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05/08/2023
Parallel External Sorting of ASCII Records Using Learned Models
External sorting is at the core of many operations in large-scale databa...
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12/11/2022
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Cardinality estimation is one of the most fundamental and challenging pr...
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05/11/2022
LSI: A Learned Secondary Index Structure
Learned index structures have been shown to achieve favorable lookup per...
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11/29/2021
Bounding the Last Mile: Efficient Learned String Indexing
We introduce the RadixStringSpline (RSS) learned index structure for eff...
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11/16/2021
The Case for Learned In-Memory Joins
In-memory join is an essential operator in any database engine. It has b...
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08/11/2021
Towards Practical Learned Indexing
Latest research proposes to replace existing index structures with learn...
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07/05/2021
Defeating duplicates: A re-design of the LearnedSort algorithm
LearnedSort is a novel sorting algorithm that, unlike traditional method...
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07/03/2021
When Are Learned Models Better Than Hash Functions?
In this work, we aim to study when learned models are better hash functi...
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05/18/2021
LEA: A Learned Encoding Advisor for Column Stores
Data warehouses organize data in a columnar format to enable faster scan...
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03/26/2021
SkyQuery: An Aerial Drone Video Sensing Platform
Video-based sensing from aerial drones, especially small multirotor dron...
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03/24/2021
TagMe: GPS-Assisted Automatic Object Annotation in Videos
Training high-accuracy object detection models requires large and divers...
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01/13/2021
Flow-Loss: Learning Cardinality Estimates That Matter
Previous approaches to learned cardinality estimation have focused on im...
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12/12/2020
Cortex: Harnessing Correlations to Boost Query Performance
Databases employ indexes to filter out irrelevant records, which reduces...
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07/21/2020
DBOS: A Proposal for a Data-Centric Operating System
Current operating systems are complex systems that were designed before ...
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06/23/2020
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Filtering data based on predicates is one of the most fundamental operat...
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06/23/2020
Benchmarking Learned Indexes
Recent advancements in learned index structures propose replacing existi...
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06/05/2020
MISIM: An End-to-End Neural Code Similarity System
Code similarity systems are integral to a range of applications from cod...
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06/05/2020
Partitioned Learned Bloom Filter
Bloom filters are space-efficient probabilistic data structures that are...
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05/19/2020
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling
Capturing and processing video is increasingly common as cameras become ...
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05/06/2020
Fast Mapping onto Census Blocks
Pandemic measures such as social distancing and contact tracing can be e...
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04/30/2020
RadixSpline: A Single-Pass Learned Index
Recent research has shown that learned models can outperform state-of-th...
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04/28/2020
Learned Garbage Collection
Several programming languages use garbage collectors (GCs) to automatica...
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04/08/2020
Bao: Learning to Steer Query Optimizers
Query optimization remains one of the most challenging problems in data ...
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03/24/2020
Context-Aware Parse Trees
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art c...
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03/21/2020
ARDA: Automatic Relational Data Augmentation for Machine Learning
Automatic machine learning () is a family of techniques to automate the ...
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12/03/2019
Learning Multi-dimensional Indexes
Scanning and filtering over multi-dimensional tables are key operations ...
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11/29/2019
SOSD: A Benchmark for Learned Indexes
A groundswell of recent work has focused on improving data management sy...
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10/10/2019
LISA: Towards Learned DNA Sequence Search
Next-generation sequencing (NGS) technologies have enabled affordable se...
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05/25/2019
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Correctly detecting the semantic type of data columns is crucial for dat...
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05/12/2019
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Researchers currently rely on ad hoc datasets to train automated visuali...
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04/07/2019
Neo: A Learned Query Optimizer
Query optimization is one of the most challenging problems in database s...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/21/2019
How I Learned to Stop Worrying and Love Re-optimization
Cost-based query optimizers remain one of the most important components ...
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01/19/2019
Custodes: Auditable Hypothesis Testing
We present Custodes: a new approach to solving the complex issue of prev...
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11/29/2018
Chiller: Contention-centric Transaction Execution and Data Partitioning for Fast Networks
Distributed transactions on high-overhead TCP/IP-based networks were con...
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11/01/2018
VizRec: A framework for secure data exploration via visual representation
Visual representations of data (visualizations) are tools of great impor...
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08/24/2018
Unknown Examples & Machine Learning Model Generalization
Over the past decades, researchers and ML practitioners have come up wit...
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08/14/2018
VizML: A Machine Learning Approach to Visualization Recommendation
Data visualization should be accessible for all analysts with data, not ...
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07/16/2018
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
As machine learning systems become democratized, it becomes increasingly...
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07/16/2018
Slice Finder: Automated Data Slicing for Model Validation
As machine learning (ML) systems become democratized, it becomes increas...
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07/16/2018
Slice Finder: Automated Data Sclicing for Model Validation
As machine learning (ML) systems become democratized, it becomes increas...
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06/10/2018
Smallify: Learning Network Size while Training
As neural networks become widely deployed in different applications and ...
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04/07/2018
IDEBench: A Benchmark for Interactive Data Exploration
Existing benchmarks for analytical database systems such as TPC-DS and T...
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01/30/2018
A-Tree: A Bounded Approximate Index Structure
Index structures are one of the most important tools that DBAs leverage ...
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01/13/2018
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Going deeper and wider in neural architectures improves the accuracy, wh...
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12/04/2017