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.
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 1: Graph DatabasesLinkurious
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.
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.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
https://www.eventbrite.com/e/talk-by-paco-nathan-graph-analytics-in-spark-tickets-17173189472
Big Brains meetup hosted by BloomReach, 2015-06-04
Case study / demo of a large-scale graph analytics project, leveraging GraphX in Apache Spark to surface insights about open source developer communities — based on data mining of their email forums. The project works with any Apache email archive, applying NLP and machine learning techniques to analyze message threads, then constructs a large graph. Graph analytics, based on concise Scala coding examples in Spark, surface themes and interactions within the community. Results are used as feedback for respective developer communities, such as leaderboards, etc. As an example, we will examine analysis of the Spark developer community itself.
Functional programming for optimization problems in Big DataPaco Nathan
Enterprise Data Workflows with Cascading.
Silicon Valley Cloud Computing Meetup talk at Cloud Tech IV, 4/20 2013
http://www.meetup.com/cloudcomputing/events/111082032/
We all know good training data is crucial for data scientists to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important. Consider for example:
- Provenance of model allowing for reproducible builds
- Context to comply with GDPR, CCPA requirements
- Identifying data shift in your production data
This is the reason we built ArangoML Pipeline, a flexible Metadata store which can be used with your existing ML Pipeline.
Today we are happy to announce a release of ArangoML Pipeline Cloud. Now you can start using ArangoML Pipeline without having to even start a separate docker container.
In this webinar, we will show how to leverage ArangoML Pipeline Cloud with your Machine Learning Pipeline by using an example notebook from the TensorFlow tutorial.
Find the video here: https://www.arangodb.com/arangodb-events/arangoml-pipeline-cloud/
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 1: Graph DatabasesLinkurious
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.
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.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
https://www.eventbrite.com/e/talk-by-paco-nathan-graph-analytics-in-spark-tickets-17173189472
Big Brains meetup hosted by BloomReach, 2015-06-04
Case study / demo of a large-scale graph analytics project, leveraging GraphX in Apache Spark to surface insights about open source developer communities — based on data mining of their email forums. The project works with any Apache email archive, applying NLP and machine learning techniques to analyze message threads, then constructs a large graph. Graph analytics, based on concise Scala coding examples in Spark, surface themes and interactions within the community. Results are used as feedback for respective developer communities, such as leaderboards, etc. As an example, we will examine analysis of the Spark developer community itself.
Functional programming for optimization problems in Big DataPaco Nathan
Enterprise Data Workflows with Cascading.
Silicon Valley Cloud Computing Meetup talk at Cloud Tech IV, 4/20 2013
http://www.meetup.com/cloudcomputing/events/111082032/
We all know good training data is crucial for data scientists to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important. Consider for example:
- Provenance of model allowing for reproducible builds
- Context to comply with GDPR, CCPA requirements
- Identifying data shift in your production data
This is the reason we built ArangoML Pipeline, a flexible Metadata store which can be used with your existing ML Pipeline.
Today we are happy to announce a release of ArangoML Pipeline Cloud. Now you can start using ArangoML Pipeline without having to even start a separate docker container.
In this webinar, we will show how to leverage ArangoML Pipeline Cloud with your Machine Learning Pipeline by using an example notebook from the TensorFlow tutorial.
Find the video here: https://www.arangodb.com/arangodb-events/arangoml-pipeline-cloud/
Applying graph analytics on data stored in relational databases can provide tremendous value in many application domains. We discuss the importance of leveraging these analyses, and the challenges in enabling them. We present a tool, called GraphGen, that allows users to visually explore, and rapidly analyze (using NetworkX) different graph structures present in their databases.
Webinar: ArangoDB 3.8 Preview - Analytics at Scale ArangoDB Database
The ArangoDB community and team are proud to preview the next version of ArangoDB, an open-source, highly scalable graph database with multi-model capabilities. Join our CTO, Jörg Schad, Ph.D. and Developer Relation Engineer Chris Woodward in this webinar to learn more about ArangoDB 3.8 and the roadmap for upcoming releases.
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.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
The Briefing Room with Dr. Robin Bloor and SYSTAP
Live Webcast June 30, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0ff3889293f6c090483295fd7362c5a4
There's a reason why the biggest Web companies these days leverage graph technology: it is incredibly powerful for revealing a wide range of insights. Unlike other analytical databases, graph can very quickly identify the kinds of patterns that lead to better business decisions. Though relatively nascent in existing data centers, graph databases are proving to be well-suited for all kinds of business use cases, from clustering and hypothesis generation to failure detection and cyber analytics.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how semantic technology fits in the spectrum of database and discovery solutions. He’ll be briefed by Brad Bebee of SYSTAP, who will showcase his company’s Blazegraph products and Mapgraph technology. He will explain how SYSTAP’s approach overcomes the challenge of scalability, and how graph technology’s powerful data management capabilities can deliver better enterprise performance and analytics using GPUs and other approaches.
