The amount of data stored is growing at a phenomenal rate. This paper documents the growth and suggests that a new standard, CMIS, may be useful in getting better control over data and data repositories.

1. How Can
Content Management Software
Keep Pace?
San Francisco Gilbane Conference 2009
Content Integration Strategies
Dick Weisinger
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June 4, 2009

2. Dick Weisinger
 Vice President and Chief Technologist
Formtek, Inc
 20+ years of experience in Content,
Document and Image Management
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 Regular blogger at
http://www.formtek.com/blog

3. Formtek
 An ECM software and services company
– 25-year history
25 year
 Experts in general ECM and CM space
 Depth of experience in engineering data
management
 Formtek Orion ECM Software
 Alfresco Gold Integration Partner

4. Drowning in Digital Data
 Hand-held devices  E-Discovery / Records
Management
 High-resolution video
 Di iti d B i
Digitized Business D t
Data
 High-End Video Games
 Financial and Health
 High-Resolution
Records
Graphics d Images
G hi and I
 Business Continuity
 Scientific Data
Backups
Analysts at:
Gartner Group,
Forester Research,
Research
IDC and
The 451 Group
all predict massive growth in digital data.
data

5. Size of the Digital Universe
 2003 – 20 exabytes
 2006 – 161 exabytes
 2007 – 281 exabytes
 2008 – 486 exabytes
 2010 – 988 exabytes of data
 2011 – 1800 exabytes of data
 2012 – 2500 exabytes of data
(30% of data is created by enterprises) Source: IDC
One Exabyte == 1 billion gigabytes or 1000 petabytes
(about 250 million DVDs)
161 exabytes is the equivalent of 12 stacks of books each
extending 93 million miles from the earth to the Sun.

6. Data in Business and Science
 Walmart adds a billion rows of data to
its 600 terabyte database every hour
 Chevron’s gas and oil exploration
collects 2 terabytes of data daily
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 Large Hadron collider in Switzerland to
collect 300 exabytes per year
 Department of Energy has increased
their data by a factor of 10 every four
years since 1990

7. Hardware’s Shrinking Cost
Year Cost/MB
1986 $51.30
Storage costs are
1991 $13.00 plummeting,
plummeting but not as fast
1994 $1.00 as the amount of data is
growing.
1997 $0.09
$0 09
2000 $0.07 Cheap storage costs also
2003 $0.02
$0 02 encourage applications to
store ever more data.
2009 $0.0002

8. Can Software Keep Pace?
How Can We Find Anything?
 Search Algorithms have evolved and
improved, but…
 Internet Search is only Fair to Good
– Google Page-Rank
 8+ billion web pages, hundreds of thousands of
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servers
 Enterprise Search is Poor
– Usage patterns are hard to model

9. The Problem of Search
 49 percent of business users say that finding
data is difficult d time consuming.
d t i diffi lt and ti i
-- AIIM 2008 Market Study
 Users have a 50 percent success rate at
search
h
-- Recommind Survey
March 2009

10. Scattered Data Repositories
p
 Corporate Applications
– ERP
– PLM/PDM
– Business Intelligence / Knowledge Management
– Content and Document Management
 Relational Databases
 Local and Shared File Syste s
oca a d S a ed e Systems
 Internet/Intranet HTTP servers
 Email Servers
 Disk Appliances (digital cameras, cell phone…)

11. Multiple Repository Challenge
p p y g
Problem
 How to access and search data to achieve:
Compliance
eDiscovery
Business Intelligence
Challenge
 Many organization have multiple repositories from
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multiple vendors
 Lack of standards around API and query language
 Each system is different and has very little common
reuse

12. Unstructured Data Search is Hard
 80 percent of enterprise data is unstructured
p p
– Eg., emails, PDF, Word and Office docs
 No underlying data model or schema
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– emails and IM often lack context and use
shorthand and abbreviations that increase the
search challenge

13. Huge Data Sets Brings Huge Problems
 Search gets harder as data sets grow
– Longer to index and search
– Harder to determine context
 The more systems, the harder to secure
 The more systems, the harder to
consolidate search
 Conflicting or Inconsistent Data
– Whi h i th system of reference?
Which is the t f f ?

14. Getting Data Under Control
 Ultimate goal: Content Intelligence
– Knowledge extraction
– Ability to distill, condense and summarize data
How?
 Apply more Structure and Reuse
– XML Tags
 Allow greater access across data sources
– Consolidation of Systems
– Integration of Systems

15. Creating Structure
Semi-Structured Data
S S
 Use a structured native data format
– XML Authoring/Publishing applications
 DITA publishing XML
– Microsoft Office 2007 docx, etc. (Office Open
XML)
 Complex: 29 namespaces and 89 schema models
 Add Structure
– Append Headers and Embedded Properties
 Eg., Tiff, jpeg images
 PDF and embedded Microsoft Office files
 Associate tags and metadata with
unstructured data

16. Centralized Repository Efficiency
 Management efficiencies of scale
 More efficient search
– No need to consolidate search results
 Available to users via a single interface

17. Integration of Repositories
 Content-Intelligence Platforms can
integrate/unite multiple repositories
 XML is the pipeline for integration
 Integration via APIs or XML Web
services
– REST Web Services have momentum
– Integration with SOA

18. CMIS -- ECM Integration
 ECM vendors have united to create a
new interoperability standard:
Content Management Interoperability
Services (CMIS)
– Web services for sharing information
between different content repositories
p
– “SQL for Document Management”

19. What is CMIS?
 Content Management Interoperability Services
– Defines a lowest-denominator CM capability set
– CM content is accessed as SOAP or AtomPub
(REST) web services
– A single application works identically with content
from any CMIS vendor
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20. CMIS Timeline
 1993 – ODMA (Open Document Management API)
 1996 – DMA (AIIM Document Management Alliance)
 1996 – WebDAV (Web-based Distributed Authoring and Versioning )
 2002 - JSR-170 / Java Content Repository (Day Software)
JSR 170
 2005 – iECM (AIIM Interoperable ECM)
 October 2006 – CMIS started
 August 2008 - Contributing members invited
 September 2008 - Draft Specification submitted to
OASIS
 Possible completion and acceptance in late 2009 or
early 2010

21. JCR versus CMIS
Session-based API Services Based
Java Only Language Agnostic
“Complete” ECM Core ECM functions
Infrastructure Interoperability
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Targets DM, RM, Intended specifically
DAM, WCM… for DM
Complex Simple
Prescriptive Little or No Change
Connectors by Day Vendor Connectors
Version 2.0 Version .61
Design spearheaded Design Led by Top
by Day Software Tier ECM Vendors

22. CMIS: Creators and Participants
 Founding Companies for the Original Standard
– EMC/Documentum
– IBM/Filenet
– Microsoft
 Contributing Members (after August 7, 2008)
– Alfresco
– Open Text
– Oracle
– SAP
– More …

24. CMIS – The Model
 Documents
– Eg Office document or image
Eg.,
– Content, Metadata and Version History
 Folders
– Defines Organization and Hierarchy
– Container, Metadata and Hierarchy/Organization
 Object Links and Relations
j
– Reference between two folders or documents
– Requires a source and target
 Policies
– Set of rules that can be applied to control other objects, eg.
ACLs or retention policy

25. Benefits of CMIS
 Standardized Core ECM functions
 Enables Interoperability between repositories
p y p
 Encourages Flexible Application Development
 Encourages ‘mash-up’ composite applications
 A single application can consolidate and
aggregate content from multiple CMIS
repositories
 Business Processes/Workflow can span and
touch all enterprise content

26. CMIS Weak Points
 Only Basic Content Functions Available
 Does not cover Admin/Management
 Does not cover User Authentication
 Does not handle Security/Authorization

27. Applications
 Workflow/Business Processes
– Connect work packages from any
repository
 Portals and Mash-ups
– Aggregated Content from multiple sources
 E-Discovery and Compliance

28. Summary
 Massive Growth in Content Creation
 Advances in hardware technology is
fueling content creation and storage
 Search and Retrieval of content grows
in complexity with its volume
 Content Intelligence is needed to bring
understanding to data
 Standards like XML and CMIS provide
p
consistent classification and handling of
data