The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.
The Future is Big Graphs: A Community View on Graph Processing SystemsNeo4j
Alexandru Iosup, Full Professor, Vrije Universiteit Amsterdam (VU Amsterdam)
Angela Bonifati, Full Professor of Computer Science, Université de Lyon
Hannes Voigt, Software Engineer, Neo4j
Bigdata and ai in p2 p industry: Knowledge graph and inferencesfbiganalytics
Title: Knowledge graph and inference: use cases in online financial market
Abstract: While the knowledge graph is an active research field in machine learning community, this powerful tool is still less known to the people in the industry. In this talk, I will first introduce knowledge graph and inference techniques including the recent developments which combine with deep learning. Then I will talk about several use cases in online financial market: fraud/anomaly detection, lost contact discovery, intelligent search, name disambiguation and etc. I will also briefly mention how to build knowledge graph using neo4j from different data sources.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Challenges in the Design of a Graph Database Benchmark graphdevroom
Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing.
During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.
The Future is Big Graphs: A Community View on Graph Processing SystemsNeo4j
Alexandru Iosup, Full Professor, Vrije Universiteit Amsterdam (VU Amsterdam)
Angela Bonifati, Full Professor of Computer Science, Université de Lyon
Hannes Voigt, Software Engineer, Neo4j
Bigdata and ai in p2 p industry: Knowledge graph and inferencesfbiganalytics
Title: Knowledge graph and inference: use cases in online financial market
Abstract: While the knowledge graph is an active research field in machine learning community, this powerful tool is still less known to the people in the industry. In this talk, I will first introduce knowledge graph and inference techniques including the recent developments which combine with deep learning. Then I will talk about several use cases in online financial market: fraud/anomaly detection, lost contact discovery, intelligent search, name disambiguation and etc. I will also briefly mention how to build knowledge graph using neo4j from different data sources.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
Challenges in the Design of a Graph Database Benchmark graphdevroom
Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing.
During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.
Spring Data Graph is an integration library for the open source graph database Neo4j and has been around for over a year, evolving from its infancy as brainchild of Rod Johnson and Emil Eifrem. It supports transparent AspectJ based POJO to Graph Mapping, a Neo4jTemplate API and extensive support for Spring Data Repositories. It can work with an embedded graph database or with the standalone Neo4j Server.
The session starts with a short introduction to graph databases. Following that, the different approaches using Spring Data Graph are explored in the Cineasts.net web-app, a social movie database which is also the application of the tutorial in the Spring Data Graph Guidebook. The session will also cover creating a green-field project using the Spring Roo Addon for Spring Data Graph and deploying the App to CloudFoundry.
This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2Connected Data World
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Parts 1 & 2
Ethics & (Explainable) AI – Semantic AI & the Role of the Knowledge ScientistStratos Kontopoulos
Presentation for the NexTech Experts Panel II during the NexTech 2021 Congress (https://www.iaria.org/conferences2021/NexTech21.html).
Discusses the emerging and versatile role of the Knowledge Scientist in designing and developing explainable SemanticAI applications.
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.
Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.
In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.
In this webinar we discuss the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
We cover the high-level steps of modeling, importing, and querying your data using Cypher and give an overview of the transition from RDBMS to Graph.
A comparison of relational and graph model theories, with an eye towards DataStax's implementation of Graph. Note: I'm working on a concise, formal mathematical definition of relational, based on Codd's 1970 paper. (Thanks to Artem Chebotko for suggesting this.)
From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...Connected Data World
Do you want to learn how to use the low-hanging fruit of knowledge graphs — schema.org and JSON-LD — to annotate content and improve your SEO with semantics and entities? This hands-on workshop with one of the leading Semantic SEO practitioners will help you get started.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Europe’s General Data Protection Regulations (GDPR) will go into effect in less than a year (on 25 May 2018). Achieving data compliance is far from simple and businesses must continuously review how they gather, process and protect personal data. From how data is stored and used to how you secure and even erase information from corporate systems, discover how graph technology can address key challenges relating to Data Quality, Governance and Metadata Management.
Spring Data Graph is an integration library for the open source graph database Neo4j and has been around for over a year, evolving from its infancy as brainchild of Rod Johnson and Emil Eifrem. It supports transparent AspectJ based POJO to Graph Mapping, a Neo4jTemplate API and extensive support for Spring Data Repositories. It can work with an embedded graph database or with the standalone Neo4j Server.