Visit InsideAnalysis.com for more information.
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MorePaco Nathan
Spark and Databricks component of the O'Reilly Media webcast "2015 Data Preview: Spark, Data Visualization, YARN, and More", as a preview of the 2015 Strata + Hadoop World conference in San Jose http://www.oreilly.com/pub/e/3289
These are the slides to the webinar about Custom Pregel algorithms in ArangoDB https://youtu.be/DWJ-nWUxsO8. It provides a brief introduction to the capabilities and use cases for Pregel.
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
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
1st in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Xavier Lopez (PM Senior Director) and Zhe Wu (Graph Architect) will share a brief intro to what property graphs can do for you, and take your questions - on property graphs or any other aspect of Oracle Database Spatial and Graph features. With property graphs, you can analyze relationships in Big Data like social networks, financial transactions, or IoT sensor networks; identify influencers; discover patterns of fraudulent behavior; recommend products, and much more -- right inside Oracle Database.
Graph Analytics on Data from Meetup.comKarin Patenge
How to improve your Meetup experience by using Graph Analytics on data from Meetup.com. Slides from my session with "Women Who Code" group in Berlin on May 23, 2018.
Building A Hybrid Warehouse: Efficient Joins between Data Stored in HDFS and ...Yuanyuan Tian
With the advent of big data, the enterprise analytics landscape has dramatically changed. The HDFS has become an important data repository for all business analytics. Enterprises are using various big data technologies to process data and drive actionable insights. HDFS serves as the storage where other distributed processing frameworks, such as Hadoop and Spark, access and operate on large volumes of data. At the same time, enterprise data warehouses (EDWs) continue to support critical business analytics. EDWs are usually shared-nothing parallel databases that support complex SQL processing, updates, and transactions. As a result, they manage up-to-date data and support various business analytics tools, such as reporting and dashboards. A new generation of applications have emerged, requiring access and correlation of data stored in HDFS and EDWs. This has created the need for a new generation of a special federation between Hadoop-like big data platforms and EDWs, which we call the hybrid warehouse. In this talk, we identify the best hybrid warehouse architecture by studying various algorithms to join database and HDFS tables.
Applying graph analytics on data stored in relational databases can provide tremendous value in many application domains. We discuss the importance of leveraging these analyses, and the challenges in enabling them. We present a tool, called GraphGen, that allows users to visually explore, and rapidly analyze (using NetworkX) different graph structures present in their databases.
Webinar: ArangoDB 3.8 Preview - Analytics at Scale ArangoDB Database
The ArangoDB community and team are proud to preview the next version of ArangoDB, an open-source, highly scalable graph database with multi-model capabilities. Join our CTO, Jörg Schad, Ph.D. and Developer Relation Engineer Chris Woodward in this webinar to learn more about ArangoDB 3.8 and the roadmap for upcoming releases.
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.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
The Briefing Room with Dr. Robin Bloor and SYSTAP
Live Webcast June 30, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0ff3889293f6c090483295fd7362c5a4
There's a reason why the biggest Web companies these days leverage graph technology: it is incredibly powerful for revealing a wide range of insights. Unlike other analytical databases, graph can very quickly identify the kinds of patterns that lead to better business decisions. Though relatively nascent in existing data centers, graph databases are proving to be well-suited for all kinds of business use cases, from clustering and hypothesis generation to failure detection and cyber analytics.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how semantic technology fits in the spectrum of database and discovery solutions. He’ll be briefed by Brad Bebee of SYSTAP, who will showcase his company’s Blazegraph products and Mapgraph technology. He will explain how SYSTAP’s approach overcomes the challenge of scalability, and how graph technology’s powerful data management capabilities can deliver better enterprise performance and analytics using GPUs and other approaches.
Visit InsideAnalysis.com for more information.
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MorePaco Nathan
Spark and Databricks component of the O'Reilly Media webcast "2015 Data Preview: Spark, Data Visualization, YARN, and More", as a preview of the 2015 Strata + Hadoop World conference in San Jose http://www.oreilly.com/pub/e/3289
These are the slides to the webinar about Custom Pregel algorithms in ArangoDB https://youtu.be/DWJ-nWUxsO8. It provides a brief introduction to the capabilities and use cases for Pregel.
