The graph ecosystem presentation lists and introduces a vast majority of storage systems for graph-like data: native graph databases, RDF databases, multi-model systems or systems with a graph API.
GraphTech Ecosystem - part 3: Graph VisualizationLinkurious
The graph ecosystem presentation lists and introduces the graph visualization actors: graph visualization libraries and toolkits; graph visualizers and add-ons
GraphTech Ecosystem - part 2: Graph AnalyticsLinkurious
The graph ecosystem presentation lists and introduces a vast majority of graph analytics actors: graph analytics frameworks; graph processing engines; graph analytics libraries and toolkits; graph query languages and projects.
Graph analytics in Linkurious EnterpriseLinkurious
Graph algorithms provide tools to extract insights from graph data. From detecting anomalies to understanding what are the key elements in a network or finding communities, graph algorithms reveal information that would otherwise remain hidden. Learn about:
- The most popular graph algorithms and what they can be used for;
- The benefits of using graph analytics with Linkurious Enterprise;
- How to integrate graph analytics in Linkurious Enterprise.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
Linkurious Enterprise is compatible with Azure Cosmos DB and offers investigation teams a turnkey solution to detect and investigate threats hidden in graph data. In this post, we explain how Linkurious Enterprise connects to Cosmos DB graph database.
Maintaining networks and servers availability while reducing downtimes to minimum are fundamental missions for IT managers and administrators. But with the growing complexity of infrastructures, the pressure from business strategists to deliver new services or the heterogeneity of data assets, managing networks is often a challenge.
Graph technologies like Linkurious offer an intuitive approach to model and investigate data by putting the connections between components at the forefront. Modeling the network into a flexible and unified overview is one of the keys to understand your architecture and reduce risks, costs and time spent on maintenance operations.
Getting started with Cosmos DB + Linkurious EnterpriseLinkurious
Nowadays, many real-world applications generate data that is naturally connected, but traditional systems fail to capture the value it represents. Thanks to its graph API, the multi-model database Cosmos DB lets you model and store graph-like data. On top of Cosmos DB, Linkurious Enterprise is turnkey solution to detect and investigate insights through an interface for graph data visualization and analysis.
In this presentation, we will explain the value of graphs and show how to get started with Cosmos DB and Linkurious Enterprise to accelerate the discovery of new insights in your connected data.
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.
GraphTech Ecosystem - part 3: Graph VisualizationLinkurious
The graph ecosystem presentation lists and introduces the graph visualization actors: graph visualization libraries and toolkits; graph visualizers and add-ons
GraphTech Ecosystem - part 2: Graph AnalyticsLinkurious
The graph ecosystem presentation lists and introduces a vast majority of graph analytics actors: graph analytics frameworks; graph processing engines; graph analytics libraries and toolkits; graph query languages and projects.
Graph analytics in Linkurious EnterpriseLinkurious
Graph algorithms provide tools to extract insights from graph data. From detecting anomalies to understanding what are the key elements in a network or finding communities, graph algorithms reveal information that would otherwise remain hidden. Learn about:
- The most popular graph algorithms and what they can be used for;
- The benefits of using graph analytics with Linkurious Enterprise;
- How to integrate graph analytics in Linkurious Enterprise.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
Linkurious Enterprise is compatible with Azure Cosmos DB and offers investigation teams a turnkey solution to detect and investigate threats hidden in graph data. In this post, we explain how Linkurious Enterprise connects to Cosmos DB graph database.
Maintaining networks and servers availability while reducing downtimes to minimum are fundamental missions for IT managers and administrators. But with the growing complexity of infrastructures, the pressure from business strategists to deliver new services or the heterogeneity of data assets, managing networks is often a challenge.
Graph technologies like Linkurious offer an intuitive approach to model and investigate data by putting the connections between components at the forefront. Modeling the network into a flexible and unified overview is one of the keys to understand your architecture and reduce risks, costs and time spent on maintenance operations.
Getting started with Cosmos DB + Linkurious EnterpriseLinkurious
Nowadays, many real-world applications generate data that is naturally connected, but traditional systems fail to capture the value it represents. Thanks to its graph API, the multi-model database Cosmos DB lets you model and store graph-like data. On top of Cosmos DB, Linkurious Enterprise is turnkey solution to detect and investigate insights through an interface for graph data visualization and analysis.
In this presentation, we will explain the value of graphs and show how to get started with Cosmos DB and Linkurious Enterprise to accelerate the discovery of new insights in your connected data.
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.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
Big Data
Hadoop
NoSQL databases and type: column oriented,document oriented, map based.
