…

BlockSketch: A Hybrid Tree-Based Sketch for Keyword Search in Blockchain Systems

Yushi Liu; Xiaodong Qi; Zhao Zhang dkk.

Data Science and Engineering, Vol. 11 No. 1 · 2026 · Inggris · DOAJ Scopus

Abstract Keyword search, which identifies transactions associated with specified keywords across historical blocks, is a critical query type in blockchain analytics. However, existing approaches, such as on-chain indexing and off-chain synchronization, may lead to significant space overhead or…

Beyond Shuffling: PAC-Tree for Efficient Distributed Joins via Hierarchical Data Placement

Pengju Liu; Cuiping Li; Hong Chen

Data Science and Engineering, Vol. 11 No. 1 · 2026 · Inggris · DOAJ Scopus

Abstract Efficiently execution of join operations over large-scale distributed data is critical in modern analytical engines, yet costly shuffle operations involving extensive network transfer and remote I/O often hinder performance. Existing data layout techniques struggle with complex multi-table…

Generating Counterfactual Temporal Motifs: Unraveling the Mysteries of Temporal Graph Neural Networks

Yibowen Zhao; Yonghui Xu; Ning Liu dkk.

Data Science and Engineering, Vol. 11 No. 1 · 2026 · Inggris · DOAJ Scopus

Abstract Temporal Graph Neural Networks (TGNNs) are increasingly applied in dynamic scenarios, however, their limited explainability hinders their adoption in high-stakes domains. Existing methods tend to conflate causality with temporal proximity, leading to ambiguous explanations that mix…

Real-Time Dynamic Response Identification for Highway Structural Health Monitoring Data

Zhixin Qi; Xin Su; Yulin Wang dkk.

Data Science and Engineering, Vol. 11 No. 1 · 2026 · Inggris · DOAJ Scopus

Abstract The high sampling frequency of highway structural health monitoring systems brings a heavy burden on data storage. However, existing dynamic response identification approaches can guarantee either reduced data volume after identification or high accuracy of dynamic response identification…