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
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
1st in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
Xavier Lopez (PM Senior Director) and Zhe Wu (Graph Architect) will share a brief intro to what property graphs can do for you, and take your questions - on property graphs or any other aspect of Oracle Database Spatial and Graph features. With property graphs, you can analyze relationships in Big Data like social networks, financial transactions, or IoT sensor networks; identify influencers; discover patterns of fraudulent behavior; recommend products, and much more -- right inside Oracle Database.
Graph Analytics on Data from Meetup.comKarin Patenge
How to improve your Meetup experience by using Graph Analytics on data from Meetup.com. Slides from my session with "Women Who Code" group in Berlin on May 23, 2018.
Building A Hybrid Warehouse: Efficient Joins between Data Stored in HDFS and ...Yuanyuan Tian
With the advent of big data, the enterprise analytics landscape has dramatically changed. The HDFS has become an important data repository for all business analytics. Enterprises are using various big data technologies to process data and drive actionable insights. HDFS serves as the storage where other distributed processing frameworks, such as Hadoop and Spark, access and operate on large volumes of data. At the same time, enterprise data warehouses (EDWs) continue to support critical business analytics. EDWs are usually shared-nothing parallel databases that support complex SQL processing, updates, and transactions. As a result, they manage up-to-date data and support various business analytics tools, such as reporting and dashboards. A new generation of applications have emerged, requiring access and correlation of data stored in HDFS and EDWs. This has created the need for a new generation of a special federation between Hadoop-like big data platforms and EDWs, which we call the hybrid warehouse. In this talk, we identify the best hybrid warehouse architecture by studying various algorithms to join database and HDFS tables.
Introduction to GCP DataFlow PresentationKnoldus Inc.
In this session, we will learn about how Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.
Big Graph : Tools, Techniques, Issues, Challenges and Future Directions csandit
Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks,
knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing
scalable systems for processing and analyzing large scale graphs has become one of the
timeliest problems facing the big data research community. In general, distributed processing of
big graphs is a challenging task due to their size and the inherent irregular structure of graph
computations. In this paper, we present a comprehensive overview of the state-of-the-art to
better understand the challenges of developing very high-scalable graph processing systems. In
addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.
BIG GRAPH: TOOLS, TECHNIQUES, ISSUES, CHALLENGES AND FUTURE DIRECTIONScscpconf
Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing scalable systems for processing and analyzing large-scale graphs has become one of the timeliest problems facing the big data research community. In general, distributed processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. In this paper, we present a comprehensive overview of the state-of-the-art to better understand the challenges of developing very high-scalable graph processing systems. In addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.
Running Emerging AI Applications on Big Data Platforms with Ray On Apache SparkDatabricks
With the rapid evolution of AI in recent years, we need to embrace advanced and emerging AI technologies to gain insights and make decisions based on massive amounts of data. Ray (https://github.com/ray-project/ray) is a fast and simple framework open-sourced by UC Berkeley RISELab particularly designed for easily building advanced AI applications in a distributed fashion.
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
Open source grid middleware packages – Globus Toolkit (GT4) Architecture , Configuration – Usage of Globus – Main components and Programming model - Introduction to Hadoop Framework - Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write.
Discover How IBM Uses InfluxDB and Grafana to Help Clients Monitor Large Prod...InfluxData
IBM has been innovating to create new products for its clients and the world for over a century. Customers look to IBM Power Systems to address their hybrid multicloud infrastructure needs. Larger POWER9 servers can have up to 192 CPU cores, 64 TB of memory, dozens of PB of SAN storage, and typically run a mixture of AIX (UNIX) and Enterprise Linux (RHEL or SLES) workloads. As part of its sales process, IBM is always benchmarking its new hardware and software which clients use to monitor their systems. Discover how IBM and its clients are using InfluxDB and Grafana to collect, store and visualize performance data, which is used to monitor and tune for peak performance in ever-changing workload environments.
Join this webinar featuring Nigel Griffiths from IBM, Ronald McCollam from Grafana Labs, and Russ Savage from InfluxData to learn how you can use InfluxDB and Grafana to improve large production workloads. Learn about the latest product updates from InfluxData and Grafana Labs.
