IEEE Transactions on Knowledge and Data Engineering

The problem of identifying the <italic>k</italic>-shortest paths (KSPs for short) in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph ...

In the same way as with relational systems, schema evolution is a crucial aspect of NoSQL systems. But providing approaches and tools to support NoSQL schema evolution is more challenging than for relational databases. Not only are most NoSQL systems ...

Real-world datasets are often incomplete due to data collection cost, privacy considerations or as a side effect of data integration/preparation. We focus on answering aggregate queries on such datasets, where data incompleteness causes the answers to be ...

Organizations leverage massive volumes of information and new types of data to generate unprecedented insights and improve their outcomes. Correctly identifying duplicate records that represent the same entity, such as user, customer, patient and so on, a ...

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion ...

Querying cohesive subgraphs on temporal graphs (e.g., social network, finance network, etc.) with various conditions has attracted intensive research interests recently. In this paper, we study a novel Temporal <inline-formula><tex-math notation="LaTeX">$(...

Fuzzy <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="liang-ieq2-3329821.gif"/></alternatives></inline-formula>-Means with Entropy Regularization method (ERFKM) is ...

This paper studies how to design an index structure on a collection of sets <inline-formula><tex-math notation="LaTeX">$S_{1}, S_{2},{\ldots }, S_{n}$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mn>1</mml:mn></mml:msub><...

Distributed key-value storage and computation are essential components of cloud services. As the demand for high-performance systems has increased significantly, a new architecture has been motivated to separate computing and storage nodes and connect ...

POI recommendation can facilitate various Location-Based Social Network services. Existing methods generally assume the available POI check-ins are the ground-truth depiction of user behaviors. However, in real scenarios, check-in data can be rather ...

Searching for well-connected user communities in a Location-based Social Network (LBSN) has been extensively investigated. However, very few studies focus on finding a group of locations in an LBSN which are significantly engaged with socially cohesive ...

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles. In this paper, we conduct an ...

Recent years have witnessed the growth of Graph-based Collaborative Filtering (GCF) for high-performance recommendations, but the widely adopted user-item bipartite graphs are subject to deeper layers&#x2019; over-smoothing effect and sparse user-item ...

Depression is one of the most common mental illnesses, but few of the currently proposed in-depth models based on social media data take into account both temporal and spatial information in the data for the detection of depression. In this paper, we ...

Graphs in real-world applications usually evolve constantly presenting dynamic behaviors such as social networks and transportation networks. Hence, dynamic graph embedding has gained much attention recently. In dynamic graphs, both the topology and node ...

Quantification research has sought to accurately estimate class distributions under dataset shift. While existing methods perform well under assumed conditions of shift, it is not always clear whether such assumptions will hold in a given application. ...

Given a source node <inline-formula><tex-math notation="LaTeX">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href="guo-ieq1-3349164.gif"/></alternatives></inline-formula> and a target node <inline-formula><tex-...

Frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are two important branches of itemset mining which is a key technology of knowledge discovery in many applications. Nowadays, there have been extensive algorithms on FIM and HUIM, but ...

Electronic health records (EHRs) contain vast medical information like diagnosis, medication, and procedures, enabling personalized drug recommendations and treatment adjustments. However, current drug recommendation methods only model patients&#x2019; ...

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the ...

Contrastive learning, which learns data representations by contrasting similar and dissimilar instances, has achieved great success in various domains including natural language processing (NLP). Recently, it has been demonstrated that incorporating class ...

Recent research has revealed that natural language processing (NLP) models are vulnerable to adversarial examples. However, the current techniques for generating such examples rely on deterministic heuristic rules, which fail to produce optimal ...

Relation reasoning in <bold>K</bold>nowledge <bold>G</bold>raph <bold>C</bold>ompletion (KGC) aims at predicting missing relations between entities. Recently, effective KGC methods have usually focused on exploring the path pattern between entities, such ...

Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention. Due to their powerful emergent abilities, recent LLMs are considered as a possible alternative to structured knowledge bases like knowledge graphs (KGs). ...

Calculating the number of distinct values (i.e., NDV) in a column of a big table is costly yet fundamental to a variety of database applications such as data compression and profiling. To reduce the high time and space cost, a number of sketch methods (...

Graph neural networks greatly facilitate data processing in homogeneous and heterogeneous graphs. However, training GNNs on large-scale graphs poses a significant challenge to computing resources. It is especially prominent on heterogeneous graphs, which ...

Anomaly detection in multivariate time series is a critical research area, but it is also a challenging one due to its occurrence in various real-world scenarios, such as structural health monitoring and risk management. Traditional approaches for anomaly ...

Regret minimization queries are important methods to extract representative tuples from databases. They have been extensively investigated in the last decade due to wide applications in multi-criteria decision making. For a given database <inline-formula><...

Anomaly detection in multivariate time series has been widely studied in one-class classification (OCC) setting. The training samples in this setting are assumed to be normal. In more practical situations, it is difficult to guarantee that all samples are ...

Generating synthetic workloads is essential and critical to the performance evaluation of database systems. When benchmarking database performance for a specific application, the similarity between synthetic workloads and real application workloads ...