Retrieval-Augmented Generation (RAG) is often used with Large Language Models (LLMs) to infuse domain knowledge or user-specific information. In RAG, given a user query, a retriever extracts chunks of relevant text from a knowledge base. These chunks are sent to an LLM as part of the input prompt. Typically, any given chunk is repeatedly retrieved across user questions. However, currently, for every question, attention layers in LLMs fully compute the Keys and Values (KVs) repeatedly for the input chunks, as state-of-the-art methods cannot reuse KV-caches when chunks appear at arbitrary locations or with arbitrary contexts. Naive reuse leads to output quality degradation. This leads to potentially redundant computations on expensive GPUs and increases latency. In this work, we propose Cache-Craft , a system for managing and reusing precomputed KVs corresponding to the text chunks (which we call chunk-caches ) in RAG-based systems. We present how to identify chunk-caches that are reusable, how to efficiently perform a small fraction of recomputation to fix the cache and maintain output quality, and how to efficiently store and evict chunk-caches in the hardware for maximizing reuse while masking any overheads. With real production workloads as well as synthetic datasets, we show that Cache-Craft reduces redundant computation by 51% over SOTA prefix-caching and 75% over full recomputation. Additionally, with continuous batching on a real production workload, we get a 1.6× speed up in throughput for both the LLama-3-8B and 70B models and a 2.1× and 2× reduction in end-to-end response latency respectively, compared to prefix-caching, while maintaining generation quality.

Interactive visualization interfaces enable users to efficiently explore, analyze, and make sense of their datasets. However, as data grows in size, it becomes increasingly challenging to build data interfaces that meet the interface designer's desired latency expectations and resource constraints. Cloud DBMSs, while optimized for scalability, often fail to meet latency expectations, necessitating complex, bespoke query execution and optimization techniques for data interfaces. This involves manually navigating a huge optimization space that is sensitive to interface design and resource constraints, such as client vs server data and compute placement, choosing which computations are done offline vs online, and selecting from a large library of visualization-optimized data structures. This paper advocates for a Physical Visualization Design (PVD) tool that decouples interface design from system design to provide design independence. Given an interfaces underlying data flow, interactions with latency expectations, and resource constraints, PVD checks if the interface is feasible and, if so, proposes and instantiates a middleware architecture spanning the client, server, and cloud DBMS that meets the expectations. To this end, this paper presents Jade, the first prototype PVD tool that enables design independence. Jade proposes an intermediate representation called Diffplans to represent the data flows, develops cost estimation models that trade off between latency guarantees and plan feasibility, and implements an optimization framework to search for the middleware architecture that meets the guarantees. We evaluate Jade on six representative data interfaces as compared to Mosaic and Azure SQL database. We find Jade supports a wider range of interfaces, makes better use of available resources, and can meet a wider range of data, latency, and resource conditions.

What-if queries are the building blocks for many explanation and analytics applications—sensitivity analysis, hypothetical reasoning, data cleaning, probabilistic databases—that explore how a query's output changes due to input data changes. Their response time is bounded by intervention evaluation latency, which can be in the minute or hours for complex queries and large datasets. FaDE is a compilation engine that uses provenance to evaluate hypothetical deletion and scaling interventions at low latency and high throughput. FaDE forgoes conventional provenance representations as symbolic expressions and leverages their underlying relational structure. This accelerates intervention evaluation on average by 1000× against IVM and 10,000× against prior provenance-based approaches. In addition, FaDE develops a suite of optimizations (e.g., compilation, parallelization, incremental evaluation, sparse representations) that collectively raise evaluation throughput to >1 million interventions per sec—a rate that can brute-force existing applications within 1 s.

Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for these datasets. In this work, we focus on identifying and reducing redundancy in enterprise data lakes by addressing the problem of "dataset containment". To the best of our knowledge, this is one of the first works that addresses table-level containment at a large scale. We propose R2D2: a three-step hierarchical pipeline that efficiently identifies almost all instances of containment by progressively reducing the search space in the data lake. It first builds (i) a schema containment graph, followed by (ii) statistical min-max pruning, and finally, (iii) content level pruning. We further propose minimizing the total storage and access costs by optimally identifying redundant datasets that can be deleted (and reconstructed on demand) while respecting latency constraints. We implement our system on Azure Databricks clusters using Apache Spark for enterprise data stored in ADLS Gen2, and on AWS clusters for open-source data. In contrast to existing modified baselines that are inaccurate or take several days to run, our pipeline can process an enterprise customer data lake at the TB scale in approximately 5 hours with high accuracy. We present theoretical results as well as extensive empirical validation on both enterprise (scale of TBs) and open-source datasets (scale of MBs - GBs), which showcase the effectiveness of our pipeline.

State-of-the-art video database management systems (VDBMSs) often use lightweight proxy models to accelerate object retrieval and aggregate queries. The key assumption underlying these systems is that the proxy model is an order of magnitude faster than the heavyweight oracle model. However, recent advances in computer vision have invalidated this assumption. Inference time of recently proposed oracle models is on par with or even lower than the proxy models used in state-of-the-art (SoTA) VDBMSs. This paper presents Seiden, a VDBMS that leverages this radical shift in the runtime gap between the oracle and proxy models. Instead of relying on a proxy model, Seiden directly applies the oracle model over a subset of frames to build a query-agnostic index, and samples additional frames to answer the query using an exploration-exploitation scheme during query processing. By leveraging the temporal continuity of the video and the output of the oracle model on the sampled frames, Seiden delivers faster query processing and better query accuracy than SoTA VDBMSs. Our empirical evaluation shows that Seiden is on average 6.6 x faster than SoTA VDBMSs across diverse queries and datasets.

There are various scenarios where very quick insights from a massive amount of data need to be extracted in a time-critical manner. These might be fresh insights or re-looking at why previous insights did not work and how to fix those. A marketing campaign is one real-world scenario where a non-programmer needs to dig such huge data in a very short period of time (a few hours) in order to hit a target revenue. In this demo paper, we will describe Electra - a system that integrates an automated data-insight discovery mechanism with a novel machine-learning (ML) driven approximate query processing (AQP) engine that can answer complex queries with a large number of predicates or conditions with high accuracy. This AQP engine uses a conditional generative model to generate a very small sample (~1000 rows) corresponding to the actual query to be answered and computes the highly accurate approximate answer from those instead of running the query against the original data. The insight discovery workflow bootstraps insights using ML algorithms based on the statistical characteristics of the data and further offers a no-code based interface to drill down for deeper insights. The queries from this interface are answered by the AQP engine that runs locally at the client-side itself to offer low latency interactions.

The need for improved segmentation, targeting, personalization fuel the practice of data sharing among companies. Concurrently, data sharing faces the headwind of new laws emphasizing users' privacy in data. Under the premise that sharing of data occurs from a provider to a recipient, we propose a practicable approach of generating representational data for sharing that achieves value-addition for the recipient's tasks while preserving privacy of users. Prior art shows that the mechanism to improve value-addition inevitably weakens privacy in the generated data. In a first of a kind contribution, our system offers tunable controls to adjust the extent of privacy desired by the provider and the extent of value-addition expected by the recipient. Our experiments on a public data show that under common organizational practice of data-sharing, data generation for value-addition is achievable while preserving privacy. Our demonstration starkly shows the trade-off between privacy-protection and value addition, through user-controlled knobs and offers a prototype of a platform for data sharing which is mindful of this trade-off.