Application Modeling with Graph Databases - Relationships are cool

The document discusses graph databases and their use cases. It provides an overview of Neo Technology, the creator of Neo4j, the world's leading graph database. It describes when graph databases are useful and how they model relationships between data differently than traditional databases. Examples are given of how graph databases can be used for recommendations, fraud detection, supply chain management, and powering the Internet of Things.

Noel Yuhanna, VP, Principal Analyst, Forrester Mary Barton, Consultant, Forrester Blaise James, Analyst Relations, Neo4j

Artificial Intelligence in real world applications needs the notion of an open world assumption; they need to be able to work in unknown situations. However, most current image processing application cannot handle unknown situations and objects. Unknown objects are classified as background and systems are only able to classify images into pretrained and predefined object classes. Using the KGLIB package of Grakn, we designed and trained a graph network for object classification, which is able to handle unknown objects. Data-driven insights based on image properties are combined with expert knowledge about class-hierarchies to classify images on multiple categories. We tested our network on a dataset of vehicles and predicted higher level categories. (For example 'land', 'air' or 'sea' vehicle). The graph network is used to predict interesting object characteristic, which require abstract knowledge predefined in a Grakn knowledge graph. During this talk we will present our approach taken and discuss the design process we have taken. We will not only discuss the results, but also the difficulties and learning process we encountered.

This document contains snippets from a Neo Technology conference presentation on graphs and graph databases. It discusses how graphs can be used to model real-world domains like social networks, telecommunications networks, financial networks, healthcare networks, and more. It also provides examples of how specific companies like Accenture are using graph databases and outlines Neo Technology's roadmap for improving the user experience of its graph database platform.

Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace. This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology. Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data. Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc. Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018). Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance. Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US. We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry. Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.

What is graph all about, and why should you care? Graphs come in many shapes and forms, and can be used for different applications: Graph Analytics, Graph AI, Knowledge Graphs, and Graph Databases. Talk by George Anadiotis. Connected Data London Meetup June 29th 2020. Up until the beginning of the 2010s, the world was mostly running on spreadsheets and relational databases. To a large extent, it still does. But the NoSQL wave of databases has largely succeeded in instilling the “best tool for the job” mindset. After relational, key-value, document, and columnar, the latest link in this evolutionary proliferation of data structures is graph. Graph analytics, Graph AI, Knowledge Graphs and Graph Databases have been making waves, included in hype cycles for the last couple of years. The Year of the Graph marked the beginning of it all before the Gartners of the world got in the game. The Year of the Graph is a term coined to convey the fact that the time has come for this technology to flourish. The eponymous article that set the tone was published in January 2018 on ZDNet by domain expert George Anadiotis. George has been working with, and keeping an eye on, all things Graph since the early 2000s. He was one of the first to note the continuing rise of Graph Databases, and to bring this technology in front of a mainstream audience. The Year of the Graph has been going strong since 2018. In August 2018, Gartner started including Graph in its hype cycles. Ever since, Graph has been riding the upward slope of the Hype Cycle. The need for knowledge on these technologies is constantly growing. To respond to that need, the Year of the Graph newsletter was released in April 2018. In addition, a constant flow of graph-related news and resources is being shared on social media. To help people make educated choices, the Year of the Graph Database Report was released. The report has been hailed as the most comprehensive of its kind in the market, consistently helping people choose the most appropriate solution for their use case since 2018. The report, articles, news stream, and the newsletter have been reaching thousands of people, helping them understand and navigate this landscape. We’ll talk about the Year of the Graph, the different shapes, forms, and applications for graphs, the latest news and trends, and wrap up with an ask me anything session.

We propose a new area of research on automating data narratives. Data narratives are containers of information about computationally generated research findings. They have three major components: 1) A record of events, that describe a new result through a workflow and/or provenance of all the computations executed; 2) Persistent entries for key entities involved for data, software versions, and workflows; 3) A set of narrative accounts that are automatically generated human-consumable renderings of the record and entities and can be included in a paper. Different narrative accounts can be used for different audiences with different content and details, based on the level of interest or expertise of the reader. Data narratives can make science more transparent and reproducible, because they ensure that the text description of the computational experiment reflects with high fidelity what was actually done. Data narratives can be incorporated in papers, either in the methods section or as supplementary materials. We introduce DANA, a prototype that illustrates how to generate data narratives automatically, and describe the information it uses from the computational records. We also present a formative evaluation of our approach and discuss potential uses of automated data narratives.

