Slides presenting our work on COSI at the ASONAM conference 2010
Note: The images used in this presentation are copyright by the respective owners as indicated with the picture. Pictures used are either CC or fair use. Please notify the author if you feel that your images are unfairly used in this presentation.
Students will create a "Frozen Moment" from their favorite book by combining synthesized sounds and found sounds to create a soundscape that accompanies a slowly moving multi-layered image. This will capture a moment of narrative tension from the book. Students will work in pairs to select a scene, rewrite it in first person, and design the visual and audio elements. They will learn skills in sampling, effects, and sequencing to create the soundscape and layered visuals in After Effects. Their projects will be exhibited for parents and assessed based on capturing tension, first person writing, and effective use of visual and audio elements.
Adobe After Effects Classroom tutorials on layers, keyframes, effects, importing
audio
How to write in first person Modelling and shared writing in class
How to plan and storyboard a film Storyboarding workshop
How to give a presentation Presentation skills workshop
Time management Weekly check ins, deadlines for sections
Collaboration Group roles assigned, peer feedback, joint problem solving
Critical thinking Questioning, justifying choices, considering alternatives
Self-management Goal
1. The document describes Titan, a scalable graph database that can integrate with Hadoop for large-scale graph analytics.
2. It provides examples of modeling data as a graph with nodes and edges to represent entities and their relationships.
3. Faunus is a batch graph computing framework that runs graph algorithms on large graphs stored in HDFS for applications like recommendations and pattern mining.
Titan is a scalable graph database that can distribute and query graph data across multiple machines. This presentation provides a general introduction to graph computing and Titan in particular. It also focuses on some recent development for Titan 0.9 and TinkerPop 3.
Problem solving in the 21st century increasingly depends on the analysis of complex systems. Developing new drugs, understanding risk in financial networks, searching for answers in knowledge graphs, personalization and recommendation in social networks all require the analysis of systems composed of interconnected entities that exhibit complex behavior as a whole. Graph computing provides a conceptual model and practical platform for developing such analyses.
This talk presents graph computing as an important component of every developer’s toolbox. We introduce the Aurelius graph cluster which is an open-source stack enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop. This stack addresses challenging problems in graph partitioning, graph query language design and graph algorithm development with solutions inspired by physics, biology and neuroscience.
This presentation introduces Titan, Faunus, and scalable graph computing in general. We present a case study of how Pearson builds an education social network on top of Titan, Faunus, and Cassandra to support learning in the 21st century.
Titan is an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. Faunus is an open source global graph processing engine build on top of Hadoop and compatible with Cassandra that can analyze graphs, compute graph statistics, and execute global traversals. Titan and Faunus are components of the Aurelius Graph Cluster which enables scalable graph computation and powers applications in social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
This document discusses graph databases and analyzing relationships at scale. It provides an overview of graph databases, how they are used to represent complex relationships between entities as nodes and edges in a graph, and how graph queries and analytics can reveal useful insights by traversing the graph along relationships. It also briefly introduces Aurelius, an open source graph database platform, and some of its features for working with large graph datasets.
Students will create a "Frozen Moment" from their favorite book by combining synthesized sounds and found sounds to create a soundscape that accompanies a slowly moving multi-layered image. This will capture a moment of narrative tension from the book. Students will work in pairs to select a scene, rewrite it in first person, and design the visual and audio elements. They will learn skills in sampling, effects, and sequencing to create the soundscape and layered visuals in After Effects. Their projects will be exhibited for parents and assessed based on capturing tension, first person writing, and effective use of visual and audio elements.
Adobe After Effects Classroom tutorials on layers, keyframes, effects, importing
audio
How to write in first person Modelling and shared writing in class
How to plan and storyboard a film Storyboarding workshop
How to give a presentation Presentation skills workshop
Time management Weekly check ins, deadlines for sections
Collaboration Group roles assigned, peer feedback, joint problem solving
Critical thinking Questioning, justifying choices, considering alternatives
Self-management Goal
1. The document describes Titan, a scalable graph database that can integrate with Hadoop for large-scale graph analytics.
2. It provides examples of modeling data as a graph with nodes and edges to represent entities and their relationships.
3. Faunus is a batch graph computing framework that runs graph algorithms on large graphs stored in HDFS for applications like recommendations and pattern mining.
Titan is a scalable graph database that can distribute and query graph data across multiple machines. This presentation provides a general introduction to graph computing and Titan in particular. It also focuses on some recent development for Titan 0.9 and TinkerPop 3.
Problem solving in the 21st century increasingly depends on the analysis of complex systems. Developing new drugs, understanding risk in financial networks, searching for answers in knowledge graphs, personalization and recommendation in social networks all require the analysis of systems composed of interconnected entities that exhibit complex behavior as a whole. Graph computing provides a conceptual model and practical platform for developing such analyses.
