Search Results for author:
Found 15 papers, 7 papers with code
Kepler: Robust Learning for Faster Parametric Query Optimization
We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer.
LSI: A Learned Secondary Index Structure
LSI works by building a learned index over a permutation vector, which allows binary search to performed on the unsorted base data using random access.
Towards Practical Learned Indexing
Latest research proposes to replace existing index structures with learned models.
A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification
Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task.
(Updated submission 11/20/2020) MISIM: A Novel Code Similarity System
First, MISIM uses a novel context-aware semantic structure (CASS), which is designed to aid in lifting semantic meaning from code syntax.
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance.
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection.
RadixSpline: A Single-Pass Learned Index
Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance.
Context-Aware Parse Trees
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing program \emph{structure} rather than program \emph{syntax}.
ARDA: Automatic Relational Data Augmentation for Machine Learning
Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join.
Park: An Open Platform for Learning-Augmented Computer Systems
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems.
SOSD: A Benchmark for Learned Indexes
A groundswell of recent work has focused on improving data management systems with learned components.
Flexible Operator Embeddings via Deep Learning
Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way to represent the pieces of information that are relevant to a task.
Deep Reinforcement Learning for Join Order Enumeration
However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan.
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases
Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources.
Databases
