The document proposes a federated in-memory database system for life sciences that addresses the needs of patients, clinicians, and researchers by enabling real-time analysis of big medical data while maintaining data privacy and locality. It describes key actors and a use case in cancer treatment. The proposed solution incorporates local compute resources through a federated in-memory database with a cloud service provider managing shared algorithms and master data, while sensitive patient data resides locally.

1. A Federated In-Memory Database System For Life Sciences
Dr. Matthieu-P. Schapranow
BIRTE/VLDB 2015, Kohala Coast, Hawai’i, HI
Aug 31, 2015

2. ■ Online: Visit we.analyzegenomes.com for latest research results, tools, and news
■ Offline: Read more about it, e.g. High-Performance In-Memory Genome Data Analysis:
How In-Memory Database Technology Accelerates Personalized Medicine, In-Memory
Data Management Research, Springer, ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for “Bio Data World Congress” Oct 21-22, 2015 in Cambridge, U.K.
Important things first:
Where do you find additional information?
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
2

3. ■ Patients
□ Individual anamnesis, family history, and background
□ Require fast access to individualized therapy
■ Clinicians
□ Identify root and extent of disease using laboratory tests
□ Evaluate therapy alternatives, adapt existing therapy
■ Researchers
□ Conduct laboratory work, e.g. analyze patient samples
□ Create new research findings and come-up with treatment alternatives
The Setting
Actors in Oncology
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
3
A Federated In-
Memory Database
System For Life
Sciences

4. ■ Can we enable doctors to:
□ Select best treatment options for their patients,
□ Analyze latest diagnostic data about patient’s status,
□ Exchange knowledge with patients to improve quality of living
Our Motivation
Enable Doctors to Use Precision Medicine
A Federated In-
Memory Database
System For Life
Sciences
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Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015

5. Use Case:
Identification of Best Treatment Option for Cancer Patient
■ Patient: 48 years, female, non-smoker, smoke-free environment
■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV
1. Surgery to remove tumor
2. Tumor sample is sent to laboratory to extract DNA
3. DNA is sequenced resulting in up to 750 GB of raw data per sample
4. Processing of raw data to perform analysis
5. Identification of relevant driver mutations using international medical knowledge
6. Informed decision making
Schapranow, Trends and
Concepts Lecture, July
2, 2015
Turning Big Data into
Precision Medicine
5

6. Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
Analyze Genomes:
Real-time Analysis of Big Medical Data
6
In-Memory Database
Extensions for Life Sciences
Data Exchange,
App Store
Access Control,
Data Protection
Fair Use
Statistical
Tools
Real-time
Analysis
App-spanning
User Profiles
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
A Federated In-
Memory Database
System For Life
Sciences
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

7. Combined column
and row store
Map/Reduce Single and
multi-tenancy
Lightweight
compression
Insert only
for time travel
Real-time
replication
Working on
integers
SQL interface on
columns and rows
Active/passive
data store
Minimal
projections
Group key Reduction of
software layers
Dynamic multi-
threading
Bulk load
of data
Object-
relational
mapping
Text retrieval
and extraction engine
No aggregate
tables
Data partitioning Any attribute
as index
No disk
On-the-fly
extensibility
Analytics on
historical data
Multi-core/
parallelization
Our Technology
In-Memory Database Technology
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
7
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences

8. ■ Requirements
□ Real-time data analysis
□ Maintained software
■ Restrictions
□ Data privacy
□ Data locality
□ Volume of “big medical data”
■ Solution?
□ Federated In-Memory Database System vs. Cloud Computing
Software Requirements in Life Sciences
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
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9. Federated In-Memory Database (FIMDB)
Incorporating Local Compute Resources
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
9
Site B
Federated In-M em ory
D atabase Instance,
Algorithm s, and
Applications M anaged
by Service Provider
CloudService
Provider
Site A
FIMDB
A.1
FIMDB
A.2
FIMDB
A.3
FIMDB
A.4
FIMDB
A.5
FIMDB
B.1
FIMDB
B.2
FIMDB
B.3
FIMDB
C.1
Federated In-M em ory
Database Instances
M aster Data
M anaged by
Service Provider
Sensitive D ata
reside at Site

10. Where are all those Clouds go to?
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
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Gartner's 2014 Hype Cycle for Emerging Technologies

11. ■ Three Cat IV hurricanes in the Pacific at the same time:
□ Ignacio,
□ Jimena, and
□ Kilo.
■ Kilo (most left) and Ignacio (center)
classified as Cat III by Aug 30, 2015
■ Ignacio will have passed the Hawai’i
Big Island by Sep 2, 2015
(last updated Aug 30, 10pm)
Where are all those Clouds go to?
(Excurse)
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
11
http://www.weather.com/storms/hurricane/news/three-category-4-hurricanes-pacific-kilo-ignacio-jimena

12. Multiple Cloud Service Providers
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
12
Local System
C loud
Synchronization
Service
R
Local Storage
Local
Synchronization
Service
R
Shared
C loud
Storage
Site A
Local System
R
Local Storage
Local
Synchronization
Service
Site B
C loud
Synchronization
Service
Shared
C loud
Storage
R
Cloud Provider
Site A
C loud Provider
Site B

13. A Single Service Provider
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
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Cloud
Synchronization
Service
Shared
Cloud
Storage
Site A Site BCloud Provider
Cloud System
R R

14. Multiple Sites Forming the
Federated In-Memory Database System
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
14
Federated In-M em ory D atabase System
M aster Data and
Shared Algorithm s
Site A Site BCloud Provider
Cloud IM D B
Instance
Local IM DB
Instance
Sensitive D ata,
e.g. Patient Data
R
Local IM DB
Instance
Sensitive Data,
e.g. Patient D ata
R

15. ■ File System
□ Managed services directory
□ OS binaries statically compiled for individual platforms
■ Database
□ In-memory database landscape
□ Stored procedures and database algorithms
□ Master application data
Provided by the Cloud Service Provider
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
15

16. 1. Establish site-to-site VPN connection b/w site and
cloud service provider
2. Mount remote services directory
3. Install and configure local IMDB instance from
services directory
4. Subscribe to and configure selected managed service
Setup of a New Client
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
16

17. ■ Supports parallel query execution
■ Protects sensitive data
■ Brings algorithms to data
Data Partitioning
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
17

18. ■ Test our services at we.analyzegenomes.com
■ FIMDB brings algorithms to data
■ Forms a single virtual database across sites and locations
■ Master data managed by service provider whilst sensitive data resides locally
Summary and Outlook
Schapranow, BIRTE/
VLDB 2015, Aug 31,
2015
A Federated In-
Memory Database
System For Life
Sciences
18
Pros Cons
Single database license Complex operation
Easy to consume services Complex single time setup required
Query propagation by IMDB

19. Keep in contact with us!
Hasso Plattner Institute
August-Bebel-Str. 88
14482 Potsdam, Germany
Dr. Matthieu-P. Schapranow
schapranow@hpi.de
http://we.analyzegenomes.com/