1. ML & DB – Options for Integration
Integrated as UDF
Separated:
As ETL (extract, transform, load) – data wrangling, cleansing, preparation
For postprocessing (feeding data out of the database into a ML analysis tool)
Example HANA -> SAS
Extravagant:
Self-tuning databases based on ML

2. Integration – Yes or No Pros Cons Security
Interdisciplinary Opportunity - bringing 2 communities together
Drifting consensus vs con
Pros
Cons
ML adds new insights & intelligence can be gained out of existing data
Debugging is easier
It makes databases more useful
We can bring insights to the ML community
DB allows tracking /version control of data for ML
Opportunity to add provenance correctly to DB
Potential to accelerate when combining
Opportunity to leverage insights from 50years of DB research for new types of problems
Commonalities (next slide)
Security
Open source vs customers own
Integration of software stacks (python, R), dev support, profiling, version control
DBA administration
Huge spectrum of algorithms with very different compute and storage requirements
Immature algorithms
Separate communities
Compute time of ML is so huge it outweighs the advantage of removing datatransfer overhead time

3. A closer look at the machine learning algorithms
Huge range of algorithms, however they share a lot of common arithmetic
For example: dot products
Also, observed a common selection of primitives from the database world
Joins, map, reduce, flatmap
There is an opportunity…
CNNs
Clustering
K-means
Bayesian networks
Page rank
Decision trees
Random forests
Kernel methods

4. Perhaps it is just a matter of time?
Phase 1: Adding, broadening of functionality
Systems become larger, wider
Phase 2: Systems integrate more and become more optimized
If we are convinced this will happen, we can start looking ahead within the research community
Much faster hardware, mature and less ML algorithms

5. Summary
Consensus is currently drifting towards keeping ML and DB separate
Opportunity for next year Dagstuhl, or a new workshop series
Opportunity to speculatively start investigating
Opportunity to apply our own box of tricks to ML algorithms
Opportunities for selective algorithms
Decision trees and random forests for example