Neo4j in Action


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Getting started with Neo4j

If it does not exist you show a error page, otherwise you pass the user data to the template. Refresh the homepage and click on any user for showing his profile and the list of followed users.

Maria Aparecida Santiago

The next step is to provide suggestions to the profile. As we potentially get multiple paths for each friend-of-a-friend fof , we need to distinct the results in order to avoid duplicates in our list collect is an aggregation operation that collects values into an array :. The workflow for removing relationships is pretty much the same as for adding new relationships, create a route, a controller action and adapt the layout :.

Each type of database provides unique qualities that have applications in certain domains. Our work aims to investigate and compare the performance and scalability of relational databases to graph databases in terms of handling multilevel queries such as finding the impact of a particular subject with the working area of pass out students. MySQL was chosen as the relational database, Neo4j as the graph database. Recommender systems are envisioned and design to serve automatic recommendations for various services and products to active consumer.

Such systems can find similar items and sort to generate top N suggestions as per users past Such systems can find similar items and sort to generate top N suggestions as per users past transaction, location, knowledge, profiles, preferences or choices of otherpeople. This research illustrates potential use of graph-based model intended for recommendation system and designed for various domains. The ultramodern graph technology and state-of-the-art graph query tool is prime motivation behind this research work.

The implementation has been carried out with profound online graph management tool known as 'neo4j' and recommendation algorithm has been executed with graph query language called 'Cypher'. The experiment and evaluation shows success of proposed model for increasing efficiency of the system which is emerging need of the present.

Neo4j in Action

The outcome of this research has great potential which can be put into practice and it can offer course of action for advance technical revolution in forthcoming era of Big data. This research also encourages anyone who wants to implement graph-based model for recommendation system. In all cases, In all cases, to build and store the detector description, a full software stack was needed.

In this paper we present a new and scalable mechanism to store the geometry data and to serve the detector description data through a REST web-based API. Moreover, it provides new functionalities to users, who can now search for specific volumes and get partial detector description, or filter geometry data based on custom criteria.

Using Neo4j and GraphQL to build Human Connection (Online Meetup #65)

We present two approaches to build a REST API to serve geometry data, based on two different technologies used in other fields and communities: the search engine ElasticSearch and the graph database Neo4j. We describe their characteristics and we compare them using real-world usage tests to test their speed and scalability, targeted to a HEP usage. The amount of data that is being made available on the Web is increasing. This provides business organisations with the opportunity to acquire large datasets in order to offer novel information services or to better market existing This provides business organisations with the opportunity to acquire large datasets in order to offer novel information services or to better market existing products and services.

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Much of this data is now publicly available e. The challenge from a corporate perspective is to make sense of the third party data and transform it so that it can more easily integrate with their existing corporate data or with datasets with a different provenance. This paper presents research-in-progress aimed at semantically transforming raw data on U. The approach adopted is based on BORO a 4D foundational ontology and re-engineering method and the target technological platform is Neo4J a graph database. The primary challenges encountered are 1 re-engineering the raw data into a 4D ontology and 2 representing the 4D ontology into a graph database.

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The paper will discuss such challenges and explain the transformation process that is currently being adopted. Sergio de Cesare. Social networks analysis studies the interactions among users when using social media. The content provided by social media is composed of essentially two parts: a network structure of users' links e. Topic modeling and sentiment analysis are two techniques that help extracting meaningful information from large or multiple portions of the text: identifying the topic discussed in a text, and providing a value characterizing an opinion respectively.

This extracted information can then be combined to the network structure of users' links for further tasks as predictive analytics, pattern recognition, etc. In this paper we propose a method based on graph databases, topic modelling and sentiment analysis to facilitate pattern extraction within social media texts.

We applied our model to Twitter datasets, and were able to extract a series of opinion patterns. Time-based critical infrastructure dependency analysis for large-scale and cross-sectoral failures. Dependency analysis of critical infrastructures is a computationally intensive problem when dealing with large-scale, cross-sectoral, cascading and common-cause failures.

GitHub - opencredo/neo4j-in-action

The problem intensifies when attempting a dynamic, time-based The problem intensifies when attempting a dynamic, time-based dependency analysis. This paper extends a previous graph-based risk analysis methodology to dynamically assess the evolution of cascading failures over time. Various growth models are employed to capture slow, linear and rapidly evolving effects, but instead of using static projections, the evolution of each dependency is " objectified " by a fuzzy system that also considers the effects of nearby dependencies. To achieve this, the impact and, eventually, risk of each dependency is quantified on the time axis into a form of many-valued logic.

In addition, the methodology is extended to analyze major failures triggered by concurrent common-cause cascading events.


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A critical infrastructure dependency analysis tool, CIDA, that implements the extended risk-based methodology is described. CIDA is designed to assist decision makers in proactively analyzing dynamic and complex dependency risk paths in two ways: i identifying potentially underestimated low risk dependencies and reclassifying them to a higher risk category before they are realized; and ii simulating the effectiveness of alternative mitigation controls with different reaction times.

Thus, the CIDA tool can be used to evaluate alternative defense strategies for complex, large-scale and multi-sectoral dependency scenarios and to assess their resilience in a cost-effective manner.


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Panayiotis Kotzanikolaou. Involvement of Big Data and real time streaming data makes the data Involvement of Big Data and real time streaming data makes the data processing much more challenging in order to extract and to visualize the exact data. In this talk, Nicki Watt and Michal Bachman present lessons learned and being learned on an active Neo4j project - Opigram: a socially orientated recommendation engine which is already live with some k users and growing.

He's also a certified Spring trainer.

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Nicki is the Chief Technology Officer for OpenCredo responsible for the overall direction and leadership of technical engagements. A techie at heart, her core expertise lie in problem solving and enabling pragmatic, practical solutions. Over the years at OpenCredo Nicki has worn many hats which has included the development, delivery and leading of large scale platform and application development projects involving Cloud, DevOps, Containers and PaaS.

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