Sema Trends is a sale records analytic tools based on Adobe AIR platform.

1. Sema Trends: Analysis your sales
records semantically!
Kang Zhang (jobo.zh@gmail.com)
Semantc Web Group@Apex Lab
2010-01-22
Used for SJTU Semantic Web Course 2009 Fall only. Redistribution is NOT allowed.
(c) Copyright 2010 Kang Zhang. All Rights Reserved.

2. Agenda

3. Agenda
• Motivation
• UI of Sema Trends 0.1
• Workflow & Components
• Demo Show
• Sema Trends 0.2 Info

4. Motivation
*: MIS: Management Information System

5. Motivation
• The unprecedented boom in eBusiness.
• ebay.com’s revenue: 8.541 billion$ in 2008
• taobao.com’s sales: 200 billion in 2009
*: MIS: Management Information System

6. Motivation
• The unprecedented boom in eBusiness.
• ebay.com’s revenue: 8.541 billion$ in 2008
• taobao.com’s sales: 200 billion in 2009
• Make the MIS* affordable for small business
and individuals.
• reduce the cost; improve the efficiency.
*: MIS: Management Information System

7. Decision Support in C2C

8. Decision Support in C2C
• Find information from sales records.
• “In which area, the customers make the largest number of trades?”
• “Which day do we make trades best?”
• “In which area, the customers do not bargain?”

9. Decision Support in C2C
• Find information from sales records.
• “In which area, the customers make the largest number of trades?”
• “Which day do we make trades best?”
• “In which area, the customers do not bargain?”
• Problems
• Hard to get (need to learn SQL etc.)
• Hard to read (boring, long spreadsheets)

10. User Interface

11. User Interface

12. User Interface

13. User Interface
Filters Panel

14. User Interface
Visualization
Filters Panel

15. User Interface
Visualization
Filters Panel
Quick Links

16. User Interface
Visualization
Filters Panel
Quick Links Queries Panel

17. Workflow of Sema Trends v0.1

18. Workflow of Sema Trends v0.1
Data
User Interface
Controller
Meta/Schema SPARQL/SQL
Manager Engine
Open API
Stat Crawler
RDF/
Tuples
Web Page
C2C Websites RDF/Relational Store
Desktop App

19. Crawler
*: http://open.taobao.com/, http://developer.ebay.com

20. Crawler
Meta/Schema
Manager
Crawl data: Open API
Stat Crawler
• Open API (Taobao TOP ,ebay API*) RDF/
• Webpages
Tuples
Web Page
C2C Websites RDF/Relational Store
*: http://open.taobao.com/, http://developer.ebay.com

21. Crawler
Meta/Schema
Manager
Crawl data: Open API
Stat Crawler
• Open API (Taobao TOP ,ebay API*) RDF/
• Webpages
Tuples
Web Page
C2C Websites RDF/Relational Store
Translate data into Ntriples:
<http://www.taobao.com/product/instance31b7445d258> <http://apex.sjtu.edu.cn/kang/taobao#price> "2004.0" .
<http://www.taobao.com/product/instance31b7445d258> <http://apex.sjtu.edu.cn/kang/taobao#title> "Nokia N95" .
<http://www.taobao.com/user/userleakjo> <http://apex.sjtu.edu.cn/kang/taobao#sale> <http://www.taobao.com/product/
instance31b7445d258> .
<http://www.taobao.com/product/instance31b7445d258> <http://apex.sjtu.edu.cn/kang/taobao#created> "2009-12-28" .
*: http://open.taobao.com/, http://developer.ebay.com

22. Crawler
Meta/Schema
Manager
Crawl data: Open API
Stat Crawler
• Open API (Taobao TOP ,ebay API*) RDF/
• Webpages
Tuples
Web Page
C2C Websites RDF/Relational Store
Translate data into Ntriples:
<http://www.taobao.com/product/instance31b7445d258> <http://apex.sjtu.edu.cn/kang/taobao#price> "2004.0" .
<http://www.taobao.com/product/instance31b7445d258> <http://apex.sjtu.edu.cn/kang/taobao#title> "Nokia N95" .
<http://www.taobao.com/user/userleakjo> <http://apex.sjtu.edu.cn/kang/taobao#sale> <http://www.taobao.com/product/
instance31b7445d258> .
<http://www.taobao.com/product/instance31b7445d258> <http://apex.sjtu.edu.cn/kang/taobao#created> "2009-12-28" .
Store in knowledge base.
*: http://open.taobao.com/, http://developer.ebay.com

