Computing with Directed Labeled Graphs

Marko Rodriguez
Marko RodriguezFounder at RReduX, Inc.
Computing with Directed Labeled Graphs Marko A. Rodriguez Los Alamos National Laboratory Vrije Universiteit Brussel University of California at Santa Cruz [email_address] http://www.soe.ucsc.edu/~okram
Mini-CV. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
My infrastructure. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Main projects. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Digital Library Research and Prototyping Team
The history of this talk. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline. ,[object Object],[object Object],[object Object],[object Object]
Outline. ,[object Object],[object Object],[object Object],[object Object]
What is a computer? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Turing machine. ,[object Object],[object Object],A. M. Turing. On computable numbers, with an application to the entscheidungsproblem. Proceedings of the London Mathematical Society, 42(2):230–265, 1937.
Turing completeness. ,[object Object],[object Object],[object Object],[object Object]
The Von Neumann architecture. ,[object Object],[object Object],[object Object],[object Object],J. von Neumann. The principles of large-scale computing machines. IEEE Annals of the History of Computing, 10(4):243–256, 1988 Processor ( M* ) Data ( D_M ) Instructions ( M ) Memory ( D )
What is in memory? ,[object Object],[object Object],Memory Data: Integer, Float, Memory Address, etc. opcode Instruction: add, subtract, goto 1  0  1  1  1  1  1  0  1  0  1  0  1  1  1  1  1  0  0  0  0  0  0  0  0  0  1  1  1  1  1  0
What are the types of data? ,[object Object],[object Object],[object Object],[object Object],[object Object],* This is not the standard two’s complement convention.   16 8 4 2 1   * ASCII 7-bit standard for representing characters.
What are the types of instructions? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A  D  D 7 43 1  0  1  1  1  1  1  0  1  0  1  0  1  1  1  1  1  0  0  0  0  0  0  0  0  0  1  1  1  1  1  0
How does a processor compute? ,[object Object],[object Object],Data ( D_M ) Instructions ( M ) Memory ( D ) 0 1 2 3 4 5 6 7 8 9 10 load 7 0 load 8 1 add 0 1 2 store 2 7 goto 0 noop 1..2..3..4..5.. 1 PC Processor ( M* ) 0 1 2 3 registers * Note that memory does not represent characters, just 0 or 1.   ALU
Virtual computing machines. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Programming patterns through the ages. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],* Note that all patterns are ultimately represented as lists of instructions in memory.
Object orientation and its relationship to a network. ,[object Object],[object Object],[object Object],marko johan hasFriend hasPaycode $10,000 0000 hasAmount
Outline. ,[object Object],[object Object],[object Object],[object Object]
The undirected network. ,[object Object],[object Object],[object Object],[object Object],[object Object],i j
Example undirected network. Herbert Marko Aric Ed Zhiwu Alberto Jen Johan Luda Stephan Whenzong
The directed network. ,[object Object],[object Object],[object Object],i j
Example directed network. Muskrat Bear Fish Fox Meerkat Lion Human Wolf Deer Beetle Hyena
The semantic network. ,[object Object],[object Object],[object Object],i j s
Example semantic network. SantaFe Marko NewMexico Ryan California UnitedStates LANL livesIn worksWith cityOf originallyFrom stateOf stateOf locatedIn hasLab Cells Atoms madeOf madeOf researches Oregon southOf hasResident Arnold governerOf northOf
Modeling computing data structures with a network. ,[object Object],[object Object],[object Object],[object Object]
A network analog to the Turing model. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],M. A. Rodriguez and J. Bollen. Modeling computations in a semantic network substrate. in review at International Journal of Semantic Computing, LA-UR-07-3678, 2007.
Network representations of the various software patterns. 7 0 load load 8 1 add 0 1 2 store 8 1 someProcedure opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst someObject hasBody hasBody hasMethod List of Instructions Procedure Object someProcedure
Objects and their relationship to each other and  their methods. 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst charges marko hasBody hasMethod 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst addMoney 0000 hasBody hasMethod 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst angry johan hasBody hasFriend hasPaycode hasMethod $10,000 hasAmount * Though not represented, each method should have different instructions.
A virtual machine at its relationship to instructions. PC (current instruction) Method variables LIFO Stack
Physics and its relationship to the virtual machine. M. A. Rodriguez. General-purpose computing on a semantic network substrate. accepted with revisions at Journal of Web Semantics, LA-UR-07-2885, 2007. * Not for the faint of heart. * Ultimately, the only true “computer” is physics. All computing representations must be grounded in physics.
Mapping a semantic network to an undirected network. A computing infrastructure can be represented by dots and lines. M. A. Rodriguez. Mapping Semantic Networks to Undirected Networks. in review at International Journal of Applied Mathematics and Computer Science, LA-UR-07-5287, 2007.
Obviously a network can represent computer instructions and virtual machines. ,[object Object],[object Object],[object Object],[object Object]
Outline. ,[object Object],[object Object],[object Object],[object Object]
A standardized semantic network data model. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],T. Berners-Lee and J. Hendler. Publishing on the Semantic Web. Nature, 410(6832):1023–1024, April 2001.
Triple store technology. SELECT ?a ?c WHERE  { ?a type human ?a wrote ?b  ?b type article  ?c wrote ?b  ?c type human  ?a != ?c } ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Triple store vs. relational database Triple store Relational Database SQL Interface SPARQL Interface SELECT (?x4) WHERE {  ?x1 dc:creator lanl:LAUR-06-2139. ?x1 lanl:hasFriend ?x2 . ?x2 lanl:worksFor ?x3 . ?x3 lanl:collaboratesWith ?x4 .  ?x4 lanl:hasEmployee ?x1 . } SELECT collaboratesWithTable.ordId2  FROM personTable, authorTable, articleTable, friendTable,  hasEmployeeTable, organizationTable, worksForTable, collaboratesWithTable WHERE personTable.id = authorTable.personId AND authorTable.articleId = "dc:creator LAUR-06-2139" AND personTable.id = friendTable.personId1 AND friendTable.personId2 = worksForTable.personId AND worksForTable.orgId = collaboratesWithTable.orgId2 AND collaboratesWithTable.ordId2 = personTable.id
A distributed semantic network data model. 127.0.0.1 127.0.0.5 127.0.0.2 127.0.0.3 127.0.0.6 127.0.0.4
An RDF program. <http://neno.lanl.gov/instance#42a65d00-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov/demo#Example> . <http://neno.lanl.gov/instance#42a65d00-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasMethod> <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov/demo#130ec6a7-8f0a-4f49-adec-b399c849bb9b> . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasArgumentDescriptor> <http://neno.lanl.gov/instance#42a65d03-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d03-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#ArgumentDescriptor> . <http://neno.lanl.gov/instance#42a65d03-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#_a0> <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Argument> . <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasSymbol> &quot;n&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasType> &quot;http://www.w3.org/2001/XMLSchema#integer . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasMethodName> &quot;test&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasBlock> <http://neno.lanl.gov/instance#42a65d04-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d04-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Block> . <http://neno.lanl.gov/instance#42a65d04-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Set> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasLeft> <http://neno.lanl.gov/instance#42a65d06-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d06-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LocalVariable> . <http://neno.lanl.gov/instance#42a65d06-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasSymbol> &quot;n&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasRight> <http://neno.lanl.gov/instance#42a65d07-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d07-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#PopLiteral> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d08-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d08-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Block> . <http://neno.lanl.gov/instance#42a65d08-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#PushValue> . <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasValue> <http://neno.lanl.gov/instance#42a65d0a-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0a-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LocalDirect> . <http://neno.lanl.gov/instance#42a65d0a-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasURI> &quot;0&quot;^^<http://www.w3.org/2001/XMLSchema#integer> . <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Set> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasLeft> <http://neno.lanl.gov/instance#42a65d0c-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0c-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LocalVariable> . <http://neno.lanl.gov/instance#42a65d0c-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasSymbol> &quot;i&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasRight> <http://neno.lanl.gov/instance#42a65d0d-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0d-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#PopLiteral> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d0e-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0e-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LessThan> . <http://neno.lanl.gov/instance#42a65d0e-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasLeft> <http://neno.lanl.gov/instance#42a65d0f-71c4-11dc-96bb-000014095701> . … .. .
Open computing. ,[object Object],[object Object],[object Object],[object Object],M. A. Rodriguez and J. Shinavier. The RDF Virtual Machine. in review at 2008 World Wide Web Conference, Beijing, China, 2007.
Distributed computing. ,[object Object],[object Object],R/T : Virtual Machine and Stored Program  D? : Data
Reflective computing. ,[object Object],[object Object],[object Object],[object Object]
A new level of abstraction. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline. ,[object Object],[object Object],[object Object],[object Object]
Future research objectives. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],V. B. Mountcastle. An organizing principle for cerebral function: the unit model and the distributed system.  In G. Edelman and V. Mountcastle, editors, Mindful Brain: Cortical Organization and the Group-Selective Theory of Higher Brain Function. MIT Press, Cambridge, Mass., 1978.
1 of 45

