The document discusses the use of cloud computing services, specifically Google services, in the ChemConnect project which leverages various data management techniques including NoSQL databases and APIs. It highlights the establishment of relationships between independent data sources through RDF (Resource Description Framework) and emphasizes the importance of interconnected data networks for analysis in computational chemistry. The document also touches on the challenges of handling large amounts of data and the necessity for advanced database capabilities.

4. 4 Adopted from: Effectively and Securely Using the Cloud Computing Paradigm by peter Mell, Tim
Grance

5. 5

6. 6

9. Software as a
Service (SaaS)
Platform as a
Service (PaaS)
Infrastructure as a
Service (IaaS)
Google
App
Engine
SalesForce CRM
LotusLive
Adopted from: Effectively and Securely Using the Cloud Computing Paradigm by peter Mell, Tim
Grance

10. 10

11. https://cloud.google.com/
Others exist
(another popular choice)
Why this one?
ChemConnect is based on several Google
services
(and philosophies)
Project Connected to Google Account

12. These are types of services provided by Google
as a cloud service provider
For ChemConnect the
services of interest are:
To run the JAVA based website
(the ‘App’)
The ‘NOSQL’ database:
(for large amounts of information)
Storage (data files)

14. API: Application Program Interfaces

17. User interface on browser, tablet or phone
(adjustable for each)
Generates Interface
ChemConnect
Computing
and
Responses
SERVER CLIENT

18. Example:
ChemConnect is written in JAVA
Eclipse:
Uses a ‘standard’ (public domain)
Environment to write code
Local debug and then
Deploy to Google Cloud

19. Google Cloud The communityLocal Environment
Testing
feedback
Local Deploy
Deploy to Cloud
Local client Interface
Web client Interface

22. Not restricted to ‘accepted’ published data
Recognize interdependencies between data
Database as an analytical tool
Fine-grained

23. Publications and
conferences
Data exchanged between
researchers (email, etc)
Virtual Research Environment
paper
Data files
Clouds (infrastructures)

24. Keywords specifying
DataType
Data Source (origin, time, place, etc.)
Data Qualifications (sharing, quality, etc.)
Data relationships to other data
(ontologies)

25. Purpose:
Defining interrelationships between data objects
Source:
SemanticWeb Concepts
Motivation:
Large body of research in discovering relationships

26. Subject: The subject of the description
Predicate: The description of the relationship between subject and object
Object: The object of the description
Subject Object
Predicate

27. Object Relationship Object
Mech-butane-2011 hasReaction c2h5+o2 = c2h5o2
Mech-butane-2011 hasSpecies c2h5
c2h5o2 = c2h4o2h hasReactant c2h4o2h
c2h5o2 = c2h4o2h hasProduct c2h4o2h
c2h4o2h isIsomer c2h5o2
c2h4o2h hasStandardEnthalpy -276.51 kJ/mol
c2h5 hasProduct c2h5o2
c2h5 hasProduct c2h4o2h
c2h5o2 = c2h4o2h subMechanism C2
c2h5o2 = c2h4o2h subMechanism C2H5O2
C2h5 + o2 = c2h5o2 followedBy c2h5o2=c2h4o2h

28. Passive Connection:
Don’t need to know
which structures you want to connect to
If they share
an RDF subject or a RDF object
Then they are connected!!
Keyword: Passive

29. In one sense,
standards are only important for the initial parsing of the data
and maybe outputting the data
But not within the database itself
If new standards come up,
they can supplement the data
(thinking of the keys, identifiers, meta-data keys, DOIs, etc.)

30. Each ‘bond’ is an RDF

31. Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data Element
Data Element
Data Element
Data Element
Data Element
Data Element
Data Element Blocks of data
Individual pieces of data
(with tags/descriptions)
Network of interconnected
data

32. Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Relationships are
established
between
previously Independent
data sets/elements

33. http://…isbn/000651409X
Ghosh, Amitav http://www.amitavghosh.com
The Glass Palace
2000
London
Harper Collins
a:name
a:homepage
a:author
Author URL
Origin
(and development of
idea)

34. Adds ‘meaning’
to the independent sources of
information
Gives ‘relationships’
Between the
Pieces of information

35. http://…isbn/000651409X
Ghosh, Amitav
Besse, Christianne
Le palais des miroirs
f:nom
f:traducteur
f:auteur
http://…isbn/2020386682
f:nom
http://…isbn/000651409X
Ghosh, Amitav
http://www.amitavghosh.co
m
The Glass Palace
2000
London
Harper Collins
a:name
a:homepag
e
a:author
Common URL!
Connecting sets of
Concepts
French
Language
English
Language

