DataCyte - The Future of Data Storage & Retrieval

Converting Data to Information
29/04/2011 DataCyte (Pty) Ltd 1

DataCyte Group of Companies

• Founded in 1998
• Previously known as World Wide Objects
• Privately owned and funded
• Development done in Pretoria, South Africa
• Expanding to create distribution and partner network
• Building relationships with ISVs

29/04/2011 2

DataCyte Timeline
 1998 - Product was conceptualized, developed first version by late 1999.
 2000 - Lodge Patent Application
 2001 - Rated 5-10 years before IBM grid computing initiative by
DARPA/CSC/Lockheed Martin
- Awarded United States of America Department of Defense contract
 2002 - Defense contract suspended due to war on terror
 2003 - Return to South Africa due to declaration of war against “terror”
- Start delivering healthcare systems to South African market
 2005 - Return to the US market with Healthcare and hi-tech value proposition
 2006 - Benchmark data analysis capabilities with Zirmed, prove a 50% in size reduction
and 10x faster
- Entered into business relationship with Dr PatrickSoon-Shiong of Abraxis
Biosciences and American Pharmaceutical Partners Inc.
 2008 - A conflict of product direction emerged with Dr Patrick Soon-Shiong - resulted
in termination of the relationship. All Intellectual Property rights reverted back to
DataCyte.

29/04/2011 3

DataCyte Timeline cont.
 2008 - Cedars Sinai Cancer and Proteomic Research Unit (UCLA) benchmark
- DXS Health Care Systems Technology Partnership (www.dxs-systems.com)
- Trash Can Kids Technology Partnership (www.trashcankidz.com )
- Electronic Price Labeling Technology Partnership
- Interactive Television/Phumelela Technology Partnership
(www.phumelela.com)
 2009 - Establish strategic partnership Health One Global (www.healthoneglobal.com )
- IR Global Partnership to deliver international roaming at dramatic discounted
rates and enabling prepaid customer to also roam.
- Barlow World Logistics Product Development
- Re-engage with the United Sates Department of Defense through US presence
- Granted US Patent #7571442

29/04/2011 4

29/04/2011 e-Merchandising (Pty) Ltd t/a Revelation Systems 5

 2010
April - Booz Allen Hamilton (www.boozallen.com) presents DataCyte as future data
solution at American Association for the Advancement of Science. AAAS (www.aaas.org)
is the largest paid circulation of any peer-reviewed general science journal in the world,
founded in 1848, and is considered one of the global authorities in the direction of Science,
Engineering and Innovation
May - Launch Interactive Television, 400 units rolled out in TABS. Prime Media is
currently finalizing purchase of advertising slots for 12 month period.
June - Launch of DXS (dxssynergy.com) web based system to the USA market as part of
its global rollout. A global vendor in the provisioning of healthcare related systems.
June – Launch of Trash Can Kids (www.trashcankidz.com)
June – Launch of Process Discovery Product with 2 customers going live this month. The
system has already being adopted by a large defense manufacturer.
June - E-Discovery product launched with EM (The largest non-life actuarial consultant
firm in UK)

29/04/2011 6


 2010
June - Negotiation started with Bytes Technology and its Med-e-mass
(www.medemass.com) subsidiary to underpin their current suite of management system
with a comprehensive EHR Solution for the South African market.
July - Health One Global (www.healthoneglobal.com.au) launches Personal Electronic
Health Record and Medical Management record in Australia. This launch coincides with the
launch of the Australian Government Unique personal health identifier, with the support of
the Australian Automobile Association and the Royal Academy of Physicians as a first step
to provide the Australian a health record management service. The Australian government
has legislated that all citizens must have these records in place by 2013.

29/04/2011 7

Why DataCyte?

