Lecture 20: Scaling Inference with RDBMS Slides by Niu et al.

Today Markov Logic Scaling up grounding with RBDMS Scaling up search with partitioning Main-memory Statistical Analytics

Section 1 1. Markov Logic

Section 1 Data Model

Section 1 Markov Logic*

Markov Logic by Example Section 1 Markov Logic by Example

Section 1 Inference

Section 1 Inference

How to Perform Inference Section 1 How to Perform Inference

How to Perform Inference Section 1 How to Perform Inference

How to Perform Inference Section 1 How to Perform Inference

2. Scaling up Grounding with RDBMS

Section 2 Challenge

Section 2 Grounding in Alchemy*

Section 2 Grounding with RDBMS

Grounding Performance Section 2 Grounding Performance

3. Scaling Search

Section 3 Scaling Search

Section 3 Scaling Search

Partition to Scale up Search Section 3 Partition to Scale up Search

Effect of Partitioning Section 3 Effect of Partitioning

Partitioning vs Quality Section 3 Partitioning vs Quality

Partitioning Improves Quality Section 3 Partitioning Improves Quality

It’s all about tradeoffs Section 3 It’s all about tradeoffs

4. Main-Memory Statistical Analytics

Section 3 Memory Model and SGD

Section 3 Access Methods

Section 3 Modern Architectures

It’s all about tradeoffs Section 3 It’s all about tradeoffs

It’s all about tradeoffs Section 3 It’s all about tradeoffs

It’s all about tradeoffs Section 3 It’s all about tradeoffs

Conclusion Optimized data access makes a difference It’s critical to understand and exploit tradeoffs Modern hardware introduces new opportunities