The session starts with a short introduction to graph databases. Following that, the different approaches using Spring Data Graph are explored in the Cineasts.net web-app, a social movie database which is also the application of the tutorial in the Spring Data Graph Guidebook. The session will also cover creating a green-field project using the Spring Roo Addon for Spring Data Graph and deploying the App to CloudFoundry.
This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2Connected Data World
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Parts 1 & 2
Ethics & (Explainable) AI – Semantic AI & the Role of the Knowledge ScientistStratos Kontopoulos
Presentation for the NexTech Experts Panel II during the NexTech 2021 Congress (https://www.iaria.org/conferences2021/NexTech21.html).
Discusses the emerging and versatile role of the Knowledge Scientist in designing and developing explainable SemanticAI applications.
Detecting eCommerce Fraud with Neo4j and LinkuriousNeo4j
Last year, the global eCommerce market represented $1.9 trillions. As the market expands worldwide, the opportunity for fraud keeps growing with fraudsters constantly refining their tactics to outsmart anti-fraud frameworks. From chargeback fraud to re-shipping scam or identity fraud, numerous types of fraud can impact your organization. While collecting data is essential to enable real-time risk assessment, many organizations don’t have the necessary tools to find the insights needed to block fraud attempts.
Neo4j and Linkurious offer a solution to tackle the eCommerce fraud challenge. Their combined technologies provide a 360° overview of organization’s data and allow real-time analysis and detection of eCommerce fraud patterns and activities.
In this webinar, you will learn about:
- The current trends of eCommerce frauds and the risks for organizations;
- The challenges of detecting fraud tentatives in real-time and the advantage of the graph approach;
- How to use Linkurious’ graph visualization and analysis software to prevent and investigate eCommerce fraud.
In this webinar we discuss the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
We cover the high-level steps of modeling, importing, and querying your data using Cypher and give an overview of the transition from RDBMS to Graph.
A comparison of relational and graph model theories, with an eye towards DataStax's implementation of Graph. Note: I'm working on a concise, formal mathematical definition of relational, based on Codd's 1970 paper. (Thanks to Artem Chebotko for suggesting this.)
From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...Connected Data World
Do you want to learn how to use the low-hanging fruit of knowledge graphs — schema.org and JSON-LD — to annotate content and improve your SEO with semantics and entities? This hands-on workshop with one of the leading Semantic SEO practitioners will help you get started.
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Europe’s General Data Protection Regulations (GDPR) will go into effect in less than a year (on 25 May 2018). Achieving data compliance is far from simple and businesses must continuously review how they gather, process and protect personal data. From how data is stored and used to how you secure and even erase information from corporate systems, discover how graph technology can address key challenges relating to Data Quality, Governance and Metadata Management.
These days we hear a lot about NoSQL and particularly graph database. But before jumping straight into development using any graph database, we should ask the following questions - 'what makes it a case for GraphDB? And can you prove it?' Basically de-risking and making a case for management buy in. Further, its important to convince ourselves.
This talk highlights some cases where GraphDB will be useful. Followed by some insights from a comparison we did between Neo4j and MySQL/MSSQL. By the end of this session, you'll understand the advantages of using a GraphDB and also what questions to ask before selecting a GraphDB.
This was presented at TechJam on 11th Sept 2014
Graph Databases for SQL Server Professionals - SQLSaturday #350 WinnipegStéphane Fréchette
Presented on November 22, 2014 @ SQLSaturday #350 in Winnipeg, MB Canada
Graph databases are used to represent graph structures with nodes, edges and properties. Neo4j, an open-source graph database is reliable and fast for managing and querying highly connected data. Will explore how to install and configure, create nodes and relationships, query with the Cypher Query Language, importing data and using Neo4j in concert with SQL Server... Providing answers and insight with visual diagrams about connected data that you have in your SQL Server Databases!
Session Level: Intermediate
A presentation of the Neo4j graph database given at QCon SF 2008. It describes why relational databases are increasingly unfit for many applications today and why graphs may be a good fit. It also covers the fundamentals of how to program with Neo4j.