36. Ghosh, Amitav
Besse, Christianne
Le palais des miroirs
f:original
f:no
m
f:traducteu
r
f:auteur
http://…isbn/2020386682
f:nom
Ghosh, Amitav
http://www.amitavghosh.com
The Glass Palace
2000
London
Harper Collins
a:nam
e a:homepage
a:autho
r
http://…isbn/000651409X
Two independent data sources
(who did not know about each other)
Become connected
Passive

37. Extraction of all the bits of information within the data object
CHEMKIN model:
Extract set of molecules (with isomer,thermodynamic data)
Extract set of reactions (with ‘isomer’, kinetic data,
Extract relationships between
molecules and molecules (related through reactions)
molecules and reactions (reactants, products, etc.)
reactions and reactions (reaction network information)
Other Sources:
Automatic Generation:
Mechanism with the information as above, plus
2D-structure, reaction class information, substructure information
Thermodynamic Calculators: more thermodynamic information (plus 2d-structures)
Have to have database capacity to store
this immense amount of info
To be demonstrated
today

38. Chemkin
Model I
Chemkin
Model II
2-D Structure
Computational
Chemistry
Calculations
Automatically
Generated
CHEMKIN
Model
1-Butyl-3-hydroperoxide
C4H11O2
ch2ch2ch(ooh)ch31-c4hh8-3-ooh
hasSpecies
hasSpecies
hasSpecies
hasThermo
isIsomer isIsomer
isIsomer
Thermo
hasThermo
Thermo
hasThermo
Thermo

39. Snapshot from
query interface
UCSanDiego#NaturalGas
IsA
Mechanism
UCSanDiego#NaturalGas#n-c3h7=c2h4+ch3
MechanismReaction
UCSanDiego#NaturalGas

40. UCSanDiego#NaturalGas#N-C3H7
IsAsReactant
UCSanDiego#NaturalGas#n-c3h7=c2h4+ch3
Names specific to the mechanism
Predicate relating items

41. Mechanism
Reaction in mechanism
Molecule in reaction
Simple Species
Name
Isomer
GRI#GRI-3.0#C3H7
Species in another mechanism

43. Extremely large amount of
Information
Needs another
Technology
(even a small CHEMKIN
mechanism translates to
megabytes of information)

45. Traversing through the network of information
is a tool
to ‘analyze’
and
extract more/new information

46. Species
(Isomer)
asReactant
asProduct
Set
Of
Reactions
Set
Of
Reactions
Not just from one
Mechanism,
but from all
cataloged
mechanisms
Database as analytic device

48. Collecting
Information
To
‘’cart’
(building a mechanism)

49. Database as analytic device
isAProduct
Species
isAReactant
Reaction
isAProduct
Species
isAReactant
Reaction
isAProduct
Species
isAReactant
Reaction
Species
Establishes
a further relationship
between two species
Could even supplement
Database
Species1 PathTo Species2

50. Database as analytic device
CHEMKIN
Mechanism
Species are labels:
Only know atomic composition
(NASA polynomial)
Not structure
CHEMKIN
Mechanism
C3H7
N-C3H7
i-C3H7
Reactions
(asProduct)
Reactions
(asReactant)
Reactions
(asProduct)
Reactions
(asReactant)
Reactions
(asProduct)
Reactions
(asReactant)
Compare
reactions
(species as isomers)
The set with the most similarities:
wins

51. Database as analytic device
Reactions
(asProduct)
Reactions
(asReactant)
Reactions
(asProduct)
Reactions
(asReactant)
The set with the most similarities:
wins
C3H7 N-C3H7
A new relationship
can be established
For the cautious:
The relationship can be qualified
With a probability
(related to degree of matching)
For more certainty:
One can extend the comparison through
A larger network
(path through two or more reactions)

52. If one of the mechanisms is automatically generated
Then have the 2D structure
The species goes from a ‘label’
to a
Species with a structure
(can be further classified with substructures)
Database as analytic device

55. Account Sign in:
Query:
Which data do you have access to
Data input:
How is your data shared
Security
Inhibit hacking Social media concepts: groups
Each data point has sharing and ownership parameters

56. Transactions:
How who and when was the data entered (or analysed)
How was the database used: which queries
Why?
Have to filter query results are shown and order them
Both personal and in general
General Field (computer science):
Recommendation Systems
Each google search (from different people) gives different results
eCommerce sites use this to

57. Some basic functionality is present:
Reading in CHEMKIN mechanisms from many sources
Management of RDFs
Simple Query (single keyword search)
Data Sources:
Automatic generated mechanisms (mechanism)
Data behind automatic generation (reaction classes, 2-D (sub)structures)
Independent thermodynamic data
Computational chemistry results
Query
More complex searches
multiple keywords
interpretation/preprocessing of keyword expression before search
Ordering and filtering results (passive and with check boxes)

58. See you there!
If the gods of the internet
(and the demon - ’demo effect’)
allows,
you can try it out