Publications associées à BigTable

Scalable Multi-Query Execution using Reinforcement Learning

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...

Association for Computing Machinery2021

FPGAs in the Datacenters: the Case of Parallel Hybrid Super Scalar String Sample Sort

Paolo Ienne, Mikhail Asiatici, Damian Maiorano

String sorting is an important part of database and MapReduce applications; however, it has not been studied as extensively as sorting of fixed-length keys. Handling variable-length keys in hardware is challenging and it is no surprise that no string sorte ...

IEEE COMPUTER SOC2020

DiNoDB: Efficient Large-Scale Raw Data Analytics

Anastasia Ailamaki, Marko Vukolic, Ioannis Alagiannis, Erietta Liarou, Yongchao Tian

Modern big data workflows, found in e.g., machine learning use cases, often involve iterations of cycles of batch analytics and interactive analytics on temporary data. Whereas batch analytics solutions for large volumes of raw data are well established (e ...

2014

Online indexing and distributed querying model-view sensor data in the cloud

Karl Aberer, Tian Guo, Hao Zhuang

As various kinds of sensors penetrate our daily life (e.g., sensor networks for environmental monitoring, GPS for localization and navigation), the efficient management of massive amount of sensor data becomes increasingly important at present. Many sensor ...

Springer-Verlag Berlin2014

Same Queries, Different Data: Can we Predict Query Performance?

Anastasia Ailamaki, Miguel Sérgio De Oliveira Branco, Adrian Daniel Popescu

We consider MapReduce workloads that are produced by analytics applications. In contrast to ad hoc query workloads, analytics applications are comprised of fixed data flows that are run over newly arriving data sets or on different portions of an existing ...

2012

Adaptive query execution for data management in the cloud

Anastasia Ailamaki, Debabrata Dash, Adrian Daniel Popescu

A major component of many cloud services is query processing on data stored in the underlying cloud cluster. The traditional techniques for query processing on a cluster are those offered by parallel DBMS. These techniques however, cannot guarantee high pe ...

ACM Press2010

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BigTable

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Scalable Multi-Query Execution using Reinforcement Learning

FPGAs in the Datacenters: the Case of Parallel Hybrid Super Scalar String Sample Sort

DiNoDB: Efficient Large-Scale Raw Data Analytics

Online indexing and distributed querying model-view sensor data in the cloud

Same Queries, Different Data: Can we Predict Query Performance?

Adaptive query execution for data management in the cloud

Graph Chatbot

Chattez avec Graph Search

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

DiNoDB: Efficient Large-Scale Raw Data Analytics

Scalable Multi-Query Execution using Reinforcement Learning

FPGAs in the Datacenters: the Case of Parallel Hybrid Super Scalar String Sample Sort

Online indexing and distributed querying model-view sensor data in the cloud

Same Queries, Different Data: Can we Predict Query Performance?

Adaptive query execution for data management in the cloud