Apache AGE and the synergy effect in the combination of Postgres and NoSQLEDB
In this session, we will introduce the concept of Apache AGE and the synergy effect in the combination of Postgres and NoSQL (Graph Database). We shall discuss the story and background of Apache AGE as an open-source project and introduce challenges that AGE can solve for its users. Furthermore, we will talk about a graph database as an extension to PostgreSQL and how it can support all the functionalities and features of PostgreSQL and offers a graph model in addition. We will also discuss how users with a relational background and data model who are in need of having a graph model on top of their existing relational model, can use this extension with minimal effort because they can use existing data without migration to enable a graph database.
Faunus is a graph analytics engine built atop the Hadoop distributed computing platform. The graph representation is a distributed adjacency list, whereby a vertex and its incident edges are co-located on the same machine. Querying a Faunus graph is possible with a MapReduce-variant of the Gremlin graph traversal language. A Gremlin expression compiles down to a series of MapReduce-steps that are sequence optimized and then executed by Hadoop. Results are stored as transformations to the input graph (graph derivations) or computational side-effects such as aggregates (graph statistics). Beyond querying, a collection of input/output formats are supported which enable Faunus to load/store graphs in the distributed graph database Titan, various graph formats stored in HDFS, and via arbitrary user-defined functions. This presentation will focus primarily on Faunus, but will also review the satellite technologies that enable it.
Similar to GraphTech Ecosystem - part 2: Graph Analytics (20)
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
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.
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.
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.
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.
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.
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.
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
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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/
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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/
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
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
5. Apache Giraph
Apache Giraph
Distributed
Apache 2.0 Licence
https://giraph.apache.org/
About
Apache Giraph is an iterative graph processing system built for high scalability. For example, it is
currently used at Facebook to analyze the social graph formed by users and their connections. Giraph
originated as the open-source counterpart to Pregel, the graph processing architecture developed at
Google and described in a 2010 paper. Both systems are inspired by the Bulk Synchronous Parallel
model of distributed computation introduced by Leslie Valiant. Giraph adds several features beyond the
basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core
computation, and more.
6. Apache Hadoop Spark
Apache Hadoop Spark
Distributed
Apache License 2.0
http://spark.apache.org/
About
Spark is an Apache Software Foundation project focused on general-purpose OLAP data processing.
Spark provides a hybrid in-memory/disk-based distributed computing model that is similar to Hadoop’s
MapReduce model.
7. Apache Hama
Apache Hama
Distributed
Apache License 2.0
https://hama.apache.org/
About
Apache HamaTM is a framework for Big Data analytics which uses the Bulk Synchronous Parallel (BSP)
computing model, which was established in 2012 as a Top-Level Project of The Apache Software
Foundation. It is a top-level open source project to do advanced analytics beyond MapReduce.
8. Cassovary
Cassovary
Single system
Apache License 2.0
https://github.com/twitter/cassovary
About
Cassovary is a in-memory graph engine for the Java Virtual Machine (JVM) written in Scala. Cassovary is
designed from the ground up to efficiently handle graphs with billions of edges. It comes with some
common node and graph data structures and traversal algorithms. A typical usage is to do large-scale
graph mining and analysis.
10. Faunus
Faunus
Distributed
Apache License 2.0
https://github.com/thinkaurelius/faunus
About
Faunus is a distributed analytics engine for processing property graphs with Hadoop. A breadth-first
version of the graph traversal language Gremlin operates on a vertex-centric property graph data
structure. Faunus provides adaptors to the distributed graph database Titan, any Rexster fronted graph
database, and to text and binary graphs stored in HDFS. The provided Gremlin operations and Hadoop
graph tools can be extended using MapReduce and Blueprints.
11. FlashGraph
FlashGraph
Distributed
Apache License 2.0
https://github.com/Smerity/FlashGraph
About
FlashGraph is a semi-external memory graph processing engine, optimized for a high-speed SSD array.
FlashGraph provides flexible programming interface to help users implement graph algorithms. In
FlashGraph, users write serial code that reads data in memory and FlashGraph executes users' code in
parallel and out of core. It enables us to process a billion-node graph in a single machine and has
performance comparable to or exceed in-memory graph engines such as PowerGraph.
12. Galloy
Galloy
Distributed
BSD License
http://iss.ices.utexas.edu/?p=projects/g
alois
About
The Galois system permits application programmers to exploit amorphous data-parallelism in irregular
algorithms without having to write explicitly parallel code. The Galois library provides concurrent data
structures, schedulers, and memory allocators. The Galois runtime executes these programs in parallel,
using parallelization strategies such as optimistic and round-based execution. Galois runs on
shared-memory NUMA platforms and NVIDIA GPUs. A subset of the Galois programming model is
supported on distributed-memory machines.
