LDML is a lightweight data markup language that uses graphs and relations between nodes to represent multidimensional data from multiple sources. It allows for dead ends in the graph to be removed to improve storage efficiency. The relations and metadata can be used to guide actions like calculating missing information, comparing equivalent datasets, and coupling different datasets. LDML provides a framework for scientific manipulation and investigation of data.

1. Lightweight Data Markup Language
Applications
Sayandeep Khan
Drakoon Aerospace
Invention Report
Public Release
May 27 2012

2. Contents
→LDML
⬔ Review
⬔ Example
→Multidimensional Data
⬔ Dead Ends
⬔ Translation : Description guided action
→Multiple Data Source
⬔ Coupling
→Metadata
⬔ Basics
⬔ Application : Machine guided investigation

3. LDML Review
Lightweight Data Markup Language
UTF-8
Multiple Nodes in One Document
Strict Inter-sentence Relation
A graph can be formed

4. LDML Example
Example Data Corresponding Tree
River R R P
Channel Size Sc Ef
Carriage Capacity Cc
Vg
Actual Carriage Ac Sc
Slit Capacity Ss Ac
Slit Load SL
Vegetation Vg CC
SL
Rainfall P Ss

5. LDML Example
⬔ In the last Model:
Orange : Attribute Relation, i.e. R>(Sc, Ac, Ss, Cc)
Yellow : Detemination Relation, i.e. Sc > Cc>Cs
Notice Sc uniquely determines the other two. It is possible to
arrive at Cc and Cs from just Sc, along a single branch of the
tree, whose root is known. This is dead-end, and can be
removed. Just save Sc.
⬔ Actuall Carriage however depends on Rainfall, and
External factors Ef. Ef may be unknown. Therefore this is not
Dead end
⬔ Remove Deadends to improve storage!!!

6. Description Guided Action
⬔ Take the statements: R>(Sc, Ac, Ss, Cc,SL) and Sc >Cc> Ss
⬔ Parser must look for definitions of each attribute
→ Deduction relation, Dead end : Calculate, Stop
→ Deduction relation without dead end: Search other
variables. Alert if missing.
⬔ Example: Sc, Ac, Ss, are defined. Sc >Cc, therefore can
calculate Cc. Sc >Cc>Ss , therefore, delete Ss .(Ac ,Vg,Ef)>SL.
Therefore either define SL or define (Vg ,Ef)

7. Multiple Data Sources
⬔ The data on river and vegetation are from
different sources (surveys).
⬔ Connected by relations
⬔ Clustering of Datasets

8. Metadata - Basics
⬔ Describes a Dataset.
▣ Assume Dataset River
→ Channel size
→ Carriage capacity
→ Slit capacity
→ Actuall Carriage
→ Slit Load
⬔ Metadata is :
R>(Sc,Cc,Ss,Ac,SL)
Sc>Cc>Ss
⬔ Unique relations, regardless to variable name

9. Metadata Comparison
⬔ Graphs
→ Each element a node
→ Each relation an edge
→ Different relations have different weighted value
⬔ Equivalent Datasets
→ Isomorphic graphs
⬔ Comparison by isomorphism test

10. Metadata Comparison: example
q
A C
s
r
B
D
t
E
u
F p
Dataset 1
Dataset 2
⬔ Orange : Attributes, value: a, Yellow: Deduction,
value: b. Both graphs are isomorphic → same dataset, A
= p, etc

11. Conclusion
⬔ Using relation between attributes:
→ Remove unnecessary storage
→ Find if information missing
→ Compare datasets (explianation of variable names
not needed)
→ Couple multiple datasets
→ Calculate missing information
⬔ In future: any algebric procedure applies
⬔ LDML : Capable of scientific manipulation of Data