1. Carlos Ordonez, Javier Garcia-Garcia,
Data Mining Algorithms as a Service in the Cloud Exploiting Relational Database Systems
Carlos Ordonez, Javier Garcia-Garcia,
Carlos Garcia-Alvarado, Wellington Cabrera, Veera Baladandayuthapani, Shoaib Quraishi
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

2. Motivation Relational databases are a natural repository of data.
Enterprise Systems
But analytical tasks are often done outside the DBMS
Drawbacks
External data mining software
Data exporting
Privacy issues
- Most of the real life data is stored in DBMS

3. Our proposal
Provide analytical algorithms as a service in the cloud, exploiting the processing power of DBMSs
DBMSs present both in the cloud and in the client side
No external packages required
Standard SQL queries , UDFs and Aggregate UDFs
A set of off-the-shelf algorithms are provided
To provide analytical algorithms

4. Challenges Large volume of data to be transmitted Matrix computations
Processing power requirements of number crunching
Data redundancy
Minimize I/O
All data in relational format
Avoid exporting tasks
-Matrix Comp , while data stored in relational format -
Avoid duplicity of data
Avoid exporting data to external packages

5. Advantages Cloud system can:
Reduce work load on local system
Accelerate analytical processing
Enforce data security
Simplify multiple model management
It is not required to install data mining software, neither in local system nor in the cloud
Everything stored in relational tables
Cloud system can
Accelerate analytical processing
Since the data does not go outside the DBMS, the access permissions still work,
Enforced confidentiality and integrity of data

6. System attributes
Smart local processing: exploit CPU/RAM of local DBMS
Integrated: Local DBMS and Cloud DBMS are tightly integrated
Fast: one pass over input table for most algorithms; parallel
Simple: Calling the algorithms is simple: Stored Procedure with default parameters
Relational: relational tables to store models, job parameters

7. System Components Cloud DBMS Cloud management server Local DBMS
Store procedures, UDFs
Cloud management server
Handling data mining job requests
Monitoring job progress
Cost estimation for 3 alternative processing modes
Managing jobs
Local DBMS
Web application
User can post jobs using a web interface

8. Models
PCA
K-Means
Linear Regression
Variable Selection
Naïve Bayes

9. Job processing

10. Remarks Hybrid Mode: Cloud Model: Local Mode:
Sufficient statistics calculated in local DBMS
Take advantage of local power processing, RAM
Cloud DBMS receives a summarization
Transmitting the entire dataset is avoided
Model computation in cloud DBMS
Cloud Model:
Summarization step
Occurs in cloud
Large data sets: Sampling
Local Mode:
Preferred for small datasets
Summarization/Sampling

11. Job Scheduler FIFO job scheduling by default
If wait time for an individual job goes beyond a threshold ψ, then the system switches to SJF
If most jobs take a lot time to compute and the waiting time is beyond ψ, then the system switches to Round Robin(RR).
As the load decreases, the system backtrack to SJF, FIFO

12. Job queue

13. Job queue

14. Algorithm Optimizations
Sufficient Statistics are exploited to accelerate data mining algorithms
Previous work [1] shows that Linear Regression, PCA, Naïve Bayes, K-means are efficiently computed by using sufficient statistics n, L , Q
Sufficient Statistics can be computed
On samples
On the whole dataset

15. Sufficient Statistics: nLQ/Γ
Considering a dataset with n points
The sufficient statistics are generalized as:
n=|X|
Z=[ 1, X, Y]

16. Sufficient Statistics: nLQ/Γ
1 set of sufficient statistic for each class/ cluster is necessary for:
Naïve Bayes
K-means
One matrix Γ is enough for
PCA
Linear Regression
Variable Selection

17. Data transfer comparison
Data set Physical Activity ( n=2.88M, d=42)
Dataset : MB
nLQ/Γ: MB
50,000 times smaller!

18. Optimizations Sufficient Statistics Matrix computations in RAM
Calculated in one parallel scan
Aggregate UDFS
Multithreaded, RAM
Matrix computations in RAM
LAPACK integration
Fast, accurate, stable

19. Summary Sufficient statistics transmitted to cloud
Hybrid processing is best
Job policy: FIFO->SJF->RR
Parallel summarization, parallel scan
Model computation in RAM in the cloud
Complicated number crunching in the cloud
Job and model history in the cloud
All data is relational tables: they can be queried, stored securely

20. References
C. Ordonez. Statistical model computation with UDFs. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2010
C. Ordonez, Y. Zhang, W. Cabrera. The Gamma Operator for Big Data Summarization on an Array DBMS (BigMine 2014). JMLR W&CP 36 :88-103, 2014
Carlos Ordonez, Carlos Garcia-Alvarado, Veera Baladandayuthapani. Bayesian Variable Selection in Linear Regression in One Pass for Large Data Sets, ACM 2Transactions on Knowledge Discovery from Data (TKDD), 2015

21. Questions:
What do you use to communicate both databases?
How NLQ is moved to the cloud?
What utility/protocol do you use to move data from local DBMS to cloud DBMS when system is workingin “cloud” mode?
Why you do not exploit multicores to enable processing several jobs at the same time in the cloud DBMS?