Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Kipf, Andreas"'
Autor:
Stoian, Mihail, van Renen, Alexander, Kobiolka, Jan, Kuo, Ping-Lin, Grabocka, Josif, Kipf, Andreas
The growing adoption of data lakes for managing relational data necessitates efficient, open storage formats that provide high scan performance and competitive compression ratios. While existing formats achieve fast scans through lightweight encoding
Externí odkaz:
http://arxiv.org/abs/2410.14066
Autor:
Stoian, Mihail, Kipf, Andreas
We revisit the join ordering problem in query optimization. The standard exact algorithm, DPccp, has a worst-case running time of $O(3^n)$. This is prohibitively expensive for large queries, which are not that uncommon anymore. We develop a new algor
Externí odkaz:
http://arxiv.org/abs/2409.08013
Column encoding schemes have witnessed a spark of interest with the rise of open storage formats (like Parquet) in data lakes in modern cloud deployments. This is not surprising -- as data volume increases, it becomes more and more important to reduc
Externí odkaz:
http://arxiv.org/abs/2403.17229
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
Learned index structures have been shown to achieve favorable lookup performance and space consumption compared to their traditional counterparts such as B-trees. However, most learned index studies have focused on the primary indexing setting, where
Externí odkaz:
http://arxiv.org/abs/2205.05769
We introduce the RadixStringSpline (RSS) learned index structure for efficiently indexing strings. RSS is a tree of radix splines each indexing a fixed number of bytes. RSS approaches or exceeds the performance of traditional string indexes while usi
Externí odkaz:
http://arxiv.org/abs/2111.14905
Latest research proposes to replace existing index structures with learned models. However, current learned indexes tend to have many hyperparameters, often do not provide any error guarantees, and are expensive to build. We introduce Practical Learn
Externí odkaz:
http://arxiv.org/abs/2108.05117
In this work, we aim to study when learned models are better hash functions, particular for hash-maps. We use lightweight piece-wise linear models to replace the hash functions as they have small inference times and are sufficiently general to captur
Externí odkaz:
http://arxiv.org/abs/2107.01464
Data warehouses organize data in a columnar format to enable faster scans and better compression. Modern systems offer a variety of column encodings that can reduce storage footprint and improve query performance. Selecting a good encoding scheme for
Externí odkaz:
http://arxiv.org/abs/2105.08830
Autor:
Negi, Parimarjan, Marcus, Ryan, Kipf, Andreas, Mao, Hongzi, Tatbul, Nesime, Kraska, Tim, Alizadeh, Mohammad
Previous approaches to learned cardinality estimation have focused on improving average estimation error, but not all estimates matter equally. Since learned models inevitably make mistakes, the goal should be to improve the estimates that make the b
Externí odkaz:
http://arxiv.org/abs/2101.04964