The document discusses beautifying data in the real world. It describes how much data exists on the internet, which is estimated to reach nearly 1,000 exabytes by 2015. It also discusses open notebook science, crowdsourcing data, and challenges with real world data like noise and barriers to presentation. Unique identifiers for chemicals and options for analyzing data are examined. The document proposes using semantic web technologies like RDF and SPARQL to build knowledge from beautified data and create non-obvious relationships. It demonstrates visualizing data through services like Google Docs and Second Life.

1. Instructor: Professor Lothar Piepmeyer
Beautifying Data
in the Real World
Group 5:
Toan Do - An Du
Vinh Nguyen - Tan Tran
1

2. How big is the data on the Internet?
2004: The first time Internet exceed 1EB
2005: Eric Schmidt estimated it was 5 million
Terabytes (~ 5EB)
Cisco forecasts that in 2015, the size of the
Internet will reach nearly 1,000 EB
How big is it?
Source: http://www.wisegeek.com/how-big-is-the-internet.htm
http://techland.time.com/

3. If 1 byte = 0.5mm
Source:3http://blog.fliptop.com/how-much-data-is-on-the-internet/

4. Content
Introduction
Open Notebook Sciences appoaching
Curating and presenting the data
Beautfifying the data
Data Visualization & Building a portal from
open data and free services
Demonstration

5. Data on the internet
Source: http://news.bbc.co.uk/2/hi/technology/8562801.stm

6. Problems of data in real world
(Scientific)
Noisy source of data
The barrier of data presentation
OCR version
Text version
Human-readable
Machine readable
…
How to verify the data?

7. Open Notebook Science
Purpose: record full scientific research raw data,
make it available and online
Benefits:
obtain detailed descriptions of procedures
improve the communication of science
increase the progress
reduce time lost due to the repetition of failed
experiments
…

8. Apply ONS on free services

9. Crowdsourcing
a distributed problem-solving and
production model

10. Crowdsourcing

11. Crowdsourcing

12. Crowdsourcing
Source: http://r18ultrachair.com/

13. Validating crowdsourced data
According to ONS, all detail data have been
recorded
The doubtful data also be kept and marked
for

14. Unique Identifiers for Chemical
Entity
Standardize data
Facilitate the integration with other data sets
Consider 3 possibilities
 CAS Registry Number
 InChI
 SMILES

15. CAS Registry Number
 Proprietary
 Cannot converted to chemical structure
 Dependent to a external organization to issue
For example, the CAS number of water is 7732-18-5: the
checksum 5 is calculated as (8 1 + 1 2 + 2 3 + 3 4 + 7 5 +
7 6) = 105; 105 mod 10 = 5
http://en.wikipedia.org/wiki/CAS_registry_number

16. InChI
 IUPAC International Chemical Identifier
 Freely usable and non-proprietary
 Do not have to be assigned by some organization
 Can be computed from structural information
 Human readable (with practice)
http://en.wikipedia.org/wiki/Inchi

17. SMILES
 Simplified molecular-input
line-entry system
 More human-readable than
InChI
 Can convert to InChI
http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system

18. 18
http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system

19. Analysis Options
Access to live data
Get Summary
Complex Statistical representations of
models
Mark the skeptical data for later
consideration

20. 20

21. Google Docs API
Allows developers to create, retrieve, update, and
delete Google Docs files and collections
Also provides some advanced features like resource
archives, Optical Character
Recognition, translation, and revision history.
Useful to store data in the cloud, perform resource
management, convert document formats
https://developers.google.com/google-apps/documents-list/

22. Google Visualization API
Chart Library
JavaScript classes
Data Table
JavaScript DataTable class
Data Source
Chart Tools Datasource
protocol
https://developers.google.com/chart/interactive/docs/index

23. 23

24. 24
https://google-developers.appspot.com/chart/interactive/docs/gallery

25. RESTful Web Service
 Representational State Transfer - a simpler alternative to
SOAP - and Web Services Description Language (WSDL)
based Web services
 Principles:
 Use HTTP methods explicitly.
 Be stateless.
 Expose directory structure-like URIs.
 Transfer XML, JavaScript Object
 Notation (JSON), or both.
http://www.ibm.com/developerworks/webservices/library/ws-restful/

26. Compare REST and SOAP
Who's using REST?
All of Yahoo's web services use REST, including Flickr,
del.icio.us API uses it, pubsub, bloglines, technorati, and
both eBay, and Amazon have web services for both
REST and SOAP.
Who's using SOAP?
Google seams to be consistent in implementing their
web services to use SOAP, with the exception of
Blogger, which uses XML-RPC. You will find SOAP web
services in lots of enterprise software as well.
http://www.petefreitag.com/item/431.cfm

27. Compare REST and SOAP
REST SOAP
Lightweight - not a Easy to consume -
lot of extra xml sometimes
markup Rigid - type
Human Readable checking, adheres to
Results a contract
Easy to build - no Development tools
toolkits required