13. Gelly
Gelly
Single system
Apache 2.0 License
https://flink.apache.org/news/2015/08/
24/introducing-flink-gelly.html
About
Gelly is Apache Flink’s graph-processing API and library. Flink’s native support for iterations makes it a
suitable platform for large-scale graph analytics. By leveraging delta iterations, Gelly is able to map
various graph processing models such as vertex-centric or gather-sum-apply to Flink dataflows. Gelly
allows Flink users to perform end-to-end data analysis in a single system. Gelly can be seamlessly used
with Flink’s DataSet API, which means that pre-processing, graph creation, analysis, and post-processing
can be done in the same application.
14. GPS
GPS
Distributed
BSD License
http://infolab.stanford.edu/gps/
About
GPS is an open-source system for scalable, fault-tolerant, and easy-to-program execution of algorithms
on extremely large graphs. GPS is similar to Google’s proprietary Pregel system, and Apache Giraph. GPS
is a distributed system designed to run on a cluster of machines, such as Amazon's EC2.
15. Gradoop
Gradoop
Distributed
Apache 2.0 License
https://dbs.uni-leipzig.de/en/research/pr
ojects/gradoop
About
Gradoop is a research framework for scalable graph analytics built on top of Apache Flink™. It offers a
graph data model which extends the widespread property graph model by the concept of logical graphs
and further provides operators that can be applied on single logical graphs and collections of logical
graphs. The combination of these operators allows the flexible, declarative definition of graph analytical
workflows. Gradoop can be easily integrated in a workflow which already uses Flink™ operators and
Flink™ libraries (i.e. Gelly, ML and Table).
16. GraphChi
GraphChi
Single System
Apache 2.0 License
https://github.com/GraphChi/graphchi-c
pp
About
GraphChi is a disk-based system for computing efficiently on graphs with billions of edges. By using a
well-known method to break large graphs into small parts, and a novel parallel sliding windows method,
GraphChi is able to execute several advanced data mining, graph mining, and machine learning
algorithms on very large graphs, using just a single consumer-level computer. GraphChi is a spin-off
project separate from the GraphLab PowerGraph project
17. GraphLab PowerGraph
GraphLab PowerGraph
Distributed
Apache 2.0 License
https://github.com/jegonzal/PowerGrap
h
About
GraphLab PowerGraph is a graph-based, high performance, distributed computation framework written
in C++. The GraphLab PowerGraph academic project was started in 2009 at Carnegie Mellon University
to develop a new parallel computation abstraction tailored to machine learning. GraphLab PowerGraph
is no longer in active development by the founding team. GraphLab PowerGraph is now supported by the
community. The learnings from GraphLab PowerGraph and GraphChi projects have culminated into
GraphLab Create.
18. GraphX
GraphX
Distributed
Apache 2.0 License
https://spark.apache.org/graphx/
About
GraphX is Apache Spark's API for graphs and graph-parallel computation. GraphX unifies ETL,
exploratory analysis, and iterative graph computation within a single system. You can view the same
data as both graphs and collections, transform and join graphs with RDDs efficiently, and write custom
iterative graph algorithms using the Pregel API.
19. Hadoop MapReduce
Hadoop MapReduce
Distributed
Apache 2.0 License
https://mapr.com/products/product-over
view/mapreduce/
About
MapReduce is Hadoop's native batch processing engine. Apache MapReduce is a powerful framework
for processing large, distributed sets of structured or unstructured data on a Hadoop cluster. The key
feature of MapReduce is its ability to perform processing across an entire cluster of nodes, with each
node processing its local data.
20. Microsoft Graph Engine
Microsoft Graph Engine
Distributed
MIT License
https://www.graphengine.io/
About
Microsoft Graph Engine is a distributed in-memory data processing engine, underpinned by a
strongly-typed in-memory key-value store and a general distributed computation engine.
21. Mizan
Mizan
Single System
Research project
https://thegraphsblog.wordpress.com/th
e-graph-blog/mizan/
About
Mizan is an advanced clone to Google’s graph processing system Pregel that utilizes online graph vertex
migrations to dynamically optimizes the execution of graph algorithms. You can use our Mizan system
to develop any vertex centric graph algorithm and run in parallel over a local cluster or over cloud
infrastructure. Mizan is compatible with Pregel’s API, written in C++ and uses MPICH2 for
communication.
22. PGX
PGX
Distributed
OTN License
https://www.oracle.com/technetwork/or
acle-labs/parallel-graph-analytix/overvie
w/index.html
About
PGX is a toolkit for graph analysis - both running algorithms such as PageRank against graphs, and
performing SQL-like pattern-matching against graphs, using the results of algorithmic analysis.