This talk presents graph computing as an important component of every developer’s toolbox. We introduce the Aurelius graph cluster which is an open-source stack enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop. This stack addresses challenging problems in graph partitioning, graph query language design and graph algorithm development with solutions inspired by physics, biology and neuroscience.
This presentation introduces Titan, Faunus, and scalable graph computing in general. We present a case study of how Pearson builds an education social network on top of Titan, Faunus, and Cassandra to support learning in the 21st century.
Titan is an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. Faunus is an open source global graph processing engine build on top of Hadoop and compatible with Cassandra that can analyze graphs, compute graph statistics, and execute global traversals. Titan and Faunus are components of the Aurelius Graph Cluster which enables scalable graph computation and powers applications in social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
This document discusses graph databases and analyzing relationships at scale. It provides an overview of graph databases, how they are used to represent complex relationships between entities as nodes and edges in a graph, and how graph queries and analytics can reveal useful insights by traversing the graph along relationships. It also briefly introduces Aurelius, an open source graph database platform, and some of its features for working with large graph datasets.
Adding Value through graph analysis using Titan and FaunusMatthias Broecheler
In this presentation we discuss how graph analysis can add value to your data and how to use open source tools like Titan and Faunus to build scalable graph processing systems.
This presentation gives an update on the development status of Titan and Faunus with a preview of what is to come.
The problems we are faced with in the 21st century require efficient analysis of ever more complex systems. This presentation outlines how such problems can be better understood and effectively solved if they are modeled as graphs or networks. We present two tools for to help solve such problems at scale: Titan, which is a real-time distributed graph database based on Apache Cassandra and Hbase and Faunus, which is a batch analytics framework for graphs based on Apache Hadoop. We discuss their current development status as of November 2012 and illustrate an example application for the GitHub coding network.
Titan is an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. Graphs are a versatile data model for capturing and analyzing rich relational structures. Graphs are an increasingly popular way to represent data in a wide range of domains such as social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
This presentation discusses Titan's data model, query language, and novel techniques in edge compression, data layout, and vertex-centric indices which facilitate the representation and processing of Big Graph Data across a Cassandra cluster. We demonstrate Titan's performance on a large scale benchmark evaluation using Twitter data.
Presented at the Cassandra 2012 Summit.
PMatch: Probabilistic Subgraph Matching on Huge Social NetworksMatthias Broecheler
Users querying massive social networks or RDF databases are often not 100% certain about what they are looking for due to the complexity of the query or heterogeneity of the data. In this paper, we propose “probabilistic subgraph” (PS) queries over a graph/network database, which afford users great flexibility in specifying “approximately” what they are looking for. We formally define the probability that a substitution satisfies a PS-query with respect to a graph database. We then present the PMATCH algorithm to answer such queries and prove its correctness. Our experimental evaluation demonstrates that PMATCH is efficient and scales to massive social networks with over a billion edges.
Budget-Match: Cost Effective Subgraph Matching on Large NetworksMatthias Broecheler
BudgetMatch is a subgraph matching algorithm for large networks that uses a dynamic cost model. It assigns an initial cost estimate and processes nodes using the current cost as a budget. If the budget is exceeded, processing is aborted and the cost estimate updated. Experiments on a network with 1.12 billion edges showed BudgetMatch outperformed the DOGMA algorithm on a set of benchmark queries, with query times improving as the cost update parameter λ increased. Initializing costs using average degree statistics also improved performance.
1. The document describes Probabilistic Soft Logic (PSL), a probabilistic modeling language based on logics.
2. PSL uses rules to capture dependencies and constraints between continuous random variables represented as atoms. A PSL program consists of rules, sets, constraints, and atoms.
3. PSL provides a mathematical foundation based on constrained continuous Markov random fields and a logical foundation based on generalized annotated logic programs. It allows for collective probabilistic inference and learning over relational domains.
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in general and present a polynomial-time approximation scheme under mild assumptions on the structure of the random field. Moreover, we introduce a sampling algorithm to compute marginal distributions and develop novel techniques to increase its efficency. Continuous MRFs are a general purpose probabilistic modeling tool and we demonstrate how they can be applied to statistical relational learning. On the problem of collective classification, we evaluate our algorithm and show that the standard deviation of marginals serves as a useful measure of confidence.
A Scalable Framework for Modeling Competitive Diffusion in Social NetworksMatthias Broecheler
This document presents a framework for modeling competitive diffusion in social networks. It discusses how diffusion, such as the spread of opinions, products, and diseases, is widely studied but typically modeled separately for each problem class using different models. The framework aims to develop an expressive and general language based on logical rules to represent diffusion models. This would allow diffusion processes to be modeled in a consistent way. The document outlines how the framework represents social network data and diffusion rules, handles competition between diffusions, defines a probabilistic model, and discusses algorithms for scaling the approach to large social networks.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Adding Value through graph analysis using Titan and FaunusMatthias Broecheler
In this presentation we discuss how graph analysis can add value to your data and how to use open source tools like Titan and Faunus to build scalable graph processing systems.
