1 INTRODUCTION

2 RELATED SURVEYS AND TUTORIALS

3 EVOLUTION OF LEARNED INDEXES

4 INDEXING THE LEARNED MODELS

5 LEARNED IMMUTABLE INDEXES  
5.1 The One-dimensional Case  
5.2 The Multi-dimensional Case  

6 LEARNED MUTABLE FIXED DATA LAYOUT INDEXES  
6.1 The One-dimensional Case  
6.2 The Multi-dimensional Case  

7 LEARNED MUTABLE INDEXES WITH DYNAMIC DATA LAYOUT  
7.1 The One-dimensional Case  
7.2 The Multi-dimensional Case  
7.3 Open Branches in the Taxonomy  

9.1 The One-dimensional Case  
9.2 The Multi-dimensional Case  

10 TOWARDS THE INTEGRATION OF LEARNED INDEXES INTO PRACTICAL SYSTEMS  
10.1 The One-dimensional Case  
10.2 The Multi-dimensional Case  

11 OPEN CHALLENGES AND FUTURE RESEARCH DIRECTIONS  
11.1 Total Ordering and Error Bound  
11.2 Choice of ML Models  
11.3 Model Training and Re-training  
11.4 Supporting Dynamic Inserts/Updates  
11.5 Concurrency  
11.6 Index Compression  
11.7 Security  
11.8 Benchmarking  
11.9 Theoretical Analysis  
11.10 Leveraging GPUs for Learned Indexes  

12 CONCLUSION