Algorithms are parallelized for extreme performance. The PGX toolkit includes both a single-node
in-memory engine, and a distributed engine for extremely large graphs. Graphs can be loaded from a
variety of sources including flat files, SQL and NoSQL databases and Apache Spark and Hadoop;
incremental updates are supported.
23. Pregel
Pregel
Single System
MIT License
http://web.cs.ucdavis.edu/~amenta/f15/
pregel.pdf
About
Pregel is a distributed programming framework, focused on providing users with a natural API for
programming graph algorithms while managing the details of distribution invisibly, including messaging
and fault tolerance. It is similar in concept to MapReduce, but with a natural graph API and much more
efficient support for iterative computations over the graph. The high-level organization of Pregel
programs is inspired by Valiant’s Bulk Synchronous Parallel model.
24. Ringo
Ringo
Single System
BSD License
http://snap.stanford.edu/ringo/
About
Ringo is a system for construction and analysis of large graphs on a single large memory multicore
machine, that combines high productivity analysis with fast and scalable execution times.
It offers an interactive easy-to-use Python interface, a rich set of over 200 advanced graph operations
and algorithms (based on the SNAP graph library), integration of table and graph processing, and
support for efficient graph construction and transformations between tables and graphs.
26. ThingSpan
ThingSpan
Distributed
Commercial
https://www.objectivity.com/products/th
ingspan/
About
ThingSpan is a purpose-built, massively scalable graph software platform, powered by Objectivity/DB,
that leverages the open source stack by natively integrating with Apache Spark and the Hadoop
Distributed File System (HDFS). It provides ultra-fast navigation and pathfinding queries against huge
distributed graphs. ThingSpan also supports parallel pattern-finding and predictive analytics in
combination with Spark components, such as MLlib, GraphX, and Spark SQL.
29. Combinatorial BLAS
Combinatorial BLAS
Linear Algebra
BSD License
https://people.eecs.berkeley.edu/~aydin
/CombBLAS/html/
About
The Combinatorial BLAS (CombBLAS) is an extensible distributed-memory parallel graph library offering
a small but powerful set of linear algebra primitives specifically targeting graph analytics.
30. Directed Graph Library (DGLib)
Directed Graph Library (DGLib)
C
GNU General Public License
https://grass.osgeo.org/dglib/
About
The Directed Graph Library provides functionality for vector network analysis. The original design idea
behind DGLib was to support middle sized graphs in RAM with a near-static structure that doesn't need
to be dynamically modified by the user program; ability to read graphs from input streams and process
them with no needle to rebuild internal trees. DGLib defines a serializable graph as being in FLAT state
and a editable graph as being in TREE state.
31. Dracula Graph Library
Dracula Graph Library
JavaScript
MIT License
https://www.graphdracula.net/
About
Dracula.js is a set of tools to display and layout interactive connected graphs and networks, along with
various related algorithms from the field of graph theory.
32. Graphinius JS
Graphinius JS
JavaScript
Apache 2.0
https://github.com/cassinius/Graphinius
JS
About
Graphinius JS is generic graph analysis library in Typescript. It is used in the GRAPHINIUS project an
Interactive Graph Research Framework with open access Web-based machine learning platform allowing
experts and end-users alike to visually compose state-of-the-art processing pipelines.
33. GraphJet
GraphJet
Java
Apache 2.0
https://github.com/twitter/GraphJet
About
GraphJet is a real-time graph processing library written in Java that maintains a full graph index over a
sliding time window in memory on a single server. This index supports a variety of graph algorithms
including personalized recommendation algorithms based on collaborative filtering. These algorithms
power a variety of real-time recommendation services within Twitter, notably content (tweets/URLs)
recommendations that require collaborative filtering over a heterogeneous, rapidly evolving graph.
34. Graphology
Graphology
JavaScript
MIT License
https://graphology.github.io/
About
Graphology is a specification and reference implementation for a robust & multipurpose JavaScript
Graph object. It aims at supporting various kinds of graphs with the same unified interface. Along with
those specifications, one will also find a standard library full of graph theory algorithms and common
utilities such as graph generators, layouts etc.
35. GraphStream
GraphStream
Java
CeCILL-C (French version) and LGPL v3
http://graphstream-project.org/
About
GraphStream is a Java library for the modeling and analysis of dynamic graphs. You can generate,
import, export, measure, layout and visualize them.