Map-reduce Example
Bigdata Analytics Case study
Case Study R
Retail and Finance Case Study
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.
Elegant and Scalable Code Querying with Code Property GraphsConnected Data World
Programming is an unforgiving art form in which even minor flaws can cause rockets to explode, data to be stolen, and systems to be compromised. Today, a system tasked to automatically identify these flaws not only faces the intrinsic difficulties and theoretical limits of the task itself, it must also account for the many different forms in which programs can be formulated and account for the awe-inspiring speed at which developers push new code into CI/CD pipelines. So much code, so little time.
The code property graph – a multi-layered graph representation of code that captures properties of code across different abstractions – (application code, libraries and frameworks) – has been developed over the last six years to provide a foundation for the challenging problem of identifying flaws in program code at scale, whether it is high-level dynamically-typed Javascript, statically-typed Scala in its bytecode form, the syntax trees generated by Roslyn C# compiler, or the bitcode that flows through LLVM.
Based on this graph, we define a common query language based on formal code property graph specification to elegantly analyze code regardless of the source language. Paired with the formulation of a state-of-the-art data flow tracker based on code property graphs, we arrive at a distributed cloud native powerful code analysis. This talk provides an introduction to the technology.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
Spark Summit EU 2015: Matei Zaharia keynoteDatabricks
2015 was a year of continued growth for Spark, with numerous additions to the core project and very fast growth of use cases across the industry. In this talk, I’ll look back at how the Spark community is has grown and changed in 2015, based on a large Apache Spark user survey conducted by Databricks. We see some interesting trends in the diversity of runtime environments (which are increasingly not just Hadoop); the types of applications run on Spark; and the types of users, now that features like R support and DataFrames are available in Spark. I’ll also cover the ongoing work in the upcoming releases of Spark to support new use cases.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
Big Data
Hadoop
NoSQL databases and type: column oriented,document oriented, map based.
Map-reduce Example
Bigdata Analytics Case study
Case Study R
Retail and Finance Case Study
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.
Elegant and Scalable Code Querying with Code Property GraphsConnected Data World
Programming is an unforgiving art form in which even minor flaws can cause rockets to explode, data to be stolen, and systems to be compromised. Today, a system tasked to automatically identify these flaws not only faces the intrinsic difficulties and theoretical limits of the task itself, it must also account for the many different forms in which programs can be formulated and account for the awe-inspiring speed at which developers push new code into CI/CD pipelines. So much code, so little time.
The code property graph – a multi-layered graph representation of code that captures properties of code across different abstractions – (application code, libraries and frameworks) – has been developed over the last six years to provide a foundation for the challenging problem of identifying flaws in program code at scale, whether it is high-level dynamically-typed Javascript, statically-typed Scala in its bytecode form, the syntax trees generated by Roslyn C# compiler, or the bitcode that flows through LLVM.
Based on this graph, we define a common query language based on formal code property graph specification to elegantly analyze code regardless of the source language. Paired with the formulation of a state-of-the-art data flow tracker based on code property graphs, we arrive at a distributed cloud native powerful code analysis. This talk provides an introduction to the technology.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
Spark Summit EU 2015: Matei Zaharia keynoteDatabricks
2015 was a year of continued growth for Spark, with numerous additions to the core project and very fast growth of use cases across the industry. In this talk, I’ll look back at how the Spark community is has grown and changed in 2015, based on a large Apache Spark user survey conducted by Databricks. We see some interesting trends in the diversity of runtime environments (which are increasingly not just Hadoop); the types of applications run on Spark; and the types of users, now that features like R support and DataFrames are available in Spark. I’ll also cover the ongoing work in the upcoming releases of Spark to support new use cases.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Applying large scale text analytics with graph databasesData Ninja API
Data Ninja Services collaborated with Oracle to reach a major milestone in the integration of text analytics with Oracle Spatial and Graph. The Data Ninja Services client in Java can be used to analyze free texts, extract entities, generate RDF semantic graphs, and choose from a number of graph analytics to infer entity relationships. We demonstrated two case studies involving mining health news and detecting anomalies in product reviews.
10 big data analytics tools to watch out for in 2019JanBask Training
The long-standing boss in the field of Big Data processing understood for its capacities for gigantic scale information handling.
https://www.janbasktraining.com/hadoop-big-data-analytics
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...Amazon Web Services
In this session, we will demonstrate how you can easily start using graph databases to solve your business problems. We will demonstrate setting up a Neptune instance, loading the dataset and using Gremlin and SPARQL via Java to build a application. We will also cover scaling, availability and administrative aspects of the Neptune service.
