2.3k citations · 4.7k across the 64 of their papers we have counts for
12 papers · 1 filter
Accelerating Aggregation Queries on Unstructured Streams of Data
Matthew Russo, Tatsunori Hashimoto, Daniel Kang +2
Analysts and scientists are interested in querying streams of video, audio, and text to extract quantitative insights. For example, an urban planner may wish to measure congestion…
Zelda: Video Analytics using Vision-Language Models
Francisco Romero, Caleb Winston, Johann Hauswald +2
Advances in ML have motivated the design of video analytics systems that allow for structured queries over video datasets. However, existing systems limit query expressivity, requi…
Transactions Make Debugging Easy
Qian Li, Peter Kraft, Michael Cafarella +6
We propose TROD, a novel transaction-oriented framework for debugging modern distributed web applications and online services. Our critical insight is that if applications store al…
Apiary: A DBMS-Integrated Transactional Function-as-a-Service Framework
Peter Kraft, Qian Li, Kostis Kaffes +16
Developers increasingly use function-as-a-service (FaaS) platforms for data-centric applications that perform low-latency and transactional operations on data, such as for microser…
Finding Label and Model Errors in Perception Data With Learned Observation Assertions
Daniel Kang, Nikos Arechiga, Sudeep Pillai +2
ML is being deployed in complex, real-world scenarios where errors have impactful consequences. In these systems, thorough testing of the ML pipelines is critical. A key component…
Semantic Indexes for Machine Learning-based Queries over Unstructured Data
Daniel Kang, John Guibas, Peter Bailis +2
Unstructured data (e.g., video or text) is now commonly queried by using computationally expensive deep neural networks or human labelers to produce structured information, e.g., o…
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
Daniel Kang, Ankit Mathur, Teja Veeramacheneni +2
While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researc…
Approximate Selection with Guarantees using Proxies
Daniel Kang, Edward Gan, Peter Bailis +2
Due to the falling costs of data acquisition and storage, researchers and industry analysts often want to find all instances of rare events in large datasets. For instance, scienti…
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Peter Kraft, Daniel Kang, Deepak Narayanan +3
Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss cr…
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics
Daniel Kang, Peter Bailis, Matei Zaharia
Recent advances in neural networks (NNs) have enabled automatic querying of large volumes of video data with high accuracy. While these deep NNs can produce accurate annotations of…
NoScope: Optimizing Neural Network Queries over Video at Scale
Daniel Kang, John Emmons, Firas Abuzaid +2
Recent advances in computer vision-in the form of deep neural networks-have made it possible to query increasing volumes of video data with high accuracy. However, neural network i…
Shark: SQL and Rich Analytics at Scale
Reynold Xin, Josh Rosen, Matei Zaharia +3
Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified…