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Found 38 papers, 12 papers with code
Improving DBMS Scheduling Decisions with Fine-grained Performance Prediction on Concurrent Queries -- Extended
Unlike previous approaches, IconqSched features a novel fine-grained predictor, Iconq, which treats the DBMS as a black box and accurately estimates the system runtime of concurrently executed queries under different system states.
A Declarative System for Optimizing AI Workloads
We describe the workload of AI-powered analytics tasks, the optimization methods that Palimpzest uses, and the prototype system itself.
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases.
Extract-Transform-Load for Video Streams
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
SEED: Domain-Specific Data Curation With Large Language Models
As a result, data scientists often have to develop domain-specific solutions tailored to both the dataset and the task, e. g. writing domain-specific code or training machine learning models on a sufficient number of annotated examples.
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries.
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.
Bounding the Last Mile: Efficient Learned String Indexing
RSS achieves this by using the minimal string prefix to sufficiently distinguish the data unlike most learned approaches which index the entire string.
Towards Practical Learned Indexing
Latest research proposes to replace existing index structures with learned models.
TagMe: GPS-Assisted Automatic Object Annotation in Videos
We show TagMe can produce high-quality object annotations in a fully-automatic and low-cost way.
Partitioned Learned Bloom Filters
Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives.
Learned Indexes for a Google-scale Disk-based Database
There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees.
Cortex: Harnessing Correlations to Boost Query Performance
Unlike prior work, Cortex can adapt itself to any existing primary index, whether single or multi-dimensional, to harness a broad variety of correlations, such as those that exist between more than two attributes or have a large number of outliers.
(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.
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse.
Partitioned Learned Bloom Filter
Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives.
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.
Learning Multi-dimensional Indexes
Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines.
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.
LISA: Towards Learned DNA Sequence Search
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery.
ALEX: An Updatable Adaptive Learned Index
The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
Unknown Examples & Machine Learning Model Generalization
Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data.
VizML: A Machine Learning Approach to Visualization Recommendation
Data visualization should be accessible for all analysts with data, not just the few with technical expertise.
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models.
Smallify: Learning Network Size while Training
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy.
IDEBench: A Benchmark for Interactive Data Exploration
Existing benchmarks for analytical database systems such as TPC-DS and TPC-H are designed for static reporting scenarios.
Databases
A-Tree: A Bounded Approximate Index Structure
At the core of our index is a tunable error parameter that allows a DBA to balance lookup performance and space consumption.
Databases
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance.
The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not.
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries
The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces.
MLI: An API for Distributed Machine Learning
MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing.