Putting your data in a graph database is easy. We wanted to make it as easy to query the data. That's why we set out to create the cypher query language. This session introduces cypher shows a lot of examples and explains how it is used.
Intro to Neo4j or why insurances should love graphsPeter Neubauer
This talk covers a basic intro of graphs, NOSQL and graph databases, followed b a number of domain examples and case studies, and a section on how graph databases can be interesting in the domain of insurance companies.
OrientDB vs Neo4j - and an introduction to NoSQL databasesCurtis Mosters
NoSQL databases are a good alternative to common SQL technologies. Here you get an introduction and comparison of SQL vs NoSQL. Furthermore we have a look on Graph databases and especially OrientDB vs Neo4j.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships
Comprendre le fonctionnement du lobbying en france et en Europe , des réseaux d'influences , des groupes de pressions, entre la transparence Européenne et l'opacité françaises privilèges des élus beaucoup de pain sur la planche ...quand le pouvoir des lobbyistes est plus important que celui des ministres comme Nicolas Hulot en france.
A graph is a structure composed of a set of vertices (i.e.~nodes, dots) connected to one another by a set of edges (i.e.~links, lines). The concept of a graph has been around since the late 19th century, however, only in recent decades has there been a strong resurgence in the development of both graph theories and applications. In applied computing, since the late 1960s, the interlinked table structure of the relational database has been the predominant information storage and retrieval paradigm. With the growth of graph/network-based data and the need to efficiently process such data, new data management systems have been developed. In contrast to the index-intensive, set-theoretic operations of relational databases, graph databases make use of index-free traversals. This presentation will discuss the graph traversal programming pattern and its application to problem-solving with graph databases.
K anonymity for crowdsourcing database
In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consists of a set of database records and corresponding questions for human workers.
New Opportunities for Connected Data - Emil Eifrem @ GraphConnect Boston + Ch...Neo4j
Today’s complex data is not only big, but also semi-structured and densely connected. In this session we’ll look at how size, structure and connectedness have converged to transform the data landscape. We’ll then go on to look at some of the new opportunities for creating end-user value that have emerged in a world of connected data, illustrated with practical examples drawn from the telecommunications, social media and logistics sectors.
Government GraphSummit: Optimizing the Supply ChainNeo4j
Michael Moore Ph.D., Principal, Partner Solutions and Neo4j Technology, Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML).
With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices.
That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:
- Different approaches to graph feature engineering, from queries and algorithms to embeddings
- How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks
- How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data
- Why no-code visualization and prototyping is important
"Semantic Integration Is What You Do Before The Deep Learning". dev.bg Machine Learning seminar, 13 May 2019.
It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.
Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible. Join us to hear about 4 areas at the forefront of graph enhanced AI and ML, and find out which techniques are commonly used today and which hold the potential for disrupting industries. We'll provide examples and specifically look how: - Graphs provide better accuracy through connected feature extraction - Graphs provide better performance through contextual model optimization - Graphs provide context through knowledge graphs - Graphs add explainability to neural networks
Speakers: Jake Graham, Alicia Frame
The Data Platform for Today's Intelligent Applications.pdfNeo4j
Do you know how graph technology is used in today’s data-driven applications? We’ll get you up to speed and introduce you to the Neo4j product portfolio.
Similar to La bi, l'informatique décisionnelle et les graphes (20)
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
La bi, l'informatique décisionnelle et les graphes
1. Neo Technology, Inc Confidential
La BI,
l'informatique décisionnelle
et les graphes
Philip Rathle, Sr Dir Products
philip@neotechnology.com
http://twitter.com/prathle
7. Neo Technology, Inc Confidential
Evolution of Web Search
Survival of the Fittest
Pre-1999
WWW Indexing
Discrete Data
1999 - 2012
Google Invents
PageRank
Connected Data
(Simple)
2012-?
