Large language models (LLMs) are rapidly transforming the landscape of computing and daily life, demonstrating immense potential across diverse applications like natural language processing, machine translation, and code generation. This talk delves into ...

In the digital age, the widespread collection and analysis of data pose significant privacy challenges. Differential privacy (DP) has emerged as a leading framework for ensuring that information release does not compromise individual privacy. In this ...

Over the past five decades, the relational database model has proven to be a scaleable and adaptable model for querying a variety of structured data, with use cases in analytics, transactions, graphs, streaming and more. However, most of the world's data ...

The high-profile success of Deep Learning (DL) at Big Tech companies, including recent Large Language Models (LLMs) such as the GPT and Llama families, has led to high demand among Web companies, consumer app companies, enterprises, healthcare, domain ...

Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been ...

Vector databases have recently emerged as a hot topic in the field of databases, especially in industry. This is due to the widespread interest in Large Language Models (LLMs), where vector databases provide the relevant context for LLMs to produce more ...

Existing database performance anomaly datasets have the problems of comprehensiveness in anomaly types, coarse-grained root causes, and unrealistic simulation for reproducing concurrent anomalies. To address these issues, we propose a data generation ...

We demonstrate PrismX (PRAM with SSDs as Memory eXtension), a single-machine system for graph analytics. PrismX allows users to make practical use of existing PRAM algorithms without any change. To cope with the limited DRAM capacity, it employs NVMe ...

Databases employ out-of-line storage and compression strategies to manage extensive text data. However, the growth in both the size of individual data items and overall data volume has significantly increased the burden of decompression, adversely ...

This paper demonstrates Pynapple-G, an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL, that support grouping points, grouping lines, and ...

Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning ...

Vector databases have emerged as crucial tools for managing and retrieving representation embeddings of unstructured data. Given the explosive growth of data, vector data is often distributed and stored across multiple organizations. However, privacy ...

Equivalence checking for SQL queries has many real-world applications but typically requires supporting an expressive SQL language in order to be practical. We develop VeriEQL, a system that can prove and disprove equivalence of complex SQL queries. ...

Entity matching is one of the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the data or are introduced by the ...

In response to escalating concerns about data privacy in the Large Language Model (LLM) domain, we demonstrate EncChain, a pioneering solution designed to bolster data security in LLM applications. EncChain presents an all-encompassing approach to data ...

In recent years, there has been increasing interest in network diffusion models and related problems. The most popular of these are the independent cascade and linear threshold models. Much of the recent experimental work done on these models requires a ...

Coin mixing methods are widely used to enhance anonymity in cryptocurrency transactions by obfuscating the linkages between recipients and senders. Specifically, coin mixing methods combine several users' transactions into a CoinJoin transaction and ...

Top-k Publish/Subscribe (TkPS) service is widely studied in spatial database, with various cache-based methods proposed to address its efficiency challenge in top-k result maintenance. These methods require in-depth exploration of relationships between ...

Differential privacy (DP) has garnered significant attention from both academia and industry due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being ...

We demonstrate Rock, a system for cleaning relational data. Rock highlights the following unique features: (1) it extends logic rules by embedding machine learning models as predicates, to benefit from both ML and logic deduction; (2) it supports entity ...