28. 28

29. An Effort to Aggregate Data from
Multiple Sources
Introducing ChemSpider
An online lookup engine for Chemists
http://www.chemspider.com
40 mil substances
Multiple data sources
A "link farm" to other sources

30. What is "wrong" with
wikipedia.com?
30

31. Wikipedia.com
Not “wrong”:
 Very informative for human being

32. Wikipedia.com
This little guy is left behind
Not machine-readable

33. Semantic Web
Describing things in a way that computers
applications can understand it.
“The Beatles was a band from Liverpool”
Describes the relationships between things (like A
is a part of B and Y is a member of Z) and
the properties of things (like size, weight, age, and
price)
“..will make all the data in the world look like
one huge database“ – Tim Berners-Lee
http://www.w3schools.com/web/web_semantic.asp

34. Resource Description Framework
Is a language to describe resources on
the web
Component of the Semantic Web
Data is self-describing
Triples: "subject", "predicate" and "value“
URIs are used to denote resources

35. RDF
Graph Database
Nodes
Edges
Well-suited for Knowledge Representation
Beautified Data => Knowledge

36. RDF Example
<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:cd="http://www.recshop.fake/cd#">
<rdf:Description
rdf:about="http://www.recshop.fake/cd/Empire Burlesque">
<cd:artist>Bob Dylan</cd:artist>
<cd:country>USA</cd:country>
<cd:company>Columbia</cd:company>
<cd:price>10.90</cd:price>
<cd:year>1985</cd:year>
</rdf:Description>
</rdf:RDF>

37. Semantic Web Example: DBPedia
“Old School” wikipedia:
 http://en.wikipedia.org/wiki/Porsche_Panamera
DbPedia Entries
 http://dbpedia.org/page/Porsche_Panamera
 http://dbpedia.org/page/Chromium_carbide

38. Query Language: SPARQL (sparkle)
Query Language for RDF
Graph Traversal
Matching the triples
Example:
Data:
<http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title> "SPARQL
Tutorial”
Query:
SELECT ?title
WHERE { <http://example.org/book/book1> <http://purl.org/dc/elements/1.1/title>
?title . }
Query Result: title "SPARQL Tutorial"

39. To Infinity and Beyond
• DB2 and Oracle are ready for this train
•Object Database
Versant OODBMS, anybody?
•Machine-Readable Data
Will they become self-awareness?
39

40. “Data Finds Data” and Semantic Data
Model – A Hypothesis
40

41. Non-Obvious Relationship Awareness
LÂM
BẢO
41

42. Non-Obvious Relationship Awareness
LÂM’s
iPhone
LÂM
BẢO
42

43. Non-Obvious Relationship Awareness
LÂM’s
iPhone
BẢO’s
SS Galaxy
LÂM
BẢO
43

44. TheGioiDi
Dong.com
LÂM’s
iPhone
BẢO’s
SS Galaxy
LÂM
BẢO
44

45. TheGioiDi
Dong.com
LÂM’s
iPhone
BẢO’s
SS Galaxy
LÂM
BẢO
45

46. TheGioiDi
Dong.com
LÂM’s
iPhone
BẢO’s
SS Galaxy
LÂM
BẢO
Connection Detected!
-Bao could have met Lam at Thegioididong?
-They could have discussed their World domination
scheme during the meeting there?
-??? 46

47. TheGioiDi
Dong.com
LÂM’s
iPhone
BẢO’s
SS Galaxy
LÂM
BẢO
47

48.  Data Visualization
 Building a portal from open data and
free services

49. Visualization of Data
Top million web
sites (per Alexa
traffic data) was
performed in
early 2010 ]
Source http://nmap.org/favicon/

50. Visualization of Data

51. Second Life
Second Life is a 3D world where everyone you see is a real person and
every place you visit is built by people just like you.

52. 3D Visualization in SL

53. SL- The Opportunity for "Edutainment"
iSchool Teaching: Quizzes and Lectures
Classrooms with Powerpoint Research Center
Drexel Island on Second Life

54. 3-D Environments
http://3rdrockgrid.com/
http://www.secondlife.com/
http://www.craft-world.org
http://www.osgrid.org/
http://youralternativelife.com//

55. Visualization To Suggest New
Experiments

56. Building A Portal From Open Data And
Free Services
 Freely hosted Wiki service
 Google Spreadsheet
 Google Docs API / javascripts
 Visualization services/anlalysis services (2D, 3D)
 RDF/ Senmantic Web/ Webservices
 Cost: free or fit to the purpose

57. Key To Success
Model
+ Transparency
Information
Data
Records

58. Demonstration
 Google Docs
 Second Life

59. References
Oreilly – Beautiful data – Chapter 16th
Beautifying data in the real world
http://techland.time.com/2011/06/01/how-big-
is-the-internet-spoiler-not-as-big-as-itll-be-in-
2015/
http://drexelisland.wikispaces.com/
SMILE to 3D – Secon Life,
http://www.youtube.com/watch?v=tOfhuoRbn
Cg&feature=player_embedded