23. Analysis Engine
Extend the query step by step
Data
Controller
Meta/Schema SPARQL/SQL
Manager Engine
RDF/Relational Store

24. Analysis Engine
Extend the query step by step
Data
Controller
Step 1
Meta/Schema SPARQL/SQL
Manager Engine
RDF/Relational Store

25. Analysis Engine
Extend the query step by step
Data
Controller
Step 1
Meta/Schema SPARQL/SQL
Manager Engine
Step 2
RDF/Relational Store

26. Analysis Engine
Extend the query step by step
Data
Controller
Step 1
Meta/Schema SPARQL/SQL
Manager Engine
Step 2
RDF/Relational Store
Step 3

27. Analysis Engine
Extend the query step by step
Data
Controller
Step 1
Meta/Schema SPARQL/SQL
Manager Engine
Step 2
RDF/Relational Store
Step 3
Step 4

28. SimpleTripleStore
Meta/Schema SPARQL/SQL
Manager Engine
RDF/Relational Store
*: http://labs.google.com/papers/bigtable.html

29. SimpleTripleStore
Meta/Schema SPARQL/SQL
Manager Engine
A limited light-weight triple store
• built on Adobe AIR SQLLite Runtine.
RDF/Relational Store
*: http://labs.google.com/papers/bigtable.html

30. SimpleTripleStore
Meta/Schema SPARQL/SQL
Manager Engine
A limited light-weight triple store
• built on Adobe AIR SQLLite Runtine.
One-table store RDF/Relational Store
• designed for star-shape query (attributes
only).
• recall Google’s BigTable*
*: http://labs.google.com/papers/bigtable.html

31. SimpleTripleStore
Meta/Schema SPARQL/SQL
Manager Engine
A limited light-weight triple store
• built on Adobe AIR SQLLite Runtine.
One-table store RDF/Relational Store
• designed for star-shape query (attributes
only).
• recall Google’s BigTable* SimpleTripleStore
Naive SPARQL
Translator
Naive SPARQL translator
• translate star-shape SPARQL into line- Adobe AIR SQLLite
shape SQL.
*: http://labs.google.com/papers/bigtable.html

32. Advantages

33. Advantages
Easy-to-use user interface:
• do not need learn any language.
• just click to find the answer

34. Advantages
Easy-to-use user interface:
• do not need learn any language.
• just click to find the answer
Easy-to-read information:
• analysis results are pretty visualized

35. Advantages
Easy-to-use user interface:
• do not need learn any language.
• just click to find the answer
Easy-to-read information:
• analysis results are pretty visualized
y!
Po
we
red
B
Smart and flexible SW storage:
Semantic Web • can be extended to most C2C websites
without considering of the differences
between schemas.

36. See it in Action

37. All above is what we have done in this semester,
now it’s time for future work :-P

38. To be continued: Sema Trends v0.2 (cloud version)

39. To be continued: Sema Trends v0.2 (cloud version)
User Interface Data Proxy
Client
Schema SPARQL Data Mining
Manager Engine Agent
Knowledge Base
Stat Crawler
RDF
Open API/
Web Page
RDF
Cloud
C2C Websites

40. New features
*: DSS: Decision Support System

41. New features
Working on cloud: Schema
Manager
SPARQL
Engine
Data Mining
Agent
• stable, safe and can be released as a web Stat Crawler
application. Open API/
Web Page
RDF
RDF
*: DSS: Decision Support System

42. New features
Working on cloud: Schema
Manager
SPARQL
Engine
Data Mining
Agent
• stable, safe and can be released as a web Stat Crawler
application. Open API/
Web Page
RDF
RDF
More product information:
• enriched by semantic knowledge base
(freebase, dbpedia etc. )
Knowledge Base
*: DSS: Decision Support System

43. New features
Working on cloud: Schema
Manager
SPARQL
Engine
Data Mining
Agent
• stable, safe and can be released as a web Stat Crawler
application. Open API/
Web Page
RDF
RDF
More product information:
• enriched by semantic knowledge base
(freebase, dbpedia etc. )
Knowledge Base
Useful business strategy support: Data Mining
• provided by cloud mining & statistical Agent
result.
• from data warehouse to DSS*
*: DSS: Decision Support System

44. Questions?

45. Thanks~

46. Appendix A
• Why SPARQL?
• Flexible (no schema)
• More powerful hybrid view
• Can be used in APIs for third party
developers