Lightweight Data Markup Language Applications

Sayandeep KhanDrakoon Aerospace

Invention ReportPublic Release

May 27 2012

Contents

→LDML⬔ Review⬔ Example

→Multidimensional Data⬔ Dead Ends⬔ Translation : Description guided action

→Multiple Data Source⬔ Coupling

→Metadata⬔ Basics⬔ Application : Machine guided investigation

LDML Review

Lightweight Data Markup Language UTF-8 Multiple Nodes in One Document Strict Inter-sentence Relation

A graph can be formed

LDML Example

Example Data

River R Channel Size S

c

Carriage Capacity C

c

Actual Carriage Ac

Slit Capacity Ss

Slit Load SL

Vegetation Vg

Rainfall P

Corresponding Tree

R P

Sc

CC

Ac

Ss

SL

Vg

Ef

LDML Example

⬔ In the last Model: Orange : Attribute Relation, i.e. R>(S

c, A

c, S

s, C

c)

Yellow : Detemination Relation, i.e. Sc > C

c>C

s

Notice Sc uniquely determines the other two. It is possible to

arrive at Cc and C

s from just S

c, along a single branch of the

tree, whose root is known. This is dead-end, and can be removed. Just save S

c.

⬔ Actuall Carriage however depends on Rainfall, and External factors E

f. E

f may be unknown. Therefore this is not

Dead end

⬔ Remove Deadends to improve storage!!!

Description Guided Action

⬔ Take the statements: R>(Sc, A

c, S

s, C

c,S

L) and S

c >C

c> S

s

⬔ 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, A

c, S

s, are defined. S

c >C

c, therefore can

calculate Cc. S

c >C

c>S

s , therefore, delete S

s .(A

c ,V

g,E

f)>S

L.

Therefore either define SL or define (V

g ,E

f)

Multiple Data Sources

⬔ The data on river and vegetation are from different sources (surveys).

⬔ Connected by relations

⬔ Clustering of Datasets

Metadata - Basics

⬔ Describes a Dataset.▣ Assume Dataset River

→ Channel size→ Carriage capacity

→ Slit capacity→ Actuall Carriage→ Slit Load

⬔ Metadata is :R>(S

c,C

c,S

s,A

c,S

L)

Sc>C

c>S

s

⬔ Unique relations, regardless to variable name

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

Metadata Comparison: example

⬔ Orange : Attributes, value: a, Yellow: Deduction, value: b. Both graphs are isomorphic → same dataset, A = p, etc

A

B

C

F

E

D

p

r

q

u

t

s

Dataset 1Dataset 2

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