jsongraph

This library provides tools to integrate data from multiple sources into a coherent data model. Given a heterogeneous set of source records, it will generate a set of composite entities with merged information from all available sources. Further, it allows querying the resulting graph using a simple, JSON-based graph query language.

The intent of this tool is to make a graph-based data integration system (based on RDF) seamlessly available through simple JSON objects.

Usage

This is what using the library looks like in a simplified scenario:

from jsongraph import Graph

# Create a graph for all project information. This can be backed by a
# triple store or an in-memory construct.
graph = Graph(base_uri='file:///path/to/schema/files')
graph.register('person', 'person_schema.json')

# Load data about a person.
context = graph.context()
context.add('person', data)
context.save()
# Repeat data loading for a variety of source files.

# This will integrate data from all source files into a single representation
# of the data.
context = graph.consolidate('urn:prod')

# Metaweb-style queries:
for item in context.query([{"name": None, "limit": 5}]):
    print item['name']

Design

A jsongraph application is focussed on a Graph, which stores a set of data. A Graph can either exist only in memory, or be stored in a backend database.

All data in a Graph is structured as collections of JSON objects (i.e. nested dictionaries, lists and values). The structure of all stored objects must be defined using a JSON Schema. Some limits apply to such schema, e.g. they may not allow additional or pattern properties.

Contexts and Metadata

The objects in each Graph are grouped into a set of Contexts. Those also include metadata, such as the source of the data, and the level of trust that the system shall have in those data. A Context will usually correspond to a source data file, or a user interaction.

Consolidated Contexts

When working with jsongraph, a user will first load data into a variety of Contexts. They can then generate a consolidated version of the data, in a separate Context.

This consolidated version applies entity de-duplication. For object properties with multiple available values across several Contexts, the information from the most trustworthy Context will be chosen.

Queries

jsongraph includes a query language implementation, which is heavily inspired by Google's Metaweb Query Language. Queries are written as JSON, and search proceeds by example. Searches can also be deeply nested, traversing the links between objects stored in the Graph at an arbitrary complexity.

Queries on the data can be run either against any of the source Contexts, or against the consolidated context. Queries against the consolidated Context will produce responses which reflect the best available information based on data from a variety of sources.

De-duplication

One key part of the functions of this library will be the application of de-duplication rules. This will take place in three steps:

• Generating a set of de-duplicating candidates for all entities in a given Graph. These will be simplified representations of objects which can be fed into a comparison tool (either automated or interactive with the user).

• Once the candidates have been decided, they are transformed into a mapping of the type (original_fingerprint -> same_as_fingerprint). Such mappings are applied to a context.

• Upon graph consolidation (see above), the entities which have been mapped to another are not included. All their properties are inherited by the destination entity.

A data comparison candidate may look like this:

{
    "fingerprint": "...",
    "entity": "...",
    "data": {

    },
    "source": {
        "label": "...",
        "url": "http://..."
    }
}

Tests

The test suite will usually be executed in it's own virtualenv and perform a coverage check as well as the tests. To execute on a system with virtualenv and make installed, type:

$ make test