Recommended

Automatic Metadata Generation using Associative Networks by
Automatic Metadata Generation using Associative NetworksAutomatic Metadata Generation using Associative Networks
Automatic Metadata Generation using Associative NetworksMarko Rodriguez
1.2K views28 slides
A Model of the Scholarly Community by
A Model of the Scholarly CommunityA Model of the Scholarly Community
A Model of the Scholarly CommunityMarko Rodriguez
1.3K views22 slides
Predicting the relevance of search results for e-commerce systems by
Predicting the relevance of search results for e-commerce systemsPredicting the relevance of search results for e-commerce systems
Predicting the relevance of search results for e-commerce systemsUniversiti Technologi Malaysia (UTM)
984 views23 slides
SF Python Meetup: TextRank in Python by
SF Python Meetup: TextRank in PythonSF Python Meetup: TextRank in Python
SF Python Meetup: TextRank in PythonPaco Nathan
5.7K views24 slides
A Fast and Dirty Intro to NetworkX (and D3) by
A Fast and Dirty Intro to NetworkX (and D3)A Fast and Dirty Intro to NetworkX (and D3)
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
45.3K views45 slides
A Primer on Entity Resolution by
A Primer on Entity ResolutionA Primer on Entity Resolution
A Primer on Entity ResolutionBenjamin Bengfort
14.4K views58 slides

