1. Associative Semiotic
Hypergraph Database
We build powerful meaningful relationships easily
In Intersystems Cache with Python Pandas
By Athanassios I. Hatzis, PhD
http://healis.eu/about

2. Overview
•Our focus on the “Big” Data problem
•Our approach: A Multi-Perspective
Database Framework
•Demo of key differentiating factors
– The mapping problem
– Filtering mechanism
– Traversal
•Our objectives
•Our future plan
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3. Integration of Data Resources
•4V’s of Integration
– Veracity (Certainty of data)
– Validity (Cleansing of data)
– Variety (Diversity of data, Container)
– Variability (Semantics of data, Content)
•Extract, Transform, Load
•Enrichment
•Correlation and Aggregation
•Retrieval mechanism
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4. Time for Smart Data
•A Multi-Perspective Framework for
Multi-Model Databases
– Conceptual (Entities/Attributes)
– Logical (Hypergraph)
– Data Model (ER, Topic Maps, RDF, XML/JSON)
– Physical (Key-Value Store)
– Semantic (Triangle of Reference)
– Object-Oriented (TBox, ABox)
– Programming (Functional)
– Mathematical (Sets and Morphisms)
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5. Associative Semiotic
Hypergraph• Associative In the core
– Associative Multidimensional Arrays
– Associative Entity
• Semiotic By Design (R3/S3)
– Resources, Representation, Realization
– Signified, Sign, Signifier
– Model, Identifier, Data Values
• Hypergraph In Logic
– Hyperedge (HE – connects many hypernodes)
– Hypernode (HN – is always connected to HE)
– Hyperlink (HL – connects a pair of HE, HN)
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6. Hypergraph
• Structure
– Hyperedge (HE – connects many hypernodes)
– Hypernode (HN – is always connected to HE)
– Hyperlink (HL – connects a pair of HE, HN)
• Hyperedge Represents
– Entities, Records (Hyperbond), Tables, etc…
– One side in a Many to Many relationship
• Hypernode Represents
– Attributes, Values (Hyperatoms), Columns, etc…
– One side in a Many to Many relationship
• Hypelink Represents
– A Many to Many Binary Relationship (e.g. Supplier – Part)
• Directed, Inverse, Undirected
– Bidirectional Edge that connects a tail node to a head node
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7. Hypergraph vs Graph
•Why Hypergraph
– Natively closer to n-ary Relation
– Entity (Relation)  Hyperedge
– Attribute Instance  Hypernode
– Attribute Domain (Set)  Hypercollection
– Association  (Tuple or Entity Relationship)
– Relational DBMS Reference DBMS
•Advantages
– Easier navigation
– Better fine-grain control
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8. Demo Mapping
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9. Demo Filtering
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10. Demo Traversal
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11. Our Objectives
•Facilitate collaboration across the
Python and Intersystems Cache
developers communities
•Promote our technology to developer
communities
•Act as a bridge between vendors, their
partners and industry clients
•Distribute and apply our technology to
vendors and their customers
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12. Our Future Plan
•Development and testing of a
Minimum Viable Product
•Collaborate with our partners to build
a self-service associative, semiotic BI
system
•Make the technology open and
accessible to the general public
•Promote and apply both proprietary
and Open Source implementations
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13. Our Mantra
•We build powerful meaningful
relationships easily
•Acknowledgements
•Thank you
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