Webinar: What's new in Linkurious Enterprise 2.8Linkurious
With Linkurious Enterprise 2.8, we focused on time filtering with a Timeline and on providing more control on data entry and data presentation with support for data schema.
We discuss:
- The benefits of a schema and how to set it up.
- How to explore and filter the graph over time using the Timeline.
- How to disable the auto-save mode.
We also discuss the changes in the graph layout menu.
For decades, the intelligence community has been collecting and analyzing information to produce timely and actionable insights for intelligence consumers. But as the amount of information collected increases, analysts are facing new challenges in terms of data processing and analysis. In this presentation, we explore the possibilities that graph technology is offering for intelligence analysis.
The slides from a presentation of Linkurious Enterprise, in which we look at what’s new in this release, including the new Query Template capabilities and the redesign of the query panel.
Video of the presentation: https://youtu.be/ucuntmqzTYI
3 types of fraud graph analytics can help defeatLinkurious
Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. In this presentation, we examine three types of fraud graph analytics can help investigators combat. Blog post: https://linkurio.us/blog/3-fraud-graph-analytics-help-defeat/
Graph technology and data-journalism: the case of the Paradise PapersLinkurious
Discover how graph analysis and visualization technologies allowed the ICIJ journalists to highlight the suspicious relations between political figures and offshore companies in the Paradise Papers investigations.
Visualize the Knowledge Graph and Unleash Your DataLinkurious
Slides from the webinar "Visualize the Knowledge Graph and Unleash Your Data" with Michael Grove, Vice President of Engineering and co-founder of Stardog, and Jean Villedieu, co-founder of Linkurious.
The webinar covers the topic of enterprise Knowledge Graphs and lets you experience how to visualize and analyze this data to discover actionable insights for your organization.
Fraudes Financières: Méthodes de Prévention et DétectionLinkurious
Cette présentation en partenariat avec DataStax revient sur comment détecter en temps réel des activités frauduleuses telles que la fraude identitaire. Des applications concrètes de ces technologies seront détaillées, de l’affaire des Panama Papers à des cas d’usages quotidiens dans des banques et des institutions financières. Les techniques de lutte antifraude ainsi que les avantages des approches orientées graphe seront également présentés.
Detecting eCommerce Fraud with Neo4j and LinkuriousLinkurious
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.
Slides of Linkurious presentation at GraphConnect London 2017.
Tracking the flow of data is the foundation for solid data governance. It's also a compliance imperative for financial institutions impacted by BCBS 239. In this talk, we will discuss how graph-oriented data lineage is well suited for today's growing data volume and complexity. You will learn how to answer questions like: What would be the impact of a component of my data pipeline breaking up? Where does the data from a particular report originate?
Using Linkurious in your Enterprise Architecture projectsLinkurious
Architects, analysts and business managers need comprehensive modeling and visualization tools to understand how companies assets are assembled. Graph technologies allow to understand complex connected data and manage change and complexity in a more efficient way than traditional siloed solutions. With Linkurious technology, you get a comprehensive and visual overview of your enterprise architecture to successfully implement new systems, processes or frameworks.
Graph technologies have the potential to help businesses understand complex connected data. From financial crime to cyber-security to IT management, specific business requires custom applications. This is why we created the Linkurious SDK , a toolkit that enables you to quickly build secure and flexible applications to leverage the connections within your data or unveil hidden relationships.
Discover in this presentation the challenges of integrating graph technologies into enterprise applications; and how to use the Linkurious SDK to build a robust, secure and interactive graph application.
Fighting financial crime with graph analysis at BIWA Summit 2017Linkurious
Additional details on our blog: https://linkurio.us/visualize-oracle-graph-data-ogma-library/
Discover how to use graph analysis to identify suspicious connections and unmask criminals. In this session, Jean will share his experience working on the Panama Papers or with banks and insurance companies (first-party fraud, anti-money laundering, insurance fraud). He will explain how to combine the kind of graph analytics enabled by Oracle Spatial and Graph with powerful graph visualization to help analysts detect, investigate and stop financial crime.
Reinforcing AML systems with graph technologies.Linkurious
Anti-money laundering (AML) has become complex and costly for institutions and enterprises. Nowadays, to thwart criminal intricate strategies, financial crime units have to gather, monitor and investigate large amounts of connected data.
Graph analysis and visualization technologies can provide an holistic view of the various entities and their relationships to unveil wrongdoings.
Anti-money laundering (AML) has become complex and costly for institutions and enterprises. Graph analysis and visualization technologies like Linkurious are a great fit to help AML analysts fight money laundering.
Discover in this presentation how to automate the monitoring of high risk customers with patterns alerts and how to assess risk-levels by visually investigating suspicious cases.