More Related Content

What's hot

R-programming-training-in-mumbai by
R-programming-training-in-mumbaiR-programming-training-in-mumbai
R-programming-training-in-mumbaiUnmesh Baile
450 views54 slides
What's next in Julia by
What's next in JuliaWhat's next in Julia
What's next in JuliaJiahao Chen
2.2K views62 slides
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ... by
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Jimmy Lai
29.6K views19 slides
DB and IR Integration by
DB and IR IntegrationDB and IR Integration
DB and IR IntegrationMarco A Torres
1.5K views41 slides
Introduction to the R Statistical Computing Environment by
Introduction to the R Statistical Computing EnvironmentIntroduction to the R Statistical Computing Environment
Introduction to the R Statistical Computing Environmentizahn
5.1K views50 slides
Understanding WeboNaver by
Understanding WeboNaverUnderstanding WeboNaver
Understanding WeboNaverHan Woo PARK
874 views27 slides

What's hot(20)

R-programming-training-in-mumbai by Unmesh Baile
R-programming-training-in-mumbaiR-programming-training-in-mumbai
R-programming-training-in-mumbai
Unmesh Baile450 views
What's next in Julia by Jiahao Chen
What's next in JuliaWhat's next in Julia
What's next in Julia
Jiahao Chen2.2K views
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ... by Jimmy Lai
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...
Jimmy Lai29.6K views
Introduction to the R Statistical Computing Environment by izahn
Introduction to the R Statistical Computing EnvironmentIntroduction to the R Statistical Computing Environment
Introduction to the R Statistical Computing Environment
izahn5.1K views
Understanding WeboNaver by Han Woo PARK
Understanding WeboNaverUnderstanding WeboNaver
Understanding WeboNaver
Han Woo PARK874 views
Graph Libraries - Overview on Networkx by 鈺棻 曾
Graph Libraries - Overview on NetworkxGraph Libraries - Overview on Networkx
Graph Libraries - Overview on Networkx
鈺棻 曾787 views
DB-IR-ranking by FELIX75
DB-IR-rankingDB-IR-ranking
DB-IR-ranking
FELIX75908 views
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track by Bhaskar Mitra
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackConformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track
Bhaskar Mitra110 views
Named Entity Recognition from Online News by Bernardo Najlis
Named Entity Recognition from Online NewsNamed Entity Recognition from Online News
Named Entity Recognition from Online News
Bernardo Najlis2.1K views
Duet @ TREC 2019 Deep Learning Track by Bhaskar Mitra
Duet @ TREC 2019 Deep Learning TrackDuet @ TREC 2019 Deep Learning Track
Duet @ TREC 2019 Deep Learning Track
Bhaskar Mitra107 views
Stacks in algorithems & data structure by faran nawaz
Stacks in algorithems & data structureStacks in algorithems & data structure
Stacks in algorithems & data structure
faran nawaz1.7K views
R programming & Machine Learning by AmanBhalla14
R programming & Machine LearningR programming & Machine Learning
R programming & Machine Learning
AmanBhalla14676 views
Detection of Related Semantic Datasets Based on Frequent Subgraph Mining by Mikel Emaldi Manrique
Detection of Related Semantic Datasets Based on Frequent Subgraph MiningDetection of Related Semantic Datasets Based on Frequent Subgraph Mining
Detection of Related Semantic Datasets Based on Frequent Subgraph Mining
Text Mining using LDA with Context by Steffen Staab
Text Mining using LDA with ContextText Mining using LDA with Context
Text Mining using LDA with Context
Steffen Staab2.2K views
Joey gonzalez, graph lab, m lconf 2013 by MLconf
Joey gonzalez, graph lab, m lconf 2013Joey gonzalez, graph lab, m lconf 2013
Joey gonzalez, graph lab, m lconf 2013
MLconf3.2K views
Adversarial and reinforcement learning-based approaches to information retrieval by Bhaskar Mitra
Adversarial and reinforcement learning-based approaches to information retrievalAdversarial and reinforcement learning-based approaches to information retrieval
Adversarial and reinforcement learning-based approaches to information retrieval
Bhaskar Mitra703 views

