The VLDB Journal — The International Journal on Very Large Data Bases

Modern machine learning (ML) systems commonly use stochastic gradient descent (SGD) to train ML models. However, SGD relies on random data order to converge, which usually requires a full data shuffle. For in-DB ML systems and deep learning ...$$$$$$$$

Most real-world data come with explicitly defined domain orders, e.g., lexicographic for strings. Our goal is to discover implicit domain orders that we do not already know, e.g., that the order of months in the Chinese Lunar calendar is Corner$\prec$...$$

Data scientists often develop data sets for analysis by drawing upon available data sources. A major challenge is ensuring that the data set used for analysis adequately represents relevant demographic groups or other variables. Whether data is ...

In recent studies, machine learning techniques have been employed to support or enhance cost-based query optimizers in DBMS. Although these approaches have shown superiority in certain benchmarks, they also suffer from certain drawbacks. These ...$\mathrm{}$$\mathrm{}$$\mathrm{}$

We study point-to-point distance estimation in hypergraphs, where the query is parameterized by a positive integer s, which defines the required level of overlap for two hyperedges to be considered adjacent. To answer s-distance queries, we first ...

In an encrypted database, all data items stored at the server are encrypted and some operations can be performed directly over ciphertexts. Most existing encrypted database schemes cannot support data interoperability, that is, it cannot handle ...${}^{\mathrm{}}$$\mathrm{}$${\mathrm{}}^{\mathrm{}}$${\mathrm{}}^{\mathrm{}}$$\mathrm{}$$$${\mathrm{}}^{\mathrm{}}$$$

The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done ...

Bipartite graphs are naturally used to model relationships between two types of entities, such as people-location, user-post, and investor-stock. When modeling real-world applications like disease outbreaks, edges are often enriched with temporal ...

The densest subgraph problem (DSP) is of great significance due to its wide applications in different domains. Meanwhile, diverse requirements in various applications lead to different density variants for DSP. Unfortunately, existing DSP ...$\frac{\mathrm{}}{\mathrm{}}$

Butterfly (a cyclic graph motif) counting is a fundamental task with many applications in graph analysis, which aims at computing the number of butterflies in a large graph. With the rapid growth of graph data, it is more and more challenging to ...$\frac{{}^{\mathrm{}}}{\mathit{}}$$\frac{{}^{\mathrm{}}}{\mathit{}}\frac{}{\sqrt{}}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$${\mathrm{}}^{\mathrm{}}$

To provide applications with the ability to analyze fresh data and eliminate the time-consuming ETL workflow, hybrid transactional and analytical (HTAP) systems have been developed to serve online transaction processing and online analytical ...

In designing a distributed RDF system, it is quite common to divide an RDF graph into subgraphs, called partitions, which are then distributed. Graph partitioning in general and RDF graph partitioning in particular are challenging problems. In ...

When dealing with large bipartite graphs, butterfly counting is a crucial and time-consuming operation. Graphics processing units (GPUs) are widely used parallel heterogeneous devices that can significantly boost performance for data science ...$$

Approximating a series of timestamped data points through a sequence of line segments with a maximum error guarantee is a fundamental data compression problem, termed as Piecewise Linear Approximation (PLA). As the demand for analyzing large ...

There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. ...

This paper explores how to build a shapelet-based time series classification (TSC) model in the federated learning (FL) scenario, that is, using more data from multiple owners without actually sharing the data. We propose FedST, a novel federated ...

Modern cloud-native OLAP databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. ...$$$$$$

Entity Resolution identifies entity profiles that represent the same real-world object. A brute-force approach that considers all pairs of entities suffers from quadratic time complexity. To ameliorate this issue, filtering techniques reduce the ...

Finding top-k items in data streams is a fundamental problem in data mining. Unbiased estimation is well acknowledged as an elegant and important property for top-k algorithms. In this paper, we propose a novel sketch algorithm, called ...$\mathrm{}$${\mathrm{}}^{\mathrm{}}$

Active learning for graph neural networks (GNNs) aims to select B nodes to label for the best possible GNN performance. Carefully selected labeled nodes can help improve GNN performance and hence motivates a line of research works. Unfortunately, ...$\mathrm{}\mathrm{}$$\mathrm{}\mathrm{}$

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. Recent deep learning based forecasting methods show strong capabilities ...