Media Summary: The virtual conference talk for our paper: " LMFAO: An Engine for Batches of Group-By Aggregates Layered Multiple Functional Aggregate Optimization by Maximilian ... This is the video of our presentation of "LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs" at EDBT 2022.

Deepdb Learn From Data Not - Detailed Analysis & Overview

The virtual conference talk for our paper: " LMFAO: An Engine for Batches of Group-By Aggregates Layered Multiple Functional Aggregate Optimization by Maximilian ... This is the video of our presentation of "LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs" at EDBT 2022. This talk was recorded at NDC Oslo in Oslo, Norway. Attend the next ... Chapters: - 0:00:00 - Announcement - 0:01:03 - Intro - 0:09:16 - Bootstrapping the Project - 0:15:05 - Should you handle result of ... Ready for Build Stuff 2025? Grab Your Tickets to the Software Development Conference You Can't Miss ...

See more Northwest Database Society talks here: EE380: Computer Systems Colloquium Seminar Information Theory of Deep

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DeepDB: Learn from Data, not from Queries! (VLDB'20 Talk)
[VLDB 2020] Deep Unsupervised Cardinality Estimation
Data Collection and Quality Challenges for Deep Learning (VLDB 2020 Tutorial Part.01)
Practical Applications for DuckDB (with Simon Aubury & Ned Letcher)
LMFAO: An Engine for Batches of Group-By Aggregates | VLDB Demonstration 2020
LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs
Analytics for not-so-big data with DuckDB - David Ostrovsky - NDC Oslo 2025
Reverse Engineering Data Files
P115 | Deep Unsupervised Cardinality Estimation
Analytics for not so big data with DuckDB | David Ostrovsky
F-IVM: Learning over Fast-Evolving Relational Data | SIGMOD DEMO 2020
SIGMOD’20: Auto-Suggest: Learning-to-Recommend Data Preparation Steps
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DeepDB: Learn from Data, not from Queries! (VLDB'20 Talk)

DeepDB: Learn from Data, not from Queries! (VLDB'20 Talk)

The virtual conference talk for our paper: "

[VLDB 2020] Deep Unsupervised Cardinality Estimation

[VLDB 2020] Deep Unsupervised Cardinality Estimation

Leveraging deep unsupervised

Data Collection and Quality Challenges for Deep Learning (VLDB 2020 Tutorial Part.01)

Data Collection and Quality Challenges for Deep Learning (VLDB 2020 Tutorial Part.01)

Steven Euijong Whang and Jae-Gil Lee, "

Practical Applications for DuckDB (with Simon Aubury & Ned Letcher)

Practical Applications for DuckDB (with Simon Aubury & Ned Letcher)

DuckDB's become a favourite

LMFAO: An Engine for Batches of Group-By Aggregates | VLDB Demonstration 2020

LMFAO: An Engine for Batches of Group-By Aggregates | VLDB Demonstration 2020

LMFAO: An Engine for Batches of Group-By Aggregates Layered Multiple Functional Aggregate Optimization by Maximilian ...

LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs

LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs

This is the video of our presentation of "LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs" at EDBT 2022.

Analytics for not-so-big data with DuckDB - David Ostrovsky - NDC Oslo 2025

Analytics for not-so-big data with DuckDB - David Ostrovsky - NDC Oslo 2025

This talk was recorded at NDC Oslo in Oslo, Norway. #ndcoslo #ndcconferences #developer #softwaredeveloper Attend the next ...

Reverse Engineering Data Files

Reverse Engineering Data Files

Chapters: - 0:00:00 - Announcement - 0:01:03 - Intro - 0:09:16 - Bootstrapping the Project - 0:15:05 - Should you handle result of ...

P115 | Deep Unsupervised Cardinality Estimation

P115 | Deep Unsupervised Cardinality Estimation

2-min paper review.

Analytics for not so big data with DuckDB | David Ostrovsky

Analytics for not so big data with DuckDB | David Ostrovsky

Ready for Build Stuff 2025? Grab Your Tickets to the Software Development Conference You Can't Miss ...

F-IVM: Learning over Fast-Evolving Relational Data | SIGMOD DEMO 2020

F-IVM: Learning over Fast-Evolving Relational Data | SIGMOD DEMO 2020

F-IVM:

SIGMOD’20: Auto-Suggest: Learning-to-Recommend Data Preparation Steps

SIGMOD’20: Auto-Suggest: Learning-to-Recommend Data Preparation Steps

See more Northwest Database Society talks here: http://db.cs.washington.edu/nwds/nwds.html.

Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby

Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby

EE380: Computer Systems Colloquium Seminar Information Theory of Deep