Viewers also liked

Traversing Graph Databases with Gremlin by
Traversing Graph Databases with GremlinTraversing Graph Databases with Gremlin
Traversing Graph Databases with GremlinMarko Rodriguez
8.2K views26 slides
Gremlin: A Graph-Based Programming Language by
Gremlin: A Graph-Based Programming LanguageGremlin: A Graph-Based Programming Language
Gremlin: A Graph-Based Programming LanguageMarko Rodriguez
28.7K views60 slides
Gremlin's Graph Traversal Machinery by
Gremlin's Graph Traversal MachineryGremlin's Graph Traversal Machinery
Gremlin's Graph Traversal MachineryMarko Rodriguez
8.3K views85 slides
The Gremlin Graph Traversal Language by
The Gremlin Graph Traversal LanguageThe Gremlin Graph Traversal Language
The Gremlin Graph Traversal LanguageMarko Rodriguez
21.4K views50 slides
Graph Databases: Trends in the Web of Data by
Graph Databases: Trends in the Web of DataGraph Databases: Trends in the Web of Data
Graph Databases: Trends in the Web of DataMarko Rodriguez
58.1K views129 slides
PhD Dissertation Powerpoint by
PhD Dissertation PowerpointPhD Dissertation Powerpoint
PhD Dissertation PowerpointHemal Mehta
13.8K views60 slides

Viewers also liked(7)

Traversing Graph Databases with Gremlin by Marko Rodriguez
Traversing Graph Databases with GremlinTraversing Graph Databases with Gremlin
Traversing Graph Databases with Gremlin
Marko Rodriguez8.2K views
Gremlin: A Graph-Based Programming Language by Marko Rodriguez
Gremlin: A Graph-Based Programming LanguageGremlin: A Graph-Based Programming Language
Gremlin: A Graph-Based Programming Language
Marko Rodriguez28.7K views
Gremlin's Graph Traversal Machinery by Marko Rodriguez
Gremlin's Graph Traversal MachineryGremlin's Graph Traversal Machinery
Gremlin's Graph Traversal Machinery
Marko Rodriguez8.3K views
The Gremlin Graph Traversal Language by Marko Rodriguez
The Gremlin Graph Traversal LanguageThe Gremlin Graph Traversal Language
The Gremlin Graph Traversal Language
Marko Rodriguez21.4K views
Graph Databases: Trends in the Web of Data by Marko Rodriguez
Graph Databases: Trends in the Web of DataGraph Databases: Trends in the Web of Data
Graph Databases: Trends in the Web of Data
Marko Rodriguez58.1K views
PhD Dissertation Powerpoint by Hemal Mehta
PhD Dissertation PowerpointPhD Dissertation Powerpoint
PhD Dissertation Powerpoint
Hemal Mehta13.8K views
Shewhart, 6-Sigma and snowflake-men by Maxim Dorofeev
Shewhart, 6-Sigma and snowflake-menShewhart, 6-Sigma and snowflake-men
Shewhart, 6-Sigma and snowflake-men
Maxim Dorofeev46.2K views

Similar to Computing with Directed Labeled Graphs

Chap10.ppt by
Chap10.pptChap10.ppt
Chap10.pptssuser0d0f881
4 views16 slides
GATE, HLT and Machine Learning, Sheffield, July 2003 by
GATE, HLT and Machine Learning, Sheffield, July 2003GATE, HLT and Machine Learning, Sheffield, July 2003
GATE, HLT and Machine Learning, Sheffield, July 2003butest
521 views30 slides
In 2014 Almost Everyone Has Some Kind Of Computer, Whether... by
In 2014 Almost Everyone Has Some Kind Of Computer, Whether...In 2014 Almost Everyone Has Some Kind Of Computer, Whether...
In 2014 Almost Everyone Has Some Kind Of Computer, Whether...Katie Parker
2 views85 slides
Антон Кириллов, ZeptoLab by
Антон Кириллов, ZeptoLabАнтон Кириллов, ZeptoLab
Антон Кириллов, ZeptoLabDiana Dymolazova
1.5K views44 slides
On being a professional software developer by
On being a professional software developerOn being a professional software developer
On being a professional software developerAnton Kirillov
3.1K views44 slides
Evolving as a professional software developer by
Evolving as a professional software developerEvolving as a professional software developer
Evolving as a professional software developerAnton Kirillov
985 views44 slides

Similar to Computing with Directed Labeled Graphs(20)