29/04/2011 e-Merchandising (Pty) Ltd t/a Revelation Systems 8

Computing Challenge
• The “Global Village” has “Global Data”
• Boundaries removed
• Information flow is more pervasive
• Physical Storage
• Users store more data than ever before
• Little new development in Data Retrieval Systems
• Processing
• More processing required to retrieve similar data
• Little development in Computing Processing Systems
• Present Business Tendencies
• Swing back to centralized systems
• Swing back to thin client


DataCyte Patented Solution
• Performance not dependent on number of records

• No single point of vulnerability
• No central registry
• Information redundantly distributed
• RAIS – Redundant Array of Inexpensive Servers

• Dynamic, Intelligent Information
• Contextual „named‟ links between data entities
• Dynamic data structure
• Pervasive Associations

• Self-managing, Distributed Information Structures
• Any Entity must have „independence of existence‟
• Entities „self-aware‟ of environment

• Web-enabled with open interface - Apache


PerformanceFeatures
• Access by association
• Fully distributed storage system
• DataCyte data storage is 10% of the size of traditional
systems
• Sustainable data creation at 400 000 cytes per second on
a standard desktop computer
• Random data access speed of over 250 000 cytes per
second on a standard desktop computer
• Caches up to 25 000 000 cytes in 2Gb memory
• Can access from 250 000 000 000 cytes in sub-
millisecond
• Runs on Linux and Windows


DataCyte Technology

Cyte
• Parent Registry
• Child Registry
• BLOB content
• Any data form
• Code
• Lua
• Others possible
• Flags
• Security/Access
control
• Content type, etc
• Native methods
• Provided by service


Access Models
• Multiple Logical Models: Data and application layer

Network Structured Containment


Case Studies


Case Studies DataCyte Pilot/Test Process
Not part of test –
steady source
Current State Process

DataStage ETL
MS SSIS MDX
BAH DW Process Timing: PME Data MS SSRS
MS SSAS Query
Oracle 4.5 hrs daily Mart Reports
Oracle –
6.5 hrs for closings 750GIG
(2 times month)

DataCyte Test

DataCyte Extraction & Web
BAH DW Translation Crystal
DataCyte Service
Oracle Reports
Fact
Maps


Case Studies
• BAH: Implementation of BI Reporting
Oracle DataCyte
• Database size 750Gb 51Gb (6,8%)
• Retrieval speeds:
Indexed Random Access 0,152 secs 0,008 secs (5,2%)
Indexed Step Thru 0,963 secs 0,016 secs (1,66%)
Unindexed Step Thru 2.515 secs 10.78 secs (425%)

• Hardware Platform:
±US$2 000 000 US$ 1 000
SunTM Grid Rack 400, SAN Low-end DesktopVM
1 x Staging areas, 1 x Cube storage 2,33GH processor 200GB HDD

• Software: ±US$3 000 000 Software: ± US$ 500 000
ELT Toolkit, DataCyte
2 x Oracle 11g
MS HyperCube


Other Case Studies
• Proteomic Research Unit
• Database Size
1,3Tb in Oracle 60Gb in DataCyte
• Retrieval speeds:
1½ minutes < 1sec in DataCyte
1 - 2 days < 11-66 mins in DataCyte
• Hardware Platform:
SunTM Grid Rack of 400 Toshiba Laptop
Sun FireTM x64 servers 1,86GH processor
7 200rpm drive

• UCS SAP database
• 860Gb in DB2 database 100Gb in DataCyte
• Queries up to 1000 times faster


Applications Developed

• Knowledge Management Systems
o e-Learning Systems
o Interactive TV Management Systems
o Medical Information Systems

• Health Management – “Single Patient Record”
• Practice Management
• Clinic Management System
• Pathology Laboratory Management
• Clinical Trials System
• Hospital Management System


Applications Developed
• Data Warehousing
o ETL
o “Data Cube”
o Lawgistics
o Fraud Detection
• SME Payroll System
• Process Management Server
o Document Tracking Systems
o Business Process Modeling
o Supply Chain Management System
• Computational Performance Systems
o Biometrics
o Proteomic and Genomic Analysis
o Shortest Path Routing


DataCyte Benefits

• 90% reduction in hardware requirements

• 10 to 1000 time speed improvement

• Ability to populate archive/warehouse in real-time

• Ability to access archived data faster than existing on-
line live system

• Extension of life of live systems

• Greater security due to ALL history on-line


Contact Details
• DataCyte (Pty) Ltd
• 489 Clarence Street Tel: +27 12 993 1256
Waterkloof Glen Fax: +27 12 993 2412
Pretoria
• Michael F Salomon CEO
• Cell: +27 82 552 5411
• Peter Salemink COO
• Cell: +27 83 677 2783
• Daniel Opland Technology Evangelist
• Cell: +27 83 312 5947


