Dremel

By Sergey Melnik, Andrey Gubarev, J. Long, Geoffrey Romer, Shiva Shivakumar, M. Tolton, T. Vassilakis. Proceedings of the VLDB Endowment, 2010

645 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4543690861cb95c3d220ee53df09b7c84f10ac1f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “MapReduce [13], Dryad [42], Pregel [43], and Dremel [44] exemplify programming models.”
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (2012, background): “Finally, Dremel [22] is a low-latency query engine for large on-disk datasets that leverages a column-oriented format for nested record data.”
Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads (2012, background): “The workload perspective for MapReduce is slowly emerging, albeit in point studies that focus on technology industry use cases one at a time [8, 11, 12, 38, 41, 48].”
Shark: fast data analysis using coarse-grained distributed memory (2012, background): “Dremel, another project from Google [26], is worth mentioning as an example of a new generation of database systems that are massively distributed and run interactive queries on very large data sets.”
Goods: Organizing Google's Datasets (2016, background): “Comparing any two datasets to each other can already be expensive due to large dataset sizes, but a naïve pairwise comparison of contents of billions of datasets is infeasible.”
Hopper: Decentralized Speculation-aware Cluster Scheduling at Scale (2015, background): “CCS Concepts •Networks → Cloud computing; •Computer systems organization → Distributed architectures; Keywords speculation; decentralized scheduling; straggler; fairness”
Analyzing the download time of availability codes (2015, background): “In fact, service times in modern large scale systems are known to exhibit significant variability [12, 37], and thus, the download time at a recovery group can be significantly slower than that on a single server.”