Zobrazeno 1 - 10
of 375
pro vyhledávání: '"Kemper, Alfons"'
Although database systems perform well in data access and manipulation, their relational model hinders data scientists from formulating machine learning algorithms in SQL. Nevertheless, we argue that modern database systems perform well for machine l
Externí odkaz:
http://arxiv.org/abs/2312.17355
Autor:
Pandey, Varun, van Renen, Alexander, Zacharatou, Eleni Tzirita, Kipf, Andreas, Sabek, Ibrahim, Ding, Jialin, Markl, Volker, Kemper, Alfons
Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora of sources such as billions of GPS-enabled devices (e.g., cell phones, cars, and sensors), consumer-based applications (e.g., Uber and Strava), and social med
Externí odkaz:
http://arxiv.org/abs/2309.06354
Autor:
Pandey, Varun, van Renen, Alexander, Kipf, Andreas, Sabek, Ibrahim, Ding, Jialin, Kemper, Alfons
Spatial data is ubiquitous. Massive amounts of data are generated every day from billions of GPS-enabled devices such as cell phones, cars, sensors, and various consumer-based applications such as Uber, Tinder, location-tagged posts in Facebook, Twit
Externí odkaz:
http://arxiv.org/abs/2008.10349
Autor:
Marcus, Ryan, Kipf, Andreas, van Renen, Alexander, Stoian, Mihail, Misra, Sanchit, Kemper, Alfons, Neumann, Thomas, Kraska, Tim
Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index s
Externí odkaz:
http://arxiv.org/abs/2006.12804
Autor:
Kipf, Andreas, Marcus, Ryan, van Renen, Alexander, Stoian, Mihail, Kemper, Alfons, Kraska, Tim, Neumann, Thomas
Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build.
Externí odkaz:
http://arxiv.org/abs/2004.14541
Autor:
Kipf, Andreas, Marcus, Ryan, van Renen, Alexander, Stoian, Mihail, Kemper, Alfons, Kraska, Tim, Neumann, Thomas
A groundswell of recent work has focused on improving data management systems with learned components. Specifically, work on learned index structures has proposed replacing traditional index structures, such as B-trees, with learned models. Given the
Externí odkaz:
http://arxiv.org/abs/1911.13014
Autor:
Winter, Christian, Kipf, Andreas, Anneser, Christoph, Zacharatou, Eleni Tzirita, Neumann, Thomas, Kemper, Alfons
As individual traffic and public transport in cities are changing, city authorities need to analyze urban geospatial data to improve transportation and infrastructure. To that end, they highly rely on spatial aggregation queries that extract summariz
Externí odkaz:
http://arxiv.org/abs/1908.07753
The amount of the available geospatial data grows at an ever faster pace. This leads to the constantly increasing demand for processing power and storage in order to provide data analysis in a timely manner. At the same time, a lot of geospatial proc
Externí odkaz:
http://arxiv.org/abs/1906.06085
Autor:
Kipf, Andreas, Vorona, Dimitri, Müller, Jonas, Kipf, Thomas, Radke, Bernhard, Leis, Viktor, Boncz, Peter, Neumann, Thomas, Kemper, Alfons
We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between col
Externí odkaz:
http://arxiv.org/abs/1904.08223
I/O latency and throughput is one of the major performance bottlenecks for disk-based database systems. Upcoming persistent memory (PMem) technologies, like Intel's Optane DC Persistent Memory Modules, promise to bridge the gap between NAND-based fla
Externí odkaz:
http://arxiv.org/abs/1904.01614