More information on www.linkurio.us
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
5. 3store graph
3store
http://threestore.sourceforge.net/
About
3store is an RDF "triple store", written in C and backed by MySQL and Berkeley DB. It is an optimisation
and port of an older triple store (WebKBC). It provides access to the RDF data via RDQL or SPARQL over
HTTP, on the command line or via a C API.
6. 4store
4store
https://4store.github.io/
About
4store was designed by Steve Harris and developed at Garlik to underpin their Semantic Web
applications. It has been providing the base platform for around 3 years. At times holding and running
queries over databases of 15GT, supporting a Web application used by thousands of people.
7. AgensGraph
AgensGraph
https://bitnine.net/agensgraph-2/
About
AgensGraph is a multi-model database, which supports the relational and graph data model at the same
time that enables developers to integrate the legacy relational data model and the flexible graph data
model in one database. AgensGraph supports ANSI-SQL and openCypher (http://www.opencypher.org).
SQL queries and Cypher queries can be integrated into a single query in AgensGraph.
8. Ajgu
Ajgu
https://bitbucket.org/amirouche/ajgu-gr
aphdb
About
Ajgu is graph database backed by Oracle Berkeley Database key/value store aka. bsddb. It's meant to be
an easy to use, just works persistent graph for people that want to experiment with graph databases.
Somekind of SQLite for graph databases in Python.
9. AllegroGraph
AllegroGraph
https://franz.com/agraph/allegrograph/
About
AllegroGraph® is a modern, high-performance, persistent graph database. AllegroGraph uses efficient
memory utilization in combination with disk-based storage, enabling it to scale to billions of quads while
maintaining superior performance. AllegroGraph supports SPARQL, RDFS++, and Prolog reasoning from
numerous client applications.
AllegroGraph is Linkurious partner - more information
11. Amazon Neptune
Amazon Neptune
https://aws.amazon.com/neptune/
About
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and
run applications that work with highly connected datasets. Amazon Neptune supports popular graph
models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin
and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets.
12. Apache Jena TBD
https://jena.apache.org/documentation/t
db/index.html
About
Apache Jena is an open source Semantic Web framework for Java. It provides an API to extract data
from and write to RDF graphs. TDB is a component of Jena for RDF storage and query. It support the full
range of Jena APIs. TDB can be used as a high performance RDF store on a single machine.
Apache Jena TBD
13. Apache Rya
Apache Rya
https://rya.incubator.apache.org/
About
Rya is a cloud-based RDF triple store that supports SPARQL queries. Rya is a scalable RDF data
management system built on top of Apache Accumulo®. Rya uses novel storage methods, indexing
schemes, and query processing techniques that scale to billions of triples across multiple nodes. Rya
provides fast and easy access to the data through SPARQL, a conventional query mechanism for RDF
data.
14. Apache S2Graph
Apache S2Graph
https://s2graph.apache.org/
About
Apache S2Graph is a graph database designed to handle transactional graph processing at scale. Its
REST API allows you to store, manage and query relational information using edge and vertex
representations in a fully asynchronous and non-blocking manner.
15. ArangoDB
ArangoDB
https://www.arangodb.com/
About
ArangoDB is a native multi-model database system developed by ArangoDB Inc. The database system
supports three data models with one database core and a unified query language AQL. The query
language is declarative and allows the combination of different data access patterns in a single query.
16. Arc2
Arc2
https://github.com/semsol/arc2
About
ARC is a flexible RDF database for semantic web and PHP practitioners. It's free, open-source, easy to
use, and runs in most web server environments (it's PHP 5.3 E_STRICT-compliant).
17. Azure Cosmos DB
Azure Cosmos DB
https://azure.microsoft.com/services/co
smos-db/
About
Azure Cosmos DB is the globally distributed, multi-model database service from Microsoft for
mission-critical applications. It is a multi-model database and supports document, key-value, graph, and
columnar data models. The Azure Cosmos DB Gremlin API is used to store and operate on graph data.
Gremlin API supports modeling Graph data and provides APIs to traverse through the graph data.
18. BadWolf
BadWolf
https://google.github.io/badwolf/
About
BadWolf is a temporal graph store loosely modeled after the concepts introduced by the Resource
Description Framework (RDF). It presents a flexible storage abstraction, efficient query language, and
data-interchange model for representing a directed graph that accommodates the storage and linking of
arbitrary objects without the need for a rigid schema.
