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

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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

Chat with Graph Search

Building Efficient Query Engines in a High-Level Language

Adaptive Query Processing on RAW Data

Sharing Data and Work Across Concurrent Analytical Queries

Improving Main-memory Database System Performance through Cooperative Multitasking

Timely and cost-efficient data exploration through adaptive tuning

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

NoDB in Action: Adaptive Query Processing on Raw Data

Jet: An Embedded DSL for High Performance Big Data Processing