Customers
• Booz Allen Hamilton Inc

• South African Fraud Prevention Service

• TrashCanKidz Limited

• Broadband Interactive TV System

• PayStaffOnline (Pty) Ltd

• 360 Link-up Limited / EMC Limited


Back-up Slides


Technology Overview
Database Management System
• Access
• Object
• SQL
• Cyte

Etymology: “Cyte”
• Ancient Greek word κύτος (kýtos)
• Container or Receptacle
• Human body → part of cell that keeps everything together

Developed in C++
• Runs on Windows and Linux

ODBC, XML and Web Service access

Apache module: mod_dsa
• HTTP(S), FTP, WSDL, SOAP, …

Technology Overview
• Store: any form of data → „Cytes‟
• Serialized and persisted on creation (more later)
• Accessed by association in a contextual / stateful manner
• Collectively form multiple intersecting hierarchies
• Each Cyte has the potential to form part of a distributed cloud
• Virtualize disparate data → single federated view
• Contain application business logic
• Lua (www.lua.org)

• Lua
• Powerful, fast, lightweight scripting language
• Embedable
• Lua is widely used:
• Industrial Applications (Adobe: Photoshop Lightroom)
• Games (Blizzard: World of Warcraft)
• Embedded Systems (Ginga, Digital TV in Brazil)
• Lua Server Pages
• Tag-based Web applications that dynamically generate
Web pages

Technology Overview
Execution Layers
• Application Layer
• Data / Engine Layer

Technology Overview

Basic Performance
(1.6Ghz Dual Core, 3Gb RAM, 7 200 rpm drive)

Sustained creation speed
• 400 000 cytes per second
Sequential access speed
Random access speed
Cache
• 25 000 000 cytes in 2Gb memory
Access
• Any element from 250 billion elements in under a millisecond

Data Structure

Cyte
• Parent Registry
• Child Registry
• BLOB content
• Any data form
• Code
• Lua
• Others possible
• Flags
• Security/Access
control
• Content type, etc
• Native methods
• Provided by service

Data Structure

Demonstration: IDE

Data Structure

Logical representations: data & application layer
• Network representation
• Structured representation
• Containment representation

Network Structured Containment

Data Structure

Complexity vs Simplicity
• Simpler → faster learning curve

• Translation layer
• RDBMS
• Programmed
• Maintained
• Adding features, fixing bugs, improvements
• Collectively comprise 80% of lifetime cost

• DataCyte
• No translation layer
• Saving: Development (Time and Cost)
• Integrated into database layer (a la EJB)
• e.g. Cytes with application logic

Data Structure

Impedance of Mismatch (Translation Layer)
• Maintenance and Development (RDBMS)
• Different mapping → mismatch and integrity violation
• Subtle Issues
• Difficult to locate (time + money)
• Lower impedance of mismatch in DataCyte
• No translation layer → natural modelling of data

Architecture: Simple
• Option: logically structure and constrain → RDBMS + ODBC
• Multiple logical views of the same data
• Facilitates conformance to multiple standards

Discovery

Logical Model
1 2 3 4 External

Conceptual Model Conceptual
Physical
Model

1 2 Physical

Cytes
→ Logical representation of physical storage
→ Navigational construct
each navigation → physical disk read
→ Brokered by DataCyte service

Discovery
Each Cyte is addressed as:
IP address + Disc + File + Position in file

Discovery

Cytes contain application logic
• Variables → pointers
• Access stored data
• Execute application code

Variable =

Discovery

Contextual Execution

Demonstration: IDE

…

Discovery

• Cyte Discovery
• Relative Paths
• in reference to executing Cyte
• getAge() example

• Absolute paths
• in reference to root Cyte

?