19. Bitsy
Bitsy
https://github.com/lambdazen/bitsy/wik
i
About
Bitsy is a embeddable in-memory graph database that is compatible with Tinkerpop3. It is based on
these design principles: "No seek" (avoid disk seeks to maximize write throughput), "No socket" (embed
within the application to reduce network/OS delays) and "No SQL" (leverage graph traversals for faster
queries).
20. Blazegraph
Blazegraph
https://www.blazegraph.com/
About
Blazegraph is an ultra-scalable, high-performance graph database with support for the Blueprints and
RDF/SPARQL APIs. It supports up to 50 Billion edges on a single machine. It is in production use for
Fortune 500 customers such as EMC, Autodesk, and many others. It was selected by the Wikimedia
foundation to power their wikidata query service.
21. BrightstarDB
BrightstarDB
https://www.blazegraph.com/
About
BrightstarDB is a native .NET RDF triple store. It uses dotNetRDF to provide support for a wide range of
RDF syntaxes as well as SPARQL query support. In addition to providing a raw RDF-based API,
BrightstarDB also provides support for binding RDF resources to .NET dynamic objects; and a
contract-first entity framework that enables the use of LINQ rather than SPARQL for query purposes.
22. Cayley
Cayley
https://cayley.io/
About
Cayley is an open-source graph inspired by the graph database behind Freebase and Google's
Knowledge Graph. It is built with RDF support, including multiple linked data formats such as NQuads
and JSON-LD. Cayley works on top of your existing database regardless of data model: SQL, NoSQL or
even KV and support multiple query languages Gizmo (Gremlin dialect), MQL and GraphQL dialect.
23. ChronoGraph
ChronoGraph
https://cayley.io/
About
ChronoGraph is one of the major components of Chronos. It is a versioned graph database and an
official implementation of the Apache TinkerPop AP. As with all Chronos Components, ChronoGraph is
written in 100% pure Java and should run in any environment supported by JRE 1.8 or later.
24. Cray Graph Engine
Cray Graph Engine
https://www.cray.com/products/analytic
s/cray-graph-engine
About
The Cray Graph Engine (CGE) is a semantic database using Resource Description Framework (RDF)
triples to represent the data, SPARQL as the query language and extensions to support mathematical
algorithms. CGE is a highly optimized software application designed by high-speed processing of
interconnected data. It features an advanced platform for searching very large, graph-oriented databases
and querying for complex relationships between data items in the database.
25. DataStax Enterprise Graph
DataStax Enterprise Graph
https://www.datastax.com/products/dat
astax-enterprise-graph
About
DSE Graph is an add-on to DataStax Enterprise that enables enterprises to identify and analyze hidden
relationships between connected data to build powerful applications for fraud detection, customer 360,
social networks, and real-time recommendations. Datastax Enterprise is the commercial distribution of
Apache Cassandra, a column-family NoSQL database
Datastax is Linkurious partner - more information
26. DegDB
DegDB
https://github.com/degdb/degdb
About
The Distributed Economic Graph Database is a graph database management system where every
request has either a debit (with attached bitcoin) or a credit (with bitcoin promised on delivery) payment
system. The DBMS's server nodes estimate how much it will cost to serve the data; if there is not enough
bitcoin attached to service the request, then the node will drop the request.
27. Dgraph
Dgraph
https://dgraph.io/
About
Dgraph is a horizontally scalable and distributed graph database, providing ACID transactions,
consistent replication and linearizable reads. It's built from ground up to perform for a rich set of queries.
Being a native graph database, it tightly controls how the data is arranged on disk to optimize for query
performance and throughput, reducing disk seeks and network calls in a cluster.
28. DuctileDB
DuctileDB
https://ductiledb.com/home
About
DuctileDB is a graph database inspired by Titan and Neo4j. Combining Titan's large graph storage idea
based on HBase and the rich features by Neo4j, DuctileDB goes to be an alternative graph database for
very large graphs.
29. Dydra
Dydra
https://dydra.com/
About
Dydra is a cloud-based graph database. It is the result of years of research and development in
distributed semantic data technologies and represents a next generation, adaptive API and data
management framework built from the ground up to integrate and deliver value from advances in ML
and AI.
30. FaunaDB
FaunaDB
https://fauna.com/faunadb
About
FaunaDB combines the enterprise capabilities of relational databases with scale and flexibility of
non-relational systems. Featuring multi-region strong consistency, relational modeling, schema flexibility,
and unlimited horizontal scale, FaunaDB is purpose-built for today's cloud-based OLTP apps.