GATE, HLT and Machine Learning, Sheffield, July 2003 by butest
GATE, HLT and Machine Learning, Sheffield, July 2003GATE, HLT and Machine Learning, Sheffield, July 2003
GATE, HLT and Machine Learning, Sheffield, July 2003
butest521 views
In 2014 Almost Everyone Has Some Kind Of Computer, Whether... by Katie Parker
In 2014 Almost Everyone Has Some Kind Of Computer, Whether...In 2014 Almost Everyone Has Some Kind Of Computer, Whether...
In 2014 Almost Everyone Has Some Kind Of Computer, Whether...
Katie Parker2 views
Антон Кириллов, ZeptoLab by Diana Dymolazova
Антон Кириллов, ZeptoLabАнтон Кириллов, ZeptoLab
Антон Кириллов, ZeptoLab
Diana Dymolazova1.5K views
On being a professional software developer by Anton Kirillov
On being a professional software developerOn being a professional software developer
On being a professional software developer
Anton Kirillov3.1K views
Evolving as a professional software developer by Anton Kirillov
Evolving as a professional software developerEvolving as a professional software developer
Evolving as a professional software developer
Anton Kirillov985 views
Models vs Reality: Quest for the Roots of Complexity by Julian Warszawski
Models vs Reality: Quest for the Roots of ComplexityModels vs Reality: Quest for the Roots of Complexity
Models vs Reality: Quest for the Roots of Complexity
An Effective Machine Learning Model by Diana Walker
An Effective Machine Learning ModelAn Effective Machine Learning Model
An Effective Machine Learning Model
Diana Walker3 views
cis97003 by perfj
cis97003cis97003
cis97003
perfj71 views
Information security and programming language s C by IJRES Journal
Information security and programming language s CInformation security and programming language s C
Information security and programming language s C
IJRES Journal311 views
number system understand by rickypatel151
number system  understandnumber system  understand
number system understand
rickypatel1511.1K views
Introduction to Data Structure by Prof Ansari
Introduction to Data Structure Introduction to Data Structure
Introduction to Data Structure
Prof Ansari831 views
M tech published paper by Anand Sharma
M tech published paperM tech published paper
M tech published paper
Anand Sharma101 views
BIT204 1 Software Fundamentals by James Uren
BIT204 1 Software FundamentalsBIT204 1 Software Fundamentals
BIT204 1 Software Fundamentals
James Uren627 views
0 introduction to computer architecture by aamc1100
0 introduction to computer architecture0 introduction to computer architecture
0 introduction to computer architecture
aamc11005.8K views
Notes On Computer Science Course by Jessica Simms
Notes On Computer Science CourseNotes On Computer Science Course
Notes On Computer Science Course
Jessica Simms2 views

More from Marko Rodriguez

mm-ADT: A Virtual Machine/An Economic Machine by
mm-ADT: A Virtual Machine/An Economic Machinemm-ADT: A Virtual Machine/An Economic Machine
mm-ADT: A Virtual Machine/An Economic MachineMarko Rodriguez
3.5K views62 slides
mm-ADT: A Multi-Model Abstract Data Type by
mm-ADT: A Multi-Model Abstract Data Typemm-ADT: A Multi-Model Abstract Data Type
mm-ADT: A Multi-Model Abstract Data TypeMarko Rodriguez
1.8K views66 slides
Open Problems in the Universal Graph Theory by
Open Problems in the Universal Graph TheoryOpen Problems in the Universal Graph Theory
Open Problems in the Universal Graph TheoryMarko Rodriguez
2.3K views106 slides
Gremlin 101.3 On Your FM Dial by
Gremlin 101.3 On Your FM DialGremlin 101.3 On Your FM Dial
Gremlin 101.3 On Your FM DialMarko Rodriguez
4.5K views191 slides
Quantum Processes in Graph Computing by
Quantum Processes in Graph ComputingQuantum Processes in Graph Computing
Quantum Processes in Graph ComputingMarko Rodriguez
8.6K views142 slides
ACM DBPL Keynote: The Graph Traversal Machine and Language by
ACM DBPL Keynote: The Graph Traversal Machine and LanguageACM DBPL Keynote: The Graph Traversal Machine and Language
ACM DBPL Keynote: The Graph Traversal Machine and LanguageMarko Rodriguez
9.4K views140 slides

More from Marko Rodriguez(20)

