Index Advisor tools settle for sub-optimal index configurations based on greedy heuristics, owing to the computational hardness of index selection. We investigate here how this limitation can be addressed by leveraging the computing power offered by ...

As the usage of knowledge graphs (KGs) becomes more pervasive in practical applications, there is a burgeoning need for high-quality data. The SHApes Constraint Language (SHACL) allows for expressing certain types of quality constraints that define sub-...

With a single Bloom filter, one can approximately answer set membership queries within a space budget. Practical systems often use collections of Bloom filters to facilitate applications such as data skipping, sideways information passing, and network ...

The schemalessness, one of the major advantages of JSON representation format, comes with high penalties in querying and operations by denying various critical functions such as query optimizations, indexing, or data verification. There have been ...

Potential harms from the under-representation of minorities in data, particularly in multi-modal settings, is a well-recognized concern. While there has been extensive effort in detecting such under-representation, resolution has remained a challenge.

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Data Lakes deployed in the cloud are a go-to solution for enterprise data storage. While the pay-as-you-go cost model allows flexible resource allocation and billing, it mandates an efficient use of resources like CPU hours, network traffic, and used ...

A B-Tree is the most widely used range index for larger-than-memory data systems. It organizes data in pages (usually 4 KB) that efficiently align with disk IO operations, fully utilizing each IO operation to narrow down the search space. On the other ...

Index is an important component in database systems. Learned indexes have been shown to outperform traditional tree-based index structures for fixed-sized integer or floating point keys. However, the application of the learned solution to variable-length ...

Public transportation plays a vital role in mitigating traffic congestion and reducing carbon emissions. The Top-k Nearest Neighbor (kNN) search in public transportation networks is a fundamental problem in location-based services, which aims to find k ...

Efficient data discovery is crucial in the era of data-driven decisionmaking. However, current practices face significant challenges due to the intricacies of identifying datasets with specific distributional characteristics, such as percentiles, when ...

Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing on graphs is gaining ...

Distributed in-memory processing frameworks accelerate application runs by caching important datasets in memory. Allocating a suitable cluster configuration for caching these datasets plays a crucial role in achieving minimal cost. We present Agile-ant, ...

Although clustering methods have shown promising performance in various applications, they cannot effectively handle incomplete data. Existing studies often impute missing values first before clustering analysis and conduct these two processes ...

Large language models (LLMs) demonstrate an impressive ability to internalize knowledge and answer natural language questions. Although previous studies validate that LLMs perform well on general knowledge while presenting poor performance on long-tail ...

Efficient clustering algorithms, such as k-Means, are often used in practice because they scale well for large datasets. However, they are only able to detect simple data characteristics. Ensemble clustering can overcome this limitation by combining ...

There is significant interest in examining large datasets using complex domain-specific queries. In many cases, these queries can be accelerated using specialized indexes. Unfortunately, the development of a practical index is difficult, because ...

Many real-world binary relations can be modelled as bipartite graphs, which can be inherently temporal and each edge is associated with a timestamp. The (α, β)-core, a popular structure that requires minimum degrees over two layers of vertices, is useful ...

Approximate Nearest Neighbor Search (ANNS) is a fundamental and critical component in many applications, including recommendation systems and large language model-based applications. With the advancement of multimodal neural models, which transform data ...

The task of finding a user's utility function (representing the user's preference) by asking them to compare pairs of points through a series of questions, each requiring him/her to compare 2 points for choosing a more preferred one, to find the best ...

Query plan is widely used as input in machine learning for databases (ML4DB) research, with query plan representation as a critical step. However, existing studies typically focus on one task, and propose a novel design to represent query plans along ...