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

The proliferation of big data has brought an urgent demand for privacy-preserving data publishing. Traditional solutions to this demand have limitations on effectively balancing the trade-off between privacy and utility of the released data. To ...

We study the problem of computing similarity joins under edit distance on a set of strings. Edit similarity joins is a fundamental problem in databases, data mining and bioinformatics. It finds many applications in data cleaning and integration, ...

Recently, numerous studies have been proposed to attack the natural language interfaces to data-bases (NLIDB) problem by researchers either as a conventional pipeline-based or an end-to-end deep-learning-based solution. Although each approach has ...

Cardinality estimation is one of the most important problems in query optimization. Recently, machine learning-based techniques have been proposed to effectively estimate cardinality, which can be broadly classified into query-driven and data-...$\mathtt{}$$\mathtt{}$$\mathtt{}$

We propose techniques to evaluate regular path queries (RPQs) over labeled graphs (e.g., RDF). We apply a bit-parallel simulation of a Glushkov automaton representing the query over a ring: a compact wavelet-tree-based index of the graph. To the ...$$$$

Persistent memory (PMem) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery. Especially with the release of Intel Optane DC Persistent Memory Modules, we have ...

Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data ...

Recently, the serverless paradigm of computing has inspired research on its applicability to data-intensive tasks such as ETL, database query processing, and machine learning (ML) model training. Recent efforts have proposed multiple systems for ...

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, ...

Correlation analysis is an invaluable tool in many domains, for better understanding the data and extracting salient insights. Most works to date focus on detecting high pairwise correlations. A generalization of this problem with known ...

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a ...

Distributed transaction processing over the TCP/IP network suffers from the weak transaction scalability problem, i.e., its performance drops significantly when the number of involved data nodes per transaction increases. Although quite a few of ...