O
Oscar Moll
Researcher at Massachusetts Institute of Technology
Publications - 7
Citations - 166
Oscar Moll is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Database tuning & Query plan. The author has an hindex of 4, co-authored 6 publications receiving 130 citations.
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Journal ArticleDOI
Voodoo - a vector algebra for portable database performance on modern hardware
TL;DR: This work uses Voodoo, a declarative intermediate algebra that abstracts the detailed architectural properties of the hardware, such as multi- or many-core architectures, caches and SIMD registers, without losing the ability to generate highly tuned code, to build an alternative backend for MonetDB, a popular open-source in-memory database.
Book ChapterDOI
Amalgamated Lock-Elision
TL;DR: The key idea in ALE is to use a sequence of fine-grained locks in the fall back-path to detect conflicts with the fast-path, and at the same time reduce the costs of these locks by executing the fallback-path as a series segments, where each segment is a dynamic length short hardware transaction.
Posted Content
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling.
TL;DR: ExSample is introduced, a low cost framework for object search over unindexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed.
Journal ArticleDOI
Exploring big volume sensor data with vroom
TL;DR: Vroom, a system for ad-hoc queries over AV sensor databases, combines domain specific properties of AV datasets with selective indexing and multi-query optimization to address challenges posed by AV sensor data.
Journal ArticleDOI
Vaas: video analytics at scale
TL;DR: Vas incorporates recent work in approximate video query processing to support the fast, interactive execution of queries, and accelerates the annotation process of hand-labeling examples to train models by allowing users to annotate over the outputs of previously expressed queries rather than the entire video dataset.