Google Knowledge Graph,
Facebook Graph Search
Connected Data
(Rich)
8. Neo Technology, Inc Confidential
Evolution of Online Recruiting
2010-11
Resume Searching &
Scoring
Aggregated Data
Survival of the Fittest
2011-12
Social Job Search
Connected Data
9. Neo Technology, Inc Confidential
Consumer Web Giants Depends on Five Graphs
Gartner’s “5 Graphs”
Social Graph
Ref: http://www.gartner.com/id=2081316
Interest Graph
Payment Graph
Intent Graph
Mobile Graph
14. Neo Technology, Inc Confidential
Typical Graph BI Environment
Application
Other
Databases
ETL
Neo4j
Cluster
Data Storage &
Business Rules Execution
Reporting
Graph-
Dashboards
&
Ad-hoc
Analysis
Graph
Visualization
End User Ad-hoc visual navigation &
discovery
Bulk Analytic
Infrastructure
(e.g. Graph Compute
Engine)
ETL
Graph Mining &
Aggregation
Data Scientist
Ad-Hoc
Analysis
15. What is a
Graph Database
A graph database is an online (“real-time”)
database management system with CRUD
methods that expose a graph data model
• Two important properties:
• Native graph processing, including
index-free adjacency1 to facilitate traversals
• Native graph storage engine, i.e.
written from the ground up to be
optimized for managing graph data
1] See Rodriguez, M.A., Neubauer, P., ,“The Graph Traversal Pattern,” 2010 (http://arxiv.org/abs/1004.1001)
16. Overview of Popular
Graph Data Models
• Property Graph
• Description: A “directed, labeled, attributed, multi-
graph”1 which exposes three building blocks: nodes, typed
relationships and key-value properties on both nodes and
relationships
• Vendors: Neo4j, OrientDB, InfiniteGraph, Dex
• RDF Triples
• Description: URI-centered subject-predicate-object
triples as pioneered by the semantic web movement2
• Vendors: AllegroGraph, Sesame
• HyperGraph
• Description: A generalized graph where a relationship
can connect an arbitrary amount of nodes (compared to
the more common binary graph models)3
• Vendors: HyperGraphDB,TrinityDB
1] Rodriguez, M.A., Neubauer, P., “Constructions from Dots and Lines,” 2010, http://arxiv.org/abs/1006.2361
2] W3C,“The Resource Description Framework (RDF),” 2004, http://www.w3.org/RDF/
3] Wikipedia, http://en.wikipedia.org/wiki/Hypergraph
17. Graph Compute Engine
Processing platforms that enable graph global
computational algorithms to be run against
large data sets
Graph Mining
Engine
(Working Storage)
In-Memory Processing
System(s)
of Record
Graph Compute
Engine
Data extraction,
transformation,
and load
18. Neo Technology, Inc Confidential
Graph Global Queries
What is the max/min/avg. number of connections per node?
(aka “Degree Distribution”)
19. Neo Technology, Inc Confidential
Quoi faire avec un Graph Database?
Example: Facebook Graph Search
20. Neo Technology, Inc Confidential
For the Facebook Graph Question:
What sushi restaurants in NYC do my friends like?
21. Neo Technology, Inc Confidential
What the Graph Looks Like:
What sushi restaurants in NYC do my friends like?
22. Neo Technology, Inc Confidential
What the Cypher Query Looks Like:
What sushi restaurants in NYC do my friends like?
START me=node:person(name = 'Philip'),
location=node:location(location='New York'),
cuisine=node:cuisine(cuisine='Sushi')
MATCH (me)-[:IS_FRIEND_OF]->(friend)-[:LIKES]->(restaurant)
-[:LOCATED_IN]->(location),(restaurant)-[:SERVES]->(cuisine)
RETURN restaurant
23. Neo Technology, Inc Confidential
What the Search Looks Like:
What sushi restaurants in NYC do my friends like?
24. Neo Technology, Inc Confidential
What Other Graph Searches Look Like
What drugs will bind to protein X and not interact with drugY?