Using the last Big Data technologies like Spark Dataframe, HDFS, Stratio Intelligence or Stratio Crossdata. We have developed a solution which is able to obtain critical information for multiple datasources like text files o graph databases. This process it's a simple and straight forward solution that solves the translation of a Graph database with multiple and different structured entities to a Graph library, and the problem of querying a massive database without timeouts. Find here the complete talk: https://www.youtube.com/watch?v=vucXQwEhpfw

This document discusses graph theory and its applications to data science. It provides examples of social and technological networks that can be represented as graphs, and covers graph theory concepts like connected components, triadic closure, structural balance, and centrality measures. Neo4j is presented as an open-source graph database that allows storing and querying graph data using the Cypher query language.

The document provides an outline for a presentation on graph-based data models. It introduces some key concepts about graphs and how they are used to model real-world interconnected data. It discusses how early adopters of graph technologies grew by focusing on data relationships. The document also covers graph data structures, graph databases, and graph query languages like Cypher and Gremlin.

- Davide Mottin is an assistant professor in the Department of Computer Science at Aarhus University who researches graph mining. - His talk discusses unveiling knowledge in knowledge graphs through personalized summarization techniques. Knowledge graphs contain entities and relationships between them. - He describes an approach for generating personalized summaries of a knowledge graph based on a user's query history. The algorithm aims to find a subgraph that maximizes the probability of answering future queries, subject to a size limit.

Abstract of the Presentation: This talk will focus on applications of knowledge graphs and network science to the exploration of distributed industrial capabilities relevant to support work on United Nations Sustainable Development Goals. The core message and tools presented during the talk are relevant beyond sustainable development and can be applied to inter-organisational collaboration projects, information flows within companies and innovation management. About the Author: Pedro Parraguez is the Co-founder of Dataverz, a data analytics company based in Copenhagen, and Postdoctoral Researcher at DTU Management Engineering. Pedro’s research and applied work focuses on complex socio-technical systems, with emphasis on network science and data-driven analyses. This includes the study and development of decision-making support for industrial clusters, complex organisations, and large engineering projects.

This document discusses building knowledge graphs by extracting, aligning, and linking data from various sources. It describes crawling websites to acquire raw data, using both structured and unstructured extraction to extract features from the data, aligning the extracted features to a common schema, and resolving entities in the data to merge records referring to the same real-world entity. It also discusses techniques for collectively resolving entities in large datasets, summarizing graphs by grouping similar nodes into super-nodes, and using the summarized graph to predict links in the original graph. The overall goal is to clean, organize, and link disconnected data into a knowledge graph that is easier to query, analyze, and visualize.

The document discusses using graph databases for insights into connected data. It provides an overview of graph databases, comparing them to relational databases and NoSQL stores. It discusses how graph databases are better suited than other models for richly connected data due to their native support of relationships. The document also covers graph data modeling, the Cypher query language, examples of graph databases in real world domains, and aspects of graph database internals like scalability.

Dr. Mikio L. Braun gave a presentation on hardcore data science in practice at StrataConf 2016 in London. He discussed how Zalando, an online fashion retailer operating in 15 countries, heavily uses data science for recommendation engines. Braun covered different recommendation techniques including collaborative filtering, content-based recommendations, and personalized recommendations. He also discussed challenges in moving from static data analysis to production systems that operate in real-time and are frequently updated and monitored. Additionally, Braun addressed collaborations between data scientists and developers who have different coding approaches, and advocated for cross-functional teams and microservices in organizations.

The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks. To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library. Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph. A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML. This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.

Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.

Shutl delivers with Neo4j by addressing issues with their previous MySQL database including exponential growth of joins, complex unmaintainable code, and slowing API response times. They chose Neo4j as a graph database because relationships are explicitly stored, domain modeling is simplified, and performance remains constant with growth. Queries in Neo4j use the Cypher language which focuses on pattern matching rather than implementation details.