mm-ADT: A Virtual Machine/An Economic Machine by Marko Rodriguez
mm-ADT: A Virtual Machine/An Economic Machinemm-ADT: A Virtual Machine/An Economic Machine
mm-ADT: A Virtual Machine/An Economic Machine
Marko Rodriguez3.5K views
mm-ADT: A Multi-Model Abstract Data Type by Marko Rodriguez
mm-ADT: A Multi-Model Abstract Data Typemm-ADT: A Multi-Model Abstract Data Type
mm-ADT: A Multi-Model Abstract Data Type
Marko Rodriguez1.8K views
Open Problems in the Universal Graph Theory by Marko Rodriguez
Open Problems in the Universal Graph TheoryOpen Problems in the Universal Graph Theory
Open Problems in the Universal Graph Theory
Marko Rodriguez2.3K views
Gremlin 101.3 On Your FM Dial by Marko Rodriguez
Gremlin 101.3 On Your FM DialGremlin 101.3 On Your FM Dial
Gremlin 101.3 On Your FM Dial
Marko Rodriguez4.5K views
Quantum Processes in Graph Computing by Marko Rodriguez
Quantum Processes in Graph ComputingQuantum Processes in Graph Computing
Quantum Processes in Graph Computing
Marko Rodriguez8.6K views
ACM DBPL Keynote: The Graph Traversal Machine and Language by Marko Rodriguez
ACM DBPL Keynote: The Graph Traversal Machine and LanguageACM DBPL Keynote: The Graph Traversal Machine and Language
ACM DBPL Keynote: The Graph Traversal Machine and Language
Marko Rodriguez9.4K views
Faunus: Graph Analytics Engine by Marko Rodriguez
Faunus: Graph Analytics EngineFaunus: Graph Analytics Engine
Faunus: Graph Analytics Engine
Marko Rodriguez9.5K views
Solving Problems with Graphs by Marko Rodriguez
Solving Problems with GraphsSolving Problems with Graphs
Solving Problems with Graphs
Marko Rodriguez31.3K views
Titan: The Rise of Big Graph Data by Marko Rodriguez
Titan: The Rise of Big Graph DataTitan: The Rise of Big Graph Data
Titan: The Rise of Big Graph Data
Marko Rodriguez170.5K views
The Pathology of Graph Databases by Marko Rodriguez
The Pathology of Graph DatabasesThe Pathology of Graph Databases
The Pathology of Graph Databases
Marko Rodriguez6.3K views
Memoirs of a Graph Addict: Despair to Redemption by Marko Rodriguez
Memoirs of a Graph Addict: Despair to RedemptionMemoirs of a Graph Addict: Despair to Redemption
Memoirs of a Graph Addict: Despair to Redemption
Marko Rodriguez8K views
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco... by Marko Rodriguez
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
Marko Rodriguez66.9K views
A Perspective on Graph Theory and Network Science by Marko Rodriguez
A Perspective on Graph Theory and Network ScienceA Perspective on Graph Theory and Network Science
A Perspective on Graph Theory and Network Science
Marko Rodriguez4.7K views
The Graph Traversal Programming Pattern by Marko Rodriguez
The Graph Traversal Programming PatternThe Graph Traversal Programming Pattern
The Graph Traversal Programming Pattern
Marko Rodriguez56.3K views
The Network Data Structure in Computing by Marko Rodriguez
The Network Data Structure in ComputingThe Network Data Structure in Computing
The Network Data Structure in Computing
Marko Rodriguez8.6K views
General-Purpose, Internet-Scale Distributed Computing with Linked Process by Marko Rodriguez
General-Purpose, Internet-Scale Distributed Computing with Linked ProcessGeneral-Purpose, Internet-Scale Distributed Computing with Linked Process
General-Purpose, Internet-Scale Distributed Computing with Linked Process
Marko Rodriguez2.1K views
Collective Decision Making Systems: From the Ideal State to Human Eudaimonia by Marko Rodriguez
Collective Decision Making Systems: From the Ideal State to Human EudaimoniaCollective Decision Making Systems: From the Ideal State to Human Eudaimonia
Collective Decision Making Systems: From the Ideal State to Human Eudaimonia
Marko Rodriguez1.7K views

Recently uploaded

How the World's Leading Independent Automotive Distributor is Reinventing Its... by
How the World's Leading Independent Automotive Distributor is Reinventing Its...How the World's Leading Independent Automotive Distributor is Reinventing Its...
How the World's Leading Independent Automotive Distributor is Reinventing Its...NUS-ISS
15 views25 slides
Java Platform Approach 1.0 - Picnic Meetup by
Java Platform Approach 1.0 - Picnic MeetupJava Platform Approach 1.0 - Picnic Meetup
Java Platform Approach 1.0 - Picnic MeetupRick Ossendrijver
25 views39 slides
PharoJS - Zürich Smalltalk Group Meetup November 2023 by
PharoJS - Zürich Smalltalk Group Meetup November 2023PharoJS - Zürich Smalltalk Group Meetup November 2023
PharoJS - Zürich Smalltalk Group Meetup November 2023Noury Bouraqadi
120 views17 slides
Tunable Laser (1).pptx by
Tunable Laser (1).pptxTunable Laser (1).pptx
Tunable Laser (1).pptxHajira Mahmood
23 views37 slides
Special_edition_innovator_2023.pdf by
Special_edition_innovator_2023.pdfSpecial_edition_innovator_2023.pdf
Special_edition_innovator_2023.pdfWillDavies22
16 views6 slides
Melek BEN MAHMOUD.pdf by
Melek BEN MAHMOUD.pdfMelek BEN MAHMOUD.pdf
Melek BEN MAHMOUD.pdfMelekBenMahmoud
14 views1 slide

Recently uploaded(20)