31. FlockDB
FlockDB
https://github.com/twitter-archive/flockd
b
About
FlockDB is a distributed graph database for storing adjancency lists, with goals of supporting. Twitter
uses FlockDB to store social graphs (who follows whom, who blocks whom) and secondary indices. As
of April 2010, the Twitter FlockDB cluster stores 13+ billion edges and sustains peak traffic of 20k
writes/second and 100k reads/second. Twitter is no longer maintaining this project or responding to
issues or PRs.
33. Gaffer
Gaffer
https://github.com/gchq/Gaffer
About
Gaffer is a graph database framework. It allows the storage of very large graphs containing rich
properties on the nodes and edges. Several storage options are available, including Accumulo, Hbase
and Parquet. It is designed to be as flexible, scalable and extensible as possible, allowing for rapid
prototyping and transition to production systems.
34. Grakn
Grakn
https://github.com/gchq/Gaffer
About
Grakn is the knowledge graph engine to organise complex networks of data and making it queryable, by
performing knowledge engineering. Rooted in Knowledge Representation and Automated Reasoning,
Grakn provides the knowledge foundation for cognitive and intelligent (e.g. AI) systems, by providing an
intelligent language for modelling, transactions and analytics. Being a distributed database, Grakn is
designed to scale over a network of computers through partitioning and replication.
35. GraphBase
https://graphbase.ai/
About
GraphBase makes massive, highly-structured data stores possible because it was built from scratch to
manage large graphs and not tacked on top of an RDBMS, OODBMS or other early technology. It
dramatically simplifies working with graph-structured data because it's the only database that lets you
think about graphs - by using graphs. And it comes with query functionality designed for graphs.
GraphBase
36. GraphBase
https://www.ontotext.com/products/gra
phdb/
About
GraphDB is an enterprise ready Semantic Graph Database, compliant with W3C Standards. Semantic
graph databases (also called RDF triplestores) provide the core infrastructure for solutions where
modelling agility, data integration, relationship exploration and cross-enterprise data publishing and
consumption are important.
GraphBase
37. Graph Engine Service
https://www.huaweicloud.com/en-us/pr
oduct/ges.html
About
Graph Engine Service (GES) provides distributed, at-scale, and integrated graph search and analysis
capabilities. Its high-performance kernel supports high-concurrency, multi-hop, real-time queries. GES
has extensive built-in algorithm libraries and applies to social networking, precision marketing, credit
insurance, and network and path planning.
Graph Engine Service
38. gStore
https://github.com/pkumod/gStore
About
Gstore System is a graph database engine for managing large graph-structured data, which is
open-source and targets at Linux operation systems. The whole project is written in C++, with the help of
some libraries such as readline, antlr, and so on. Only source tarballs are provided currently, which
means you have to compile the source code if you want to use our system.
gStore
39. Halyard
https://github.com/pkumod/gStore
About
Halyard is an extremely horizontally scalable triple store with support for named graphs, designed for
integration of extremely large semantic data models and for storage and SPARQL 1.1 querying of
complete Linked Data universe snapshots. Halyard implementation is based on Eclipse RDF4J
framework and Apache HBase database, and it is completely written in Java.
Halyard
40. Halyard
https://merck.github.io/Halyard/
About
Halyard is an extremely horizontally scalable triple store with support for named graphs, designed for
integration of extremely large semantic data models and for storage and SPARQL 1.1 querying of
complete Linked Data universe snapshots. Halyard implementation is based on Eclipse RDF4J
framework and Apache HBase database, and it is completely written in Java.
Halyard
42. HugeGraph
https://github.com/hugegraph/hugegrap
h
About
HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be
easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to
Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through
Gremlin(a powerful graph traversal language).
HugeGraph
43. HyperGraphDB
http://www.hypergraphdb.org/
About
HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful
knowledge management formalism known as directed hypergraphs. While a persistent memory model
designed mostly for knowledge management, AI and semantic web projects, it can also be used as an
embedded object-oriented database for Java projects of all sizes. Or a graph database. Or a (non-SQL)
relational database.
HyperGraphDB
44. IBM DB2-RDF
http://www.hypergraphdb.org/
About
On IBM DB2-RDF support is called "NoSQL Graph Support". The DB2-RDF functionality is being released
with DB2 LUW 10.1, it is also compatible with DB2 9.7. While RDBMS implementations of RDF graphs
have typically been non-performant, that is not the case here. Some very impressive and innovative work
has been put into optimization capabilities. Out-of-the box performance is comparable with native triple
stores, and read/write performance in the optimized schema has been seen to surpass these speeds.
IBM DB2-RDF
45. InfiniteGraph
https://www.objectivity.com/products/in
finitegraph/
About
InfiniteGraph is a highly specialized graph database. Its functionality is being migrated into ThingSpan.
