5 citations · 23 across the 8 of their papers we have counts for
8 papers
Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques
Jiexing Li, Arnd Christian König, Vivek Narasayya +1
The ability to estimate resource consumption of SQL queries is crucial for a number of tasks in a database system such as admission control, query scheduling and costing during que…
b-Bit Minwise Hashing in Practice: Large-Scale Batch and Online Learning and Using GPUs for Fast Preprocessing with Simple Hash Functions
Ping Li, Anshumali Shrivastava, Arnd Christian Konig
In this paper, we study several critical issues which must be tackled before one can apply b-bit minwise hashing to the volumes of data often used industrial applications, especial…
A Statistical Approach Towards Robust Progress Estimation
Arnd Christian König, Bolin Ding, Surajit Chaudhuri +1
The need for accurate SQL progress estimation in the context of decision support administration has led to a number of techniques proposed for this task. Unfortunately, no single o…
Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW)
Ping Li, Anshumali Shrivastava, Christian Konig
We generated a dataset of 200 GB with 10^9 features, to test our recent b-bit minwise hashing algorithms for training very large-scale logistic regression and SVM. The results conf…
Accurate Estimators for Improving Minwise Hashing and b-Bit Minwise Hashing
Ping Li, Christian Konig
Minwise hashing is the standard technique in the context of search and databases for efficiently estimating set (e.g., high-dimensional 0/1 vector) similarities. Recently, b-bit mi…
b-Bit Minwise Hashing for Large-Scale Linear SVM
Ping Li, Joshua Moore, Christian Konig
In this paper, we propose to (seamlessly) integrate b-bit minwise hashing with linear SVM to substantially improve the training (and testing) efficiency using much smaller memory,…