How the World's Leading Independent Automotive Distributor is Reinventing Its... by NUS-ISS
How the World's Leading Independent Automotive Distributor is Reinventing Its...How the World's Leading Independent Automotive Distributor is Reinventing Its...
How the World's Leading Independent Automotive Distributor is Reinventing Its...
NUS-ISS15 views
PharoJS - Zürich Smalltalk Group Meetup November 2023 by Noury Bouraqadi
PharoJS - Zürich Smalltalk Group Meetup November 2023PharoJS - Zürich Smalltalk Group Meetup November 2023
PharoJS - Zürich Smalltalk Group Meetup November 2023
Noury Bouraqadi120 views
Special_edition_innovator_2023.pdf by WillDavies22
Special_edition_innovator_2023.pdfSpecial_edition_innovator_2023.pdf
Special_edition_innovator_2023.pdf
WillDavies2216 views
Voice Logger - Telephony Integration Solution at Aegis by Nirmal Sharma
Voice Logger - Telephony Integration Solution at AegisVoice Logger - Telephony Integration Solution at Aegis
Voice Logger - Telephony Integration Solution at Aegis
Nirmal Sharma17 views
Web Dev - 1 PPT.pdf by gdsczhcet
Web Dev - 1 PPT.pdfWeb Dev - 1 PPT.pdf
Web Dev - 1 PPT.pdf
gdsczhcet55 views
Upskilling the Evolving Workforce with Digital Fluency for Tomorrow's Challen... by NUS-ISS
Upskilling the Evolving Workforce with Digital Fluency for Tomorrow's Challen...Upskilling the Evolving Workforce with Digital Fluency for Tomorrow's Challen...
Upskilling the Evolving Workforce with Digital Fluency for Tomorrow's Challen...
NUS-ISS28 views
Business Analyst Series 2023 - Week 3 Session 5 by DianaGray10
Business Analyst Series 2023 -  Week 3 Session 5Business Analyst Series 2023 -  Week 3 Session 5
Business Analyst Series 2023 - Week 3 Session 5
DianaGray10209 views
.conf Go 2023 - Data analysis as a routine by Splunk
.conf Go 2023 - Data analysis as a routine.conf Go 2023 - Data analysis as a routine
.conf Go 2023 - Data analysis as a routine
Splunk93 views
AMAZON PRODUCT RESEARCH.pdf by JerikkLaureta
AMAZON PRODUCT RESEARCH.pdfAMAZON PRODUCT RESEARCH.pdf
AMAZON PRODUCT RESEARCH.pdf
JerikkLaureta15 views
Transcript: The Details of Description Techniques tips and tangents on altern... by BookNet Canada
Transcript: The Details of Description Techniques tips and tangents on altern...Transcript: The Details of Description Techniques tips and tangents on altern...
Transcript: The Details of Description Techniques tips and tangents on altern...
BookNet Canada130 views
handbook for web 3 adoption.pdf by Liveplex
handbook for web 3 adoption.pdfhandbook for web 3 adoption.pdf
handbook for web 3 adoption.pdf
Liveplex19 views
Emerging & Future Technology - How to Prepare for the Next 10 Years of Radica... by NUS-ISS
Emerging & Future Technology - How to Prepare for the Next 10 Years of Radica...Emerging & Future Technology - How to Prepare for the Next 10 Years of Radica...
Emerging & Future Technology - How to Prepare for the Next 10 Years of Radica...
NUS-ISS16 views
The Importance of Cybersecurity for Digital Transformation by NUS-ISS
The Importance of Cybersecurity for Digital TransformationThe Importance of Cybersecurity for Digital Transformation
The Importance of Cybersecurity for Digital Transformation
NUS-ISS27 views
Architecting CX Measurement Frameworks and Ensuring CX Metrics are fit for Pu... by NUS-ISS
Architecting CX Measurement Frameworks and Ensuring CX Metrics are fit for Pu...Architecting CX Measurement Frameworks and Ensuring CX Metrics are fit for Pu...
Architecting CX Measurement Frameworks and Ensuring CX Metrics are fit for Pu...
NUS-ISS37 views
Five Things You SHOULD Know About Postman by Postman
Five Things You SHOULD Know About PostmanFive Things You SHOULD Know About Postman
Five Things You SHOULD Know About Postman
Postman27 views

