Best Paper Award — Entidade

amplab.cs.berkeley.edu·há mais de 10 anos

CoCoA: A Framework for Distributed Optimization

A major challenge in many large-scale machine learning tasks is to solve an optimization objective involving data that is distributed across multiple machines. In this setting, optimization methods th

amplab.cs.berkeley.edu·há quase 11 anos

Succinct: Enabling Queries on Compressed Data

Web applications and services today collect, store and analyze an immense amount of data. As data sizes continue to grow, the bottlenecks in systems for big data analytics have undergone a fundamental

amplab.cs.berkeley.edu·há quase 11 anos

AMP Lab – UC Berkeley

KeystoneML is a research project exploring techniques to simplify the construction of large scale, end-to-end, machine learning pipelines. KeystoneML is designed around the principles of composabilit

amplab.cs.berkeley.edu·há mais de 11 anos

Velox: Models in Action

To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of

amplab.cs.berkeley.edu·há mais de 12 anos

GraphX: Large-Scale Graph Analytics

Increasingly, data-science applications require the creation, manipulation, and analysis of large graphs ranging from social networks to language models. While existing graph systems (e.g., GraphBuil

amplab.cs.berkeley.edu·há mais de 14 anos

DNA Processing Pipeline

Another effort related to genomics underway at the AMP Lab involves developing a variant calling pipeline. Variant calling is the process of translating the output of DNA sequencing machines, short r

amplab.cs.berkeley.edu·há mais de 14 anos

DNA Sequence Alignment with SNAP

As the cost of DNA sequencing continues to drop faster than Moore’s Law, there is a growing need for tools that can efficiently analyze larger bodies of sequence data. By mid-2013, sequencing a human

amplab.cs.berkeley.edu·há mais de 14 anos

Sparrow: Low Latency Scheduling for Interactive Cluster Services

The Sparrow project introduces a distributed cluster scheduling architecture which supports ultra-high throughput, low latency task scheduling. By supporting very low-latency tasks (and their associat