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Web Browsing Portfolio Analytics
Mobile Social ApplicationGene Sequencing
Emergent Graph in Other Industries
(Actual Neo4j Graphs)
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Background
• World’s largest provider of IT infrastructure, software
& services
• HP’s Unified Correlation Analyzer (UCA) application is a
key application inside HP’s OSS Assurance portfolio
• Carrier-class resource & service management, problem
determination, root cause & service impact analysis
• Helps communications operators manage large,
complex and fast changing networks
Business problem
• Use network topology information to identify root
problems causes on the network
• Simplify alarm handling by human operators
• Automate handling of certain types of alarms Help
operators respond rapidly to network issues
• Filter/group/eliminate redundant Network
Management System alarms by event correlation
Solution & Benefits
• Accelerated product development time
• Extremely fast querying of network topology
• Graph representation a perfect domain fit
• 24x7 carrier-grade reliability with Neo4j HA clustering
• Met objective in under 6 months
Industry: Web/ISV, Communications
Use case: Network Management
Global (U.S., France)
39. Neo Technology, Inc Confidential
Background
•One of the world’s largest logistics carriers
•Projected to outgrow capacity of old system
•New parcel routing system
•Single source of truth for entire network
•B2C & B2B parcel tracking
•Real-time routing: up to 5M parcels per day
Business problem
•24x7 availability, year round
•Peak loads of 2500+ parcels per second
•Complex and diverse software stack
•Need predictable performance & linear
scalability
•Daily changes to logistics network: route from
any point, to any point
Solution & Benefits
•Neo4j provides the ideal domain fit:
•a logistics network is a graph
•Extreme availability & performance with Neo4j
clustering
•Hugely simplified queries, vs. relational for
complex routing
•Flexible data model can reflect real-world data
variance much better than relational
•“Whiteboard friendly” model easy to understand
Industry: Logistics
Use case: Parcel Routing
40. Neo Technology, Inc Confidential
Industry: Online Job Search
Use case: Social / Recommendations
• Online jobs and career community, providing
anonymized inside information to job seekers
Business problem
• Wanted to leverage known fact that most jobs are
found through personal & professional connections
• Needed to rely on an existing source of social
network data. Facebook was the ideal choice.
• End users needed to get instant gratification
• Aiming to have the best job search service, in a very
competitive market
Solution & Benefits
• First-to-market with a product that let users find jobs
through their network of Facebook friends
• Job recommendations served real-time from Neo4j
• Individual Facebook graphs imported real-time into Neo4j
• Glassdoor now stores > 50% of the entire Facebook
social graph
• Neo4j cluster has grown seamlessly, with new instances
being brought online as graph size and load have increased
Person
Company
KNOW
S
Person
Person
KNOWS
Company
KNOWS
WORKS_AT
WORKS_AT
Neo Technology Confidential
Background
Sausalito, CA
41. Neo Technology, Inc Confidential
Industry: Communications
Use case: Recommendations
•Cisco.com serves customer and business
customers with Support Services
•Needed real-time recommendations, to
encourage use of online knowledge base
•Cisco had been successfully using Neo4j for its
internal master data management solution.
•Identified a strong fit for online
recommendations
Solution & Benefits
•Cases, solutions, articles, etc. continuously scraped
for cross-reference links, and represented in Neo4j
•Real-time reading recommendations via Neo4j
•Neo4j Enterprise with HA cluster
•The result: customers obtain help faster, with
decreased reliance on customer support
Neo Technology Confidential
Background
Business problem
•Call center volumes needed to be lowered by
improving the efficacy of online self service
•Leverage large amounts of knowledge stored in
service cases, solutions, articles, forums, etc.
•Problem resolution times, as well as support
costs, needed to be lowered
Support
Case
Support
Case
Knowledge
Base
Article
Solution
Knowledge
Base
Article
Knowledge
Base
Article
Message
San Jose, CA
Cisco.com
42. Neo Technology, Inc Confidential
Interactive Television Programming
Industry: Communications
Use case: Social gaming
Background
• Europe’s largest communications company
• Provider of mobile & land telephone lines to
consumers and businesses, as well as internet
services, television, and other services
Solution & Benefits
• Interactive, social offering gives fans a way to
experience the game more closely
• Increased customer stickiness for Deutsche Telekom
• A completely new channel for reaching customers
with information, promotions, and ads
• Clear competitive advantage
Frankfurt, Germany
Business problem
• The Fanorakel application allows fans to have an
interactive experience while watching sports
• Fans can vote for referee decisions and interact with
other fans watching the game
• Highly connected dataset with real-time updates
• Queries need to be served real-time on rapidly
changing data
• One technical challenge is to handle the very high
spikes of activity during popular games
43. Neo Technology, Inc Confidential
Reasons for Choosing a Graph
Database
1. Order-of-magnitude improvements in query
performance for complex, connected data
2. Drastically accelerated application
development cycles
3. Maintainability and extensibility of the
data model
4. Maturity and reliability of the product