36. Grph
Grph
Java
Apache 2.0
http://www.i3s.unice.fr/~hogie/software
/index.php
About
Grph is a high-performance Java library for the manipulation of graphs. Its main design objectives are to
make it simple to use and extend, efficient, and, according to its initial motivation: useful in the context
of graph experimentation and network simulation. Grph also has the particularity to come with tools like
an evolutionary computation engine, a bridge to linear solvers, a framework for distributed computing,
etc.
37. iGraph
iGraph
C, R, Python, M, C++
GNU General Public License
https://igraph.org/
About
igraph is a collection of network analysis tools with the emphasis on efficiency, portability and ease of
use. igraph is open source and free. igraph can be programmed in R, Python, Mathematica and C/C++.
38. JGrphT
JGraphT
Java
LGPL 2.1 and EPL 2.0
https://jgrapht.org/
About
JGraphT is a Java library of graph theory data structures and algorithms designed for performance, with
near-native speed in many cases adapters for memory-optimized fastutil representation. JGraphT has
specialized iterators for graph traversal (DFS, BFS, etc) algorithms for path finding, clique detection,
isomorphism detection, coloring, common ancestors, tours, connectivity, matching, cycle detection,
partitions, cuts, flows, centrality, spanning, etc.
39. Java Universal Network Graph (Jung)
Jung
Java
BSD License
http://jung.sourceforge.net/
About
JUNG is a software library that provides a common and extendible language for the modeling, analysis,
and visualization of data that can be represented as a graph or network. The current distribution of
JUNG includes implementations of a number of algorithms from graph theory, data mining, and social
network analysis, such as routines for clustering, decomposition, optimization, random graph generation,
statistical analysis, and calculation of network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization framework.
40. NetworKit
NetworKit
Python, C++
MIT License
https://networkit.github.io/
About
NetworKit is a growing open-source toolkit for large-scale network analysis. Its aim is to provide tools for
the analysis of large networks in the size range from thousands to billions of edges. For this purpose, it
implements efficient graph algorithms, many of them parallel to utilize multicore architectures. These
are meant to compute standard measures of network analysis, such as degree sequences, clustering
coefficients, and centrality measures.
42. NVIDIA Graph Analytics library (nvGRAPH)
nvGRAPH
CUDA-C
https://developer.nvidia.com/nvgraph
About
The NVIDIA Graph Analytics library (nvGRAPH) comprises of parallel algorithms for high performance
analytics on graphs with up to 2 billion edges. nvGRAPH makes it possible to build interactive and high
throughput graph analytics applications.
43. RDFLib
RDFLib
Python
BSD License
https://4store.github.io/
About
RDFLib is a Python package working with RDF. RDFLib contains parsers and serializers for RDF/XML, N3,
NTriples, N-Quads, Turtle, TriX, RDFa and Microdata; a Graph interface which can be backed by any one
of a number of Store implementations; store implementations for in memory storage and persistent
storage on top of the Berkeley DB; a SPARQL 1.1 implementation - supporting SPARQL 1.1 Queries and
Update statements.
44. ScaleGraph
ScaleGraph
X10
Eclipse Public License v1.0
http://scalegraph.sourceforge.net/web/
About
ScaleGraph is a graph library based on the highly productive X10 programming language. The goal of
ScaleGraph is to provide large-scale graph analysis algorithms and efficient distributed computing
framework for graph analysts and for algorithm developers, respectively.
45. Stanford Network Analysis Project (SNAP)
SNAP
C++
BSD License
http://snap.stanford.edu/
About
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining
library. It scales to massive networks with hundreds of millions of nodes, and billions of edges. It
efficiently manipulates large graphs, calculates structural properties, generates regular and random
graphs, and supports attributes on nodes and edges. SNAP is also available through the NodeXL which
is a graphical front-end that integrates network analysis into Microsoft Office and Excel.
48. ArangoDB Query Language (AQL)
ArangoDB Query Language (AQL)
ArangoDB
https://docs.arangodb.com/3.3/AQL/
About
AQL is the SQL-like query language used in the ArangoDB database management system. It supports
CRUD operations for both documents (nodes) and edges, but it is not a data definition language (DDL).
AQL does support geospatial queries. It is JSON-oriented.
49. Cypher
Cypher
Neo4j
https://neo4j.com/developer/cypher/
About
Cypher is Neo4j’s graph query language that allows users to store and retrieve data from the graph
database. Cypher’s syntax provides a visual and logical way to match patterns of nodes and
relationships in the graph. It is a declarative, SQL-inspired language for describing visual patterns in
graphs using ASCII-Art syntax.