Query Approach

• Types of Queries
• Without indexes
• each record is checked in turn
• Indexed
• filtered records

• Query approach (same as RDBMS)
Know what you are looking for
AND
Where you want to look for it

• Query Steps:

STEP 1: IDENTIFY STEP 2: POPULATE
RESULT SET RESULT SET

Query Approach
• At time of query
RESULT SET IDENTIFICATION
• Improved indexes

RESULT SET POPULATION (Compound)
• Traverse logical layer (minimal reads)
• Context = Stateful Results
vs
• Additional external lookups

Additional External Navigate through Logical Structure
Lookups O(n) → O(1)

Query Approach

Define: Contextual / Stateful Results

Query Approach

Addressing schema
• Defines context of access
• Cyte → Unique ID within local file system
• Offset within file
• Cytes simply exist within file system
• No global registry

Multiple Contexts = Multiple Addresses

Address = Context = Chain of ID‟s (named)

Query Approach

• Query Language
• Show of hands: SQL users
• Similar to XPath
• Parent or child Cytes (multiple criteria)
• SQL Interface
• Cytes that conform to relational model

• Lower complexity of architecture
• More natural language
• Steeper learning curve (learn more faster)

Performance & Scalability

• Sub-linear Performance Degradation
• Logical Layer → Directed Searches
• Example: Geo-spatial modelling

• Instantiation
• Full control over level
• No class hierarchy

• Multiple Logical Structures
• Same data, different context
• Multi-dimensional searches → single dimension

• OLTP
• Architecture marries Structured + Networking paradigms
• Container Topology
• Allows extensible heterogeneous data structuring
• 3-stage versioning protocol
• Balance: performance and integrity

• Data Footprint
• Encoding and Compressing on storage

• No Intermediate link tables

Intermediate
Products Table Ingredients

• Proof of Concept: 2008
• Cancer research hospital (Los Angeles)
• Considerable funding
• A proteomic analysis problem – blood analysis study
• Data mining to search for cancer markers
• 50 data samples
• 250 billion data elements
• 1.3 Tb in Oracle

• Results are from the same data set and same queries
Cancer Center DataCyte
Single criteria queries 1½ minutes < 1 sec
Complex Queries ± 1 – 2 days < 11-66 mins
Hardware Sun™ Grid Rack 400 Laptop
Sun Fire™ x64 servers
Data footprint 1.3Tb 60Gb


• No Intermediate link tables

Intermediate
Products Table Ingredients

Query Approach
Define: Contextual / Stateful Results

Data Storage

o Serialized

o Encoded Open
Encode Decode
o Compressed
Encoded
o Pages
Decompress
Compress
o Caching Stack Encoded &
Compressed
o Data Distance

Data Storage
o Leaf nodes
o Stack management
o Partial Decoding
o Data Management

Performance
Four Spheres of Influence during Design

Data Storage
• Enterprise Cloud Storage
• Soft RAIS using commodity hardware
• RAIS provides soft parallelized, grid computing
• Soft RAIS enables redundant distribution of cytes
• Granular scalability and full sharability of resources
• Elastic auto provision of service and resources
• Unified access to data through multiple data models
• New programming approaches unconfined by
• old designs and
• existing programming languages
• to tackle the new data flood.
• Green
• Footprint
• Power usage – running, cooling and start-up

Data Storage

• External Data Sources
• Lua add-on libraries: LuaCOM and ADOLua

• Access: Data Services
• MSSQL NCLI (Native Client
Interface)
• DB2 OLEDB (Object Linking and
Embedding)
• Oracle OLEDB (Object Linking and
Embedding)

Security
• Security implemented by the service on the cyte level
• Domain-based, inclusion, exclusion
• Cyte-to-cyte communication is encrypted
• Redundant distribution of cytes provides additional
security
• Contextual access provides further flexibility for
security
• Child and parent presentation
• Hardware encryption of storage is preferable
• Cyte granularity enables
• Blind security information retaining associations
• Cleansed health data with relationships
• DataCyte can integrate with existing authentication /
authorization systems (LDAP, Active Directory)

Disaster Recovery
• Transaction-based with roll-back
• All transactions are Atomic, Consistent, Isolated and
Durable (ACID)
• 3-state versioning protocol
• My old
• My new
• Yours
• provides fine grain control
• Balance between performance and integrity
mitigation
• Each service can partial recover from physical loss
• Redundancy could provide complete recovery

DataCyte - The Future of Data Storage & Retrieval

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