How To Write A Network Analysis

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FishGraph: A Network-Driven

Data Analysis

Patrícia Cavoto*, Victor Cardoso*, Régine Vignes Lebbe§, André Santanchè*

*UNICAMP – University of Campinas, São Paulo, Brasil §ISYEB - UMR 7205 – CNRS, MNHN, UPMC, EPHE UPMC Univ. Paris 06, Sorbonne Universités, Paris, France

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Outline

• Motivation

• Goal

• ReGraph: from FishBase to FishGraph

• Data Experiments

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Motivation

 Collaborative research involving:

 LIS - Laboratory of Information Systems – UNICAMP, Brazil

 MNHN - National Museum of Natural History and Sorbonne Univertès – Paris, France

 FishBase Consortium

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Motivation

 FishBase: a relational database and information system for

biological data storage of fish species, with millions of records

containing:

 Species, taxonomic classification and predators

 Locations (country and ecosystem)

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Motivation

 Identification Key:

 A biology mechanism to identify a specific specimen

 Composed by a set of questions that guides scientists in this identification

 Has one or more species associated

 Similar to a decision tree

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6 - Freshwater fishes of Africa

Five pairs of external gill slits

Single, or single pair of gill

openings

Head without extended rostrum,

gill slits lateral

Head with extended rostrum,

gill slits ventral

Body without scales, or scales

small and not clearly visible.

Body with clearly visible scales.

Body slender, elongate and

eel-like Body not eel-like … …

Identification Key

Example

…

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Identification Key Problem

6 - Freshwater fishes of Africa 1419 - Species of Schilbe of Africa adapted from: http://fishbase.org/keys/description.php?keycode=6 http://fishbase.org/keys/description.php?keycode=1419 7

?

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Identification Key Problem

6 - Freshwater fishes of Africa … … Adipose fin present Adipose fin absent

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Identification Key Problem

6 - Freshwater fishes of Africa 1419 - Species of Schilbe of Africa … … Adipose fin present Adipose fin absent Adipose fin present Adipose fin absent … … adapted from: http://fishbase.org/keys/description.php?keycode=6 http://fishbase.org/keys/description.php?keycode=1419 11

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Motivation

 Biological data (as in FishBase) form a big network

 Biologists need network analysis for:

 Identify the most important species in an specific food chain;

 Define areas (or species) for preservation;

 Find relations in a network of identification keys.

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Motivation

How to support biologists in

network-driven analysis?

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Goal

Build a

network

database for

analysis from a

relational

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ReGraph: from FishBase to FishGraph

 Graph databases:

 Very effective in network analysis

 Flexible structure

 Easy to run transitive relationships

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ReGraph: from FishBase to FishGraph

ReGraph: a framework that generates a graph database from a relational database.

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ReGraph: from FishBase to FishGraph

ReGraph: a framework that generates a graph database from a relational database.

FishGraph: A Network-Driven Data Analysis 15

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ReGraph: from FishBase to FishGraph

ReGraph: maintain the graph database synchronized with the relational database (one-way synchronization).

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ReGraph: from FishBase to FishGraph

ReGraph: maintain the graph database synchronized with the relational database (one-way synchronization).

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ReGraph: from FishBase to FishGraph

ReGraph: Relational and Graph Databases keep their native form.

Current systemsCurrent

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ReGraph: from FishBase to FishGraph

ReGraph: allows adding new data in the graph database.

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ReGraph: from FishBase to FishGraph

ReGraph: mapped and annotated subgraphs are integrated and avaiable for running analysis.

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ReGraph: from FishBase to FishGraph

ReGraph: connects data in the local graph with global graphs on the web.

Semantic Web

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ReGraph: from FishBase to FishGraph

STEPS:

1. Map data from relational database to graph

2. Run the ETL process to load initial data

3. Synchronism process starts to run after the first loading

4. Add new information as annotation (optional)

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ReGraph: from FishBase to FishGraph

ReGraph: used to generate FishGraph (graph database) from FishBase (relational database)

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ReGraph: from FishBase to FishGraph

GENERA

FAMILIES

ORDERS CLASSES

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ReGraph: from FishBase to FishGraph

SPECIES ECOSYSTEM COUNTRY KEY GENUS FAMILY ORDER CLASS belongs_to

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Experiments: Identification Key Analysis

 Data used:

 Identification keys

 Species

 Geographic locations (countries and ecossystems)

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Experiments: Identification Key Analysis

 Long term goal:

 Start the identification process from any node across several identification keys.

 Goals in this analysis:

 Find similarities between keys

 Find differences between keys

 Analyze groups of keys

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The annotated “share” edge connects keys that share at least one species.

Experiments: Identification Keys Analysis

SPECIES COUNTRY GENUS FAMILY CLASS belongs_to

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Experiments: Identification Keys Analysis

Components based on the “share” edge connecting two or more distinct keys with their associated species.

A component is a subgraph in which there is a path from any node to another one.

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Experiments: Identification Keys Analysis

205 316

Identification key

Species colored by family (same order and class)

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Experiments: Identification Keys Analysis

205 - Key to the species of scorpionfishesoccurring in the Western Central Pacific. 316 - Key to the species of Indo-Pacific Scorpionfish(Genus Scorpaenopsis).

205 316

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Experiments: Identification Keys Analysis

205 316

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Experiments: Identification Keys Analysis

205 - Key to the species of scorpionfishesoccurring in the Western Central Pacific. 316 - Key to the species of Indo-Pacific Scorpionfish(Genus Scorpaenopsis).

205 316

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Experiments: Taxonomic Classification Analysis

 Data used:  Class  Order  Family  Genus  Species

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Experiments: Taxonomic Classification Analysis

 Goals:

 Compare data in FishGraph with data in a global graph (DBpedia)

 Find divergences

 Propose reviews

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Experiments: Taxonomic Classification

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Experiments: Taxonomic Classification Analysis

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Conclusions

 Graph databases to perform network analyses

 One-way synchronization

 Annotations and connection with other sources on the Web

 Network-driven data analysis for knowledge discovery:

 Identification keys

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Conclusions

 Future Work:

 Register provenance from data obtained from web sources

 Organize “same as” nodes in the local graph

 Enable distinct graph mappings from one relational model

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FishGraph: A Network-Driven

Data Analysis

Thank you!

Acknowledgments:

Unicamp, LIS members, FishBase Consortium, FAPESP, CNPq, CAPES