50. G-CORE
G-CORE
https://arxiv.org/pdf/1712.01550.pdf
About
G-CORE is a research language proposal from Linked Data Benchmark Council. It is a closed query
language where paths are first class citizens. The data model used in G-CORE is an extension of property
graphs with paths. It is an expressive query language.
51. Graph Query Language (GQL)
Graph Query Language (GQL)
Cypher ( Neo4j & the openCypher
community), PGQL (Oracle) and G-CORE
https://www.gqlstandards.org/
About
GQL is a proposed new international standard language for property graph querying. The idea of a
standalone graph query language to complement SQL was raised by ISO SC32/ WG3 members in early
2017, and is echoed in the GQL manifesto of May 2018.
52. GraphGrep
GraphGrep
About
GraphGrep is an application-independent method for querying graphs, finding all the occurrences of a
subgraph in a database of graphs. The interface to GraphGrep is a regular expression graph query
language Glide that combines features from XPath and Smart.
53. GraphQL
GraphQL
DGgraph
https://graphql.org/
About
GraphQL is a query language for APIs providing a complete and understandable description of the data
in APIs. It is questionable whether or not call GraphQL call a graph query language but GraphQL can be
used to query data modeled as a graph and various data system use it as such.
55. Gremlin
Gremlin
Amazon Neptune, Cosmos DB, DataStax
Enterprise Graph, Hadoop (Giraph),
Hadoop (Spark ), InfiniteGraph,
JanusGraph, Neo4j, Ontotext, OrientDB
https://tinkerpop.apache.org/gremlin.ht
ml
About
Gremlin is the graph traversal language of Apache TinkerPop. Gremlin is a functional, data-flow language
that enables users to succinctly express complex traversals on (or queries of) their application's
property graph.
56. N3QL
N3QL
RDF triplestores
https://www.w3.org/DesignIssues/N3QL
.html
About
N3QL is an implementation of an N3-based query language for RDF. It treats RDF as data and provides
query with triple patterns and constraints over a single RDF model. The target usage is for scripting and
for experimentation in information modelling languages. The language is derived from Notation3.and
RDQL.
57. OpenCypher
OpenCypher
SAP HANA Graph, Neo4j, Agens Graph,
RedisGraph, Memgraph, Apache Spark,
Apache TinkerPop, Gradoop, Ruruki,
Graphflow
https://www.opencypher.org/about
About
Neo4j started the openCypher Project in 2015 to create the industry-standard language for querying
graph databases. The project aims to deliver a full and open specification of the graph database query
language: Cypher.
58. Property Graph Query Language (PGQL)
PGQL
Oracle Big Data Spatial and Graph
http://pgql-lang.org/
About
PGQL is a graph pattern-matching query language for the property graph data model, inspired by SQL,
openCypher, G-CORE, GSQL, and SPARQL. PGQL combines graph pattern matching with familiar
constructs from SQL, such as SELECT, FROM and WHERE.
59. RDF Data Query Language (RDQL)
RDQL
https://www.w3.org/Submission/RDQL/
About
RDQL is a query language for RDF based on SquishQL. It queries RDF documents using a SQL-alike
syntax. An RDQL query consists of a graph pattern, expressed as a list of triple patterns. Each triple
pattern is comprised of named variables and RDF values (URIs and literals).
60. Sesame RDF Query Language (SeRQL)
SeRQL
RDF triplestores
http://archive.rdf4j.org/users/ch11.html
About
SeRQL ("Sesame RDF Query Language", pronounced "circle") is an RDF query language that is very
similar to SPARQL, but with other syntax. SeRQL was originally developed as a better alternative for the
query languages RQL and RDQL. A lot of SeRQL's features can now be found in SPARQL and SeRQL has
adopted some of SPARQL's features in return.
61. SociaLite
SociaLite
Hadoop
https://github.com/socialite-lang/socialit
e
About
SociaLite is a high-level query language for distributed graph analysis. In SociaLite, analysis programs
are implemented in high-level queries, that are compiled to parallel/distributed code. SociaLite is
Hadoop compatible, hence SociaLite queries can read data on HDFS (Hadoop Distributed File System).
62. SPARQL
SPARQL
RDF triplestores, Jena, OpenLink Virtuoso
https://www.w3.org/TR/sparql11-query/
About
SPARQL is a query language and a protocol for accessing RDF designed by the W3C RDF Data Access
Working Group. It is a declarative query language for performing data manipulation and data definition
operations on data represented as a collection of RDF Language sentences/statements.