This presentation gives an update on the development status of Titan and Faunus with a preview of what is to come.
The problems we are faced with in the 21st century require efficient analysis of ever more complex systems. This presentation outlines how such problems can be better understood and effectively solved if they are modeled as graphs or networks. We present two tools for to help solve such problems at scale: Titan, which is a real-time distributed graph database based on Apache Cassandra and Hbase and Faunus, which is a batch analytics framework for graphs based on Apache Hadoop. We discuss their current development status as of November 2012 and illustrate an example application for the GitHub coding network.
Titan is an open source distributed graph database build on top of Cassandra that can power real-time applications with thousands of concurrent users over graphs with billions of edges. Graphs are a versatile data model for capturing and analyzing rich relational structures. Graphs are an increasingly popular way to represent data in a wide range of domains such as social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
This presentation discusses Titan's data model, query language, and novel techniques in edge compression, data layout, and vertex-centric indices which facilitate the representation and processing of Big Graph Data across a Cassandra cluster. We demonstrate Titan's performance on a large scale benchmark evaluation using Twitter data.
Presented at the Cassandra 2012 Summit.
PMatch: Probabilistic Subgraph Matching on Huge Social NetworksMatthias Broecheler
Users querying massive social networks or RDF databases are often not 100% certain about what they are looking for due to the complexity of the query or heterogeneity of the data. In this paper, we propose “probabilistic subgraph” (PS) queries over a graph/network database, which afford users great flexibility in specifying “approximately” what they are looking for. We formally define the probability that a substitution satisfies a PS-query with respect to a graph database. We then present the PMATCH algorithm to answer such queries and prove its correctness. Our experimental evaluation demonstrates that PMATCH is efficient and scales to massive social networks with over a billion edges.
Budget-Match: Cost Effective Subgraph Matching on Large NetworksMatthias Broecheler
BudgetMatch is a subgraph matching algorithm for large networks that uses a dynamic cost model. It assigns an initial cost estimate and processes nodes using the current cost as a budget. If the budget is exceeded, processing is aborted and the cost estimate updated. Experiments on a network with 1.12 billion edges showed BudgetMatch outperformed the DOGMA algorithm on a set of benchmark queries, with query times improving as the cost update parameter λ increased. Initializing costs using average degree statistics also improved performance.
1. The document describes Probabilistic Soft Logic (PSL), a probabilistic modeling language based on logics.
2. PSL uses rules to capture dependencies and constraints between continuous random variables represented as atoms. A PSL program consists of rules, sets, constraints, and atoms.
3. PSL provides a mathematical foundation based on constrained continuous Markov random fields and a logical foundation based on generalized annotated logic programs. It allows for collective probabilistic inference and learning over relational domains.
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in general and present a polynomial-time approximation scheme under mild assumptions on the structure of the random field. Moreover, we introduce a sampling algorithm to compute marginal distributions and develop novel techniques to increase its efficency. Continuous MRFs are a general purpose probabilistic modeling tool and we demonstrate how they can be applied to statistical relational learning. On the problem of collective classification, we evaluate our algorithm and show that the standard deviation of marginals serves as a useful measure of confidence.
A Scalable Framework for Modeling Competitive Diffusion in Social NetworksMatthias Broecheler
This document presents a framework for modeling competitive diffusion in social networks. It discusses how diffusion, such as the spread of opinions, products, and diseases, is widely studied but typically modeled separately for each problem class using different models. The framework aims to develop an expressive and general language based on logical rules to represent diffusion models. This would allow diffusion processes to be modeled in a consistent way. The document outlines how the framework represents social network data and diffusion rules, handles competition between diffusions, defines a probabilistic model, and discusses algorithms for scaling the approach to large social networks.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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
7. collaborate
USA Prof Prof
COSI
dean author
member Italy
in
Jones Paper Baneri
“ABC” comment UC
UMD author
CS CS
in
faculty Prof
friends
Calero faculty
department in
member
faculty Prof presented
Dooley attended Social
University
MD Science department Universita
department in ASONAM Calabria
10 dean
attended Prof
faculty UMD
author
submitted Roma
member
Physics author
organized visited
Prof author accepted KPLLC Paper friends
09 Paper “UVW”
Smith Paper “HIJ”
submitted
“XYZ”
comment
comment attended
student of author Prof
Prof
collaborates Olsen student of
Prof Lund member
dean
Jamie Larsen
faculty Karl
Lock member
Social Oede
visited Science
Odense SDU
John
colleagues Doe Physics department
Odