The document discusses different pricing models - Software as a Service (SaaS), Platform as a Service (PaaS), and Data as a Service (DaaS) - for a tool that facilitates extracting, linking, and visualizing linked data. It examines potential cost drivers like CPU usage, storage, and data transfer. It also provides an example calculation of costs for a specific linked data operation and discusses challenges in identifying realistic costs and income. The conclusion advocates exploiting the technology through applications and services that can enhance existing data and provide an integrated data infrastructure.

1. Pricing model
Michael Kaschesky

2. Fusepool USPs
from legacy data to Linked Data
● e.g. extraction, entity recognition, interlinking
visualization of Linked Data
● e.g. GUI, facets, graph browser
user feedback to Linked Data
● e.g. user classification, user labeling, label prediction

3. Options
Software as a Service (SaaS)
Platform as a Service (PaaS)
Data as a Service (DaaS)
Pricing Free - Freemium - Premium

4. Organizations
Documents SaaS

5. PaaS
Storage
System
GUI
Utilities
Sourcing

6. PREFIX foaf: <http://xmlns.
com/foaf/0.1/>
SELECT *
WHERE {
?person foaf:name ?name .
?person foaf:mbox ?email .
}
DaaS

7. Cost drivers
CPU clock usage hours
Persistent storage levels
I/O requests, API requests
Data transfer (outbound)

8. Costs
Cost of storing a document?
Cost of one entity extraction?
Cost of one entity interlinking?
Cost of supporting one user?
...

9. Cost of interlinking?
January February March April May June
800
400

10. Controlled testing
Functions & usage (e.g. interlinking)
Components/services (e.g. SILK, ECS)
Track cost factors (e.g. CPU, datastore)
Link resource use to cost factors

11. Calculation example
Operation CPU use Storage Queries
Input 0.2 second 235 KB 10 SELECT
SILK engine 1.3 second
Output 0.1 second 4 KB 2 INSERT

12. Problem
Not there yet (e.g. FundingFinder)
Testing willingness to pay unrealistic
Identifying cost drivers unrealistic
> uncertainty about income & costs

13. Conclusion
Exploit technology, not end product
● pipeline to enhance legacy data
● landscape viewer, graph browser
● learn, update, predict framework

14. Business model
Michael Kaschesky

15. Patents analytics
positioned between Google Patents
and patent consulting services

16. Technology intelligence
positioned between Google Trends
and technology consulting services

17. Market intelligence
positioned between Facebook/
Criteo/ etc and marketing consulting
services

18. Expertise intelligence
positioned across Facebook (private),
LinkedIn/Technorati (private/work)
and tools such as Science Citation
Index/Arnetminer/biomedexperts

19. Content distributor
positioned between off-the-shelf
data services (e.g. Factual) and
‘manual’ data curation services

20. Content/data distribution

21. Linked Data?
Attribution ... is key to provenance …
making all contributors to the data
value chain visible.
Kingsley Idehen (OpenLink)

22. Attribution?
No economy without attribution
Example: Creative Commons
Creativity wants to be recognized,
then people exchange ideas

23. Data publisher
Data modeler
Data enrichment
Data user
Value
Value
Value
Value
€$¥
€$¥
€$¥

24. Mission & strategy
Mission: communicate more
effectively and efficiently
Strategy: open, integrated, and
viable data infrastructure

25. Products
turn communication use cases into
intuitively usable applications (apps)

26. Customers & partners
Customers publish connected data
● communicate, inform, enable
Partners along the data value chain
● data providers, developers, users

27. Product features
Fusepool mobile app framework for
displaying/entering data per facet
● facet is a particular aspect of the
data relevant to the use case

28. Organizations
Documents
FacetsEntities

29. Product features
Facet = basic core functions to
display/enter data for use cases
App = combination of multiple facets
to meet specific use case

30. Facets
Entities
Generic
mobile
app
framework

31. Product access
Customized solutions for Fusepool
mobile app data framework
Online configurator for data sources,
facets, display parameters

32. Swissdat's role
Reuse/extend Fusepool (e.g. RDFizer,
faceted/graph browsing, DLC)
Fusepool mobile app framework
Support open, integrated, and viable
data infrastructure

33. Profitability
Organic growth until proven
● customization > consulting fees
● online configurator > service fees
● own apps > sales income
Investors extend successful format