How to Win a Hot Dog Eating Contest: Distributed Incremental View Maintenance with Batch Updates

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Publications associées (28)

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Building Efficient Query Engines in a High-Level Language

Christoph Koch, Tiark Rompf, Ioannis Klonatos

In this paper we advocate that it is time for a radical rethinking of database systems design. Developers should be able to leverage high-level programming languages without having to pay a price in efficiency. To realize our vision of abstraction without ...

2014

Adaptive Query Processing on RAW Data

Anastasia Ailamaki, Ioannis Alagiannis, Manolis Karpathiotakis, Miguel Sérgio De Oliveira Branco

Database systems deliver impressive performance for large classes of workloads as the result of decades of research into optimizing database engines. High performance, however, is achieved at the cost of versatility. In particular, database systems only op ...

2014

Sharing Data and Work Across Concurrent Analytical Queries

Anastasia Ailamaki, Manoussos Gavriil Athanassoulis, Iraklis Psaroudakis

Today's data deluge enables organizations to collect massive data, and analyze it with an ever-increasing number of concurrent queries. Traditional data warehouses (DW) face a challenging problem in executing this task, due to their query-centric model: ea ...

2013

Improving Main-memory Database System Performance through Cooperative Multitasking

Georgios Psaropoulos

Database systems access memory either sequentially or randomly. Contrary to sequential access and despite the extensive efforts of computer architects, compiler writers, and system builders, random access to data larger than the processor cache has been s ...

EPFL2019

Timely and cost-efficient data exploration through adaptive tuning

Matthaios Alexandros Olma

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

EPFL2019

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

NoDB in Action: Adaptive Query Processing on Raw Data

Anastasia Ailamaki, Ioannis Alagiannis, Renata Borovica-Gajic, Miguel Sérgio De Oliveira Branco

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare the data, to load the data into the database and to execute the desired queries. Many applications alread ...

VLDB Endowment2012

Jet: An Embedded DSL for High Performance Big Data Processing

Stefan Ackermann, Martin Odersky, Tiark Rompf, Vojin Jovanovic

Cluster computing systems today impose a trade-off between generality, performance and productivity. Hadoop and Dryad force programmers to write low level programs that are tedious to compose but easy to optimize. Systems like Dryad/LINQ and Spark allow co ...

2012