Accelerating Aggregation Queries on Unstructured Streams of Data

TL;DR

InQuest is a system that accelerates aggregation queries over unstructured data streams by using approximation models and sampling, providing real-time results with statistical guarantees and reducing reliance on expensive high-precision models.

Contribution

It introduces InQuest, a novel streaming system that efficiently answers aggregation queries on multi-modal data streams with theoretical error guarantees.

Findings

Achieves same RMSE as baselines with 5x fewer oracle calls

Provides up to 1.9x lower RMSE than batch algorithms at fixed oracle budget

Demonstrates effectiveness on six real-world datasets

Abstract

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 by querying the live feed from a traffic camera. Prior work has used deep neural networks (DNNs) to answer such queries in the batch setting. However, much of this work is not suited for the streaming setting because it requires access to the entire dataset before a query can be submitted or is specific to video. Thus, to the best of our knowledge, no prior work addresses the problem of efficiently answering queries over multiple modalities of streams. In this work we propose InQuest, a system for accelerating aggregation queries on unstructured streams of data with statistical guarantees on query accuracy. InQuest leverages inexpensive approximation models ("proxies") and sampling techniques to limit the execution of an expensive high-precision model (an "oracle") to a subset of the stream. It then uses the oracle predictions to compute an approximate query answer in real-time. We theoretically analyzed InQuest and show that the expected error of its query estimates converges on stationary streams at a rate inversely proportional to the oracle budget. We evaluated our algorithm on six real-world video and text datasets and show that InQuest achieves the same root mean squared error (RMSE) as two streaming baselines with up to 5.0x fewer oracle invocations. We further show that InQuest can achieve up to 1.9x lower RMSE at a fixed number of oracle invocations than a state-of-the-art batch setting algorithm.