However, Objectivity will continue to support licensed users and will recommend it to Java developers
who wish to use graph analytics outside of a Spark environment. Specific features, such as pathfinding,
have been merged into the underlying database - Objectivity/DB.
InfiniteGraph
46. InfoGrid
https://infogrid.org/
About
InfoGrid is an open-source internet graph database with REST-ful web frontend. Represents information
as nodes and edges which may be dynamically typed according to freely definable conceptual models.
Can dynamically include and keep up-to-date externally-managed information “as-if” it was native to
InfoGrid.
InfoGrid
47. JanusGraph
http://janusgraph.org/
About
JanusGraph is a highly scalable graph database optimized for storing and querying large graphs with
billions of vertices and edges distributed across a multi-machine cluster. JanusGraph is a transactional
database that can support thousands of concurrent users, complex traversals, and analytic graph
queries.
JanusGraph is Linkurious partner - more information
JanusGraph
48. KiWi Triplestore
https://marmotta.apache.org/kiwi/index.
html
About
The KiWi triple store is a high performance transactional triple store backend for OpenRDF Sesame
building on top of a relational database (currently H2, PostgreSQL, or MySQL). It has optional support for
rule-based reasoning (sKWRL) and versioning of updates. The KiWi triple store is also the default
backend for Apache Marmotta. It originated in the EU-funded research project “KiWi - Knowledge in a
Wiki” (hence the name).
KiWi Triplestore
52. Memgraph
https://memgraph.com/product/
About
Memgraph is an in-memory graph database technology. It is presented as the next evolution in graph
databases, built from the ground up to deliver real‑time insights across your enterprise connected data.
Memgraph
53. Microsoft SQL Server
https://docs.microsoft.com/en-us/sql/re
lational-databases/graphs/sql-graph-arc
hitecture
About
With the release of SQL Server 2017, Microsoft added support for graph databases to better handle data
sets that contain complex entity relationships, such as the type of data generated by a social media site,
where you can have a mix of many-to-many relationships that change frequently. Microsoft SQL Server is a
relational database management system developed by Microsoft
Microsoft SQL Server
55. Neo4j
https://neo4j.com/
About
Neo4j is a graph database management system developed by Neo4j, Inc. Described by its developers as an
ACID-compliant transactional database with native graph storage and processing, Neo4j is the most popular graph
database according to DB-Engines ranking, and the 22ⁿᵈ most popular database overall.
Neo4j is Linkurious partner - more information
Neo4j
57. OpenCog AtomSpace
https://github.com/opencog/atomspace
About
The OpenCog AtomSpace is a knowledge representation (KR) database and the associated
query/reasoning engine to fetch and manipulate that data, and perform reasoning on it. Data is
represented in the form of graphs, and more generally, as hypergraphs; thus the AtomSpace is a kind of
graph database, the query engine is a general graph re-writing system, and the rule-engine is a
generalized rule-driven inferencing system.
OpenCog AtomSpace
58. Oracle Spatial and Graph
https://www.oracle.com/technetwork/da
tabase/options/spatialandgraph/overvie
w/index.html
About
Oracle Spatial and Graph includes high performance, enterprise-scale, commercial spatial and graph
database and analytics for Oracle Database 18c, in the cloud and on premises. It supports enterprise
business, business intelligence, large-scale Geographic Information Systems, and location services
applications.
Oracle Spatial and Graph
59. OrientDB
https://orientdb.com/why-orientdb/
About
OrientDB is an open source NoSQL database management system written in Java. It is a multi-model
database, supporting graph, document, key/value, and object models, but the relationships are managed
as in graph databases with direct connections between records.
OrientDB
60. Parliament
http://parliament.semwebcentral.org/
About
Parliament™ is a high-performance triple store designed for the Semantic Web. Parliament was originally
developed under the name DAML DB and was extended by BBN Technologies for internal use in its R&D
programs. Parliament was released as an open source project under the BSD license here on
SemWebCentral in June, 2009.
Parliament
61. Pointrel System
https://sourceforge.net/projects/pointrel
/
About
The Pointrel System is an RDF-like triple store implemented on the Java/JVM platform, supporting
related social semantic desktop applications to create, use, exchange, and organize informational
resources for a reasonably joyful and secure world.
Pointrel System
62. Profium Sense
https://www.profium.com/en/technologi
es/
About
Profium Sense is an in-memory NoSQL graph database, which provides native support for RDF and
OWL2 RL level of reasoning support. Profium Sense rule engine has a patented forward-chaining
algorithm optimized for frequent updates. Profium Sense has a graphical ontology editor and a related
API for making ontology changes at runtime without requiring a system restart.