Computing with Directed Labeled Graphs

  • 1. Computing with Directed Labeled Graphs Marko A. Rodriguez Los Alamos National Laboratory Vrije Universiteit Brussel University of California at Santa Cruz [email_address] http://www.soe.ucsc.edu/~okram
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21. Example undirected network. Herbert Marko Aric Ed Zhiwu Alberto Jen Johan Luda Stephan Whenzong
  • 22.
  • 23. Example directed network. Muskrat Bear Fish Fox Meerkat Lion Human Wolf Deer Beetle Hyena
  • 24.
  • 25. Example semantic network. SantaFe Marko NewMexico Ryan California UnitedStates LANL livesIn worksWith cityOf originallyFrom stateOf stateOf locatedIn hasLab Cells Atoms madeOf madeOf researches Oregon southOf hasResident Arnold governerOf northOf
  • 26.
  • 27.
  • 28. Network representations of the various software patterns. 7 0 load load 8 1 add 0 1 2 store 8 1 someProcedure opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst someObject hasBody hasBody hasMethod List of Instructions Procedure Object someProcedure
  • 29. Objects and their relationship to each other and their methods. 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst charges marko hasBody hasMethod 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst addMoney 0000 hasBody hasMethod 7 0 load load 8 1 add 0 1 2 store 8 1 opA opB opA opA opA opB opB opB opC nextInst nextInst nextInst nextInst angry johan hasBody hasFriend hasPaycode hasMethod $10,000 hasAmount * Though not represented, each method should have different instructions.
  • 30. A virtual machine at its relationship to instructions. PC (current instruction) Method variables LIFO Stack
  • 31. Physics and its relationship to the virtual machine. M. A. Rodriguez. General-purpose computing on a semantic network substrate. accepted with revisions at Journal of Web Semantics, LA-UR-07-2885, 2007. * Not for the faint of heart. * Ultimately, the only true “computer” is physics. All computing representations must be grounded in physics.
  • 32. Mapping a semantic network to an undirected network. A computing infrastructure can be represented by dots and lines. M. A. Rodriguez. Mapping Semantic Networks to Undirected Networks. in review at International Journal of Applied Mathematics and Computer Science, LA-UR-07-5287, 2007.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37. Triple store vs. relational database Triple store Relational Database SQL Interface SPARQL Interface SELECT (?x4) WHERE { ?x1 dc:creator lanl:LAUR-06-2139. ?x1 lanl:hasFriend ?x2 . ?x2 lanl:worksFor ?x3 . ?x3 lanl:collaboratesWith ?x4 . ?x4 lanl:hasEmployee ?x1 . } SELECT collaboratesWithTable.ordId2 FROM personTable, authorTable, articleTable, friendTable, hasEmployeeTable, organizationTable, worksForTable, collaboratesWithTable WHERE personTable.id = authorTable.personId AND authorTable.articleId = &quot;dc:creator LAUR-06-2139&quot; AND personTable.id = friendTable.personId1 AND friendTable.personId2 = worksForTable.personId AND worksForTable.orgId = collaboratesWithTable.orgId2 AND collaboratesWithTable.ordId2 = personTable.id
  • 38. A distributed semantic network data model. 127.0.0.1 127.0.0.5 127.0.0.2 127.0.0.3 127.0.0.6 127.0.0.4
  • 39. An RDF program. <http://neno.lanl.gov/instance#42a65d00-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov/demo#Example> . <http://neno.lanl.gov/instance#42a65d00-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasMethod> <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov/demo#130ec6a7-8f0a-4f49-adec-b399c849bb9b> . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasArgumentDescriptor> <http://neno.lanl.gov/instance#42a65d03-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d03-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#ArgumentDescriptor> . <http://neno.lanl.gov/instance#42a65d03-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#_a0> <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Argument> . <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasSymbol> &quot;n&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d02-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasType> &quot;http://www.w3.org/2001/XMLSchema#integer . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasMethodName> &quot;test&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d01-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasBlock> <http://neno.lanl.gov/instance#42a65d04-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d04-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Block> . <http://neno.lanl.gov/instance#42a65d04-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Set> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasLeft> <http://neno.lanl.gov/instance#42a65d06-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d06-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LocalVariable> . <http://neno.lanl.gov/instance#42a65d06-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasSymbol> &quot;n&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasRight> <http://neno.lanl.gov/instance#42a65d07-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d07-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#PopLiteral> . <http://neno.lanl.gov/instance#42a65d05-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d08-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d08-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Block> . <http://neno.lanl.gov/instance#42a65d08-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#PushValue> . <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasValue> <http://neno.lanl.gov/instance#42a65d0a-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0a-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LocalDirect> . <http://neno.lanl.gov/instance#42a65d0a-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasURI> &quot;0&quot;^^<http://www.w3.org/2001/XMLSchema#integer> . <http://neno.lanl.gov/instance#42a65d09-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#Set> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasLeft> <http://neno.lanl.gov/instance#42a65d0c-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0c-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LocalVariable> . <http://neno.lanl.gov/instance#42a65d0c-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasSymbol> &quot;i&quot;^^<http://www.w3.org/2001/XMLSchema#string> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasRight> <http://neno.lanl.gov/instance#42a65d0d-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0d-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#PopLiteral> . <http://neno.lanl.gov/instance#42a65d0b-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#nextInst> <http://neno.lanl.gov/instance#42a65d0e-71c4-11dc-96bb-000014095701> . <http://neno.lanl.gov/instance#42a65d0e-71c4-11dc-96bb-000014095701> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://neno.lanl.gov#LessThan> . <http://neno.lanl.gov/instance#42a65d0e-71c4-11dc-96bb-000014095701> <http://neno.lanl.gov#hasLeft> <http://neno.lanl.gov/instance#42a65d0f-71c4-11dc-96bb-000014095701> . … .. .
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.