No abstract available.
Proceeding Downloads
RUSLI: Real-time Updatable Spline Learned Index
Machine learning algorithms have accelerated data access through ‘learned index’, where a set of data items is indexed by a model learned on the pairs of data key and the corresponding record’s position in the memory. Most of the learned indexes require ...
A Tailored Regression for Learned Indexes: Logarithmic Error Regression
Although linear regressions are essential for learned index structures, most implementations use Simple Linear Regression, which optimizes the squared error. Since learned indexes use exponential search, regressions that optimize the logarithmic error ...
Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning
The ability to find a set of records in Exploratory Data Analysis (EDA) hinges on the scattering of objects in the data set and the on users’ knowledge of data and their ability to express their needs. This yields a wide range of EDA scenarios and ...
Pre-Trained Web Table Embeddings for Table Discovery
Pre-trained word embedding models have become the de-facto standard to model text in state-of-the-art analysis tools and frameworks. However, while there are massive amounts of textual data stored in tables, word embedding models are usually pre-trained ...
LEA: A Learned Encoding Advisor for Column Stores
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 a ...
Leveraging Approximate Constraints for Localized Data Error Detection
Error detection is key for data quality management. AI techniques can leverage user domain knowledge to identifying sets of erroneous records that conflict with domain knowledge. To represent a wide range of user domain knowledge, several recent papers ...