Profium Sense
63. RDF4J
http://rdf4j.org/rdf4j-databases/
About
The RDF4J Native Store is a transactional RDF database using direct disk IO for persistence. It is a more
scalable solution than the memory store, with a smaller memory footprint, and also offers better
consistency and durability. It is currently aimed at medium-sized datasets in the order of 100 million
triples.
RDF4J
64. RDFBroker
http://rdfbroker.opendfki.de/
About
RDFBroker is an RDF store that uses a natural mapping of RDF resources to database tables that does
not rely on RDF Schema, but constructs a schema based on the occurring signatures, where a signature
is the set of properties used on a resource. It can be used for both in-memory and normal (on-disk)
relational database-based RDF store implementations, and also distributed RDF stores (with distributed
query handling) benefit from it.
RDFBroker
66. RedStore
https://www.aelius.com/njh/redstore/
About
RedStore is a lightweight RDF triplestore written in C using the Redland library. It has a HTTP interface
and supports the following W3C standards: Built-in HTTP server; Mac OS X app available; Supports a
wide range of RDF formats; Only runtime dependancy is Redland ; Compatible with rdfproc command
line tool for offline operations; Unit and integration test suite.
RedStore
67. SAP Hana Graph Engine
https://blogs.sap.com/2016/08/01/what
-s-new-in-sap-hana-sps12-sap-hana-grap
h-engine/
About
SAP HANA Graph is an integral part of SAP HANA core functionality. It expands the SAP HANA platform
with native support for graph processing and allows executing typical graph operations on the data
stored in an SAP HANA system. SAP HANA is an in-memory, column-oriented, relational database
management system developed and marketed by SAP SE.
SAP Hana Graph Engine
68. Ruruki
https://github.com/optiver/ruruki
About
Ruruki is a in-memory directed property graph database tool used for building complicated graphs of
anything. It is useful for temporary lightweight graph database. You can install it in a python virtual
environment and be up and running in no time.
Ruruki
69. SimpleGraph
https://github.com/enterlab/simplegraph
About
SimpleGraph is really Simple, rudimentary Graph (in-memory) DB implemented in Java. Can be used as
an application cache storage, and is really fast mainly meant for people that want to find out how a
graph database works, by looking at code instead of reading books. The SimpleGraph is implemented as
a TripleStore, containing tuples (well, actually triples) of Subject, Object and Predicate.
SimpleGraph
71. Stardog
https://www.stardog.com/
About
Stardog is a RDF database. Stardog’s Knowledge Graph platform enables fast and flexible data
unification so you can query, analyze, and uncover hidden insights.
Stardog is Linkurious certified partner - more information
Stardog
72. Steffi
http://steffi.io/
About
STEFFI is a distributed graph database fully in-memory and amazingly fast when it comes to querying
large datasets. It provides its users with a clear competitive advantage when it comes to complicated
traversal operations on large datasets.
Steffi
73. Strabon
http://www.strabon.di.uoa.gr/
About
Strabon is a spatiotemporal RDF store. You can use it to store linked geospatial data that changes over
time and pose queries using two popular extensions of SPARQL. Strabon supports spatial datatypes
enabling the serialization of geometric objects in OGC standards WKT and GML
Strabon
75. TigerGraph
https://www.tigergraph.com/
About
Through its Native Parallel Graph™ technology, the TigerGraph™ graph platform represents what’s next in
the graph database evolution: a complete, distributed, parallel graph computing platform supporting
web-scale data analytics in real-time.
TigerGraph
76. TinkerGraph
https://tinkerpop.apache.org/docs/curre
nt/reference/#tinkergraph-gremlin
About
TinkerGraph is a single machine, in-memory (with optional persistence), non-transactional graph engine
that provides both OLTP and OLAP functionality. It is deployed with TinkerPop and serves as the
reference implementation for other providers to study in order to understand the semantics of the
various methods of the TinkerPop API.
TinkerGraph
77. Titan
http://titan.thinkaurelius.com/
About
Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of
billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional
database that can support thousands of concurrent users executing complex graph traversals in real
time.
Titan
80. Weaver
http://weaver.systems/
About
Weaver is a distributed graph store that provides horizontal scalability, high-performance, and strong
consistency. Weaver enables users to execute transactional graph updates and queries through a simple
python API.
Weaver
81. WhiteDB
http://whitedb.org/index.html
About
WhiteDB is a lightweight NoSQL database library written in C, operating fully in main memory. There is no
server process. Data is read and written directly from/to shared memory, no sockets are used between
WhiteDB and the application program.
WhiteDB