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Mongodb-graphDB Methods and Tools

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Jaspersoft MongoDB Query Language. Language Reference The Jaspersoft MongoDB Query Language is a JSON-style declarative language for specifying what data to retrieve from MongoDB. The connector converts this query into the appropriate API calls and uses the MongoDB Java connector to query the MongoDB instance. There are two types of queries supported: API driven queries: These queries rely on the mongo-java-driver support provided to query MongoDB. Command driven queries: This queries allow the usage of MongoDB Aggregation Framework operators collectionName Specifies the collection name.

. # Hard-coded collection name # Collection name specified as a String Parameter findQuery The findQuery specifies which documents from the collection will be returned. Conditional operators The value used for these operators may be explicitly specified in the query or may be specified in the report as a parameter of the appropriate data type. Partial list of conditional operators: Regular expressions $where findFields 1. Sort Accepted values: mapReduce. Mikeaddison93/mongo-express · GitHub.

License of mongobird | mongobird of CITSoft. Extended JSON. HypergraphDB - A Graph Database. Using MongoDB as a Graph Database - This is a presentation I gave to the NoSQL and Big Data Birmingham meetup group a couple of weeks back, and actually it’s a redux of a presentation I gave a couple of years ago at MongoDB UK. It tells the story of what has been quite a journey for us over the last couple of years, as we migrated away from a general purpose graph database technology to managing our graph data in a document database. The history here is that Talis used to be a graph database vendor of sorts; we had a PaaS offering called the Talis Platform, the genesis of which dates back to 2006.

The TL;DR of that particular chapter of Talis history is that we decided in 2012 that our future was not in triple stores, so the platform was mothballed and access for external customers wound down. However, the platform, and graph data, formed the architectural cornerstone of our growing EdTech business and its products. With the platform now EoL, we had some decisions to make about how to move forward with these products. Python MongoDB Drivers. This is an overview of the available tools for using Python with MongoDB. Those wishing to skip to more detailed discussion should check out the Python Driver Tutorial. Python Tools ORM Like Layers Because MongoDB is so easy to use the basic Python driver is often the best solution for many applications. However, if you need data validation, associations and other high-level data modeling functionality then ORM like layers may be desired.

ORM like layers Framework Tools Several tools and adapters for integration with various Python frameworks and libraries also exist. Framework Tools. Sparksee high-performance graph database. Evaluation license Evaluate Sparksee for free downloading the evaluation version right now. The evaluation license provided has the following configuration: XSMALL size (up to 1M objects) / 1 session / DEXHA disabled If you need a bigger configuration consider the rest of license options. Free License! Download right now! Required information will be only used to manage the download of the software and to provide promotional news in the future, but it will never be public or distributed to third parties. Sparsity Technologies encourages research with Sparksee by offering free licenses to PhD students, academics and other university staff, for non-commercial purposes.

Sparsity Technologies encourages the use of Sparksee by offering free licenses to selected companies during their development stages. Functionalities & Benefits Development/deployment of critical operations of your application as a built-in function within the Sparksee core, to boost your performance. Availability Response time. Mikeaddison93/blueprints. Jena (framework) Jena supports serialisation of RDF graphs to: SPARQL. SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language, that is, a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework format.[2][3] It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web.

On 15 January 2008, SPARQL 1.0 became an official W3C Recommendation,[4][5] and SPARQL 1.1 in March, 2013.[6] SPARQL allows for a query to consist of triple patterns, conjunctions, disjunctions, and optional patterns.[7] Implementations for multiple programming languages exist.[8] "SPARQL will make a huge difference" making the web machine-readable according to Sir Tim Berners-Lee in a May 2006 interview.[9] Advantages[edit] The example below demonstrates a simple query that leverages the ontology definition "foaf", often called the "friend-of-a-friend" ontology.

D3 Tutorial Table of Contents. 12 Devs of Xmas. I’d like to start off by asking what seems like quite a simple question: What is the difference between data and information? And taking it a step further, where does knowledge fit into this definition? Not as easy to answer as you might expect, is it! Before we start getting our hands dirty creating some awesome data visualisations, it’s going to be useful to learn the definitions of – and difference between – these three words. Data is a set of unprocessed facts (the word comes from the latin datum which means “that which is given”).

These data are generally meaningless until they have been processed. This could be a list of a population’s height, eye colour, skin tone, handedness, etc. Information is what you get after you’ve processed the data. Knowledge is perhaps the most difficult to define, but the definition which I think makes most sense in this instance is how we use our past experiences to decide what to do based on information. So what are we going to build, and what with? D3.js - Data-Driven Documents. Integrating AllegroGraph with MongoDB.

Introduction AllegroGraph has implemented extensions allowing users to query MongoDB databases using SPARQL and to execute heterogeneous joins, even though MongoDB, a NoSQL JSON document store, does not natively support joins, SPARQL or RDF-enabled linked data. In this document, we describe how to configure AllegroGraph and MongoDB to work together. Interfacing with MongoDB The steps for using MongoDB with AllegroGraph are: Installing MongoDBSynchronizing MongoDB data with AllegroGraph dataConfiguring AllegroGraph with MongoDB connection settings Please note that populating and maintaining the MongoDB database is separate from adding or deleting triples from the AllegroGraph triple-store.

Installing MongoDB MongoDB is not a Franz Inc. product. In the rest of this document, we assume you have the MongoDB server installed and running on a computer you can access. Synchronizing MongoDB data with AllegroGraph You must link AllegroGraph data and MongoDB data referring to the same object. ? Footnotes. Mikeaddison93/mongo-tools. Visualizing MongoDB Objects in Concept and Practice. D3.js and MongoDB | Architecture and Planning. I have not been shy in my love of MongoDB. The honeymoon is not over. Now I want to graph and visualize my data from MongoDB.

I just started looking at D3 – I’m coming to the party a bit late – and it is perfect for this task. I have thrown together a super simple, absurd even, example using a DB I had already populated and some left over CherryPy code. A Bar Chart, that should be in SVG, from MongoDB data in D3.js I have not put this on OpenShift yet, but may. The Python code prints out HTML that looks like this: Not much going on here, just a simple D3.js bar chart – not even done in SVG.

I return all the HTML and you get the page displayed at the top of this post. Like this: Like Loading... Mongoviewer-server. MongoDB + Node.js + Express + D3 Install API Server CLI MongoDB + Node.js + Express npm install -g mongoviewer-server Usage Start your MongoViewer API Server: mongoviewer-server By default it will serve on port 8080: See configuration options for customizing your API server. Example customized: mongoviewer-server --server:port 8081 --mongo:database testDb See the API documentation for more details. Installation npm install bower install node index.js Configuration Uses nconf. See the default configuration file at default_config.json. Alternatively you can use additional command line arguments: --server:port 8080 - Default 8080. For example: node index.js --server:port 8081 --mongo:database testDb Version 1 /api/v1/ Find See db.collection.find. GET /api/v1/:collection/find Query Parameters: queryoptions FindOne See db.collection.findOne.

GET /api/v1/:collection/findOne Aggregate See db.collection.aggregate. GET /api/v1/:collection/aggregate pipelineoptions Example API Usage node load.js Response: Mongodb-washington-dc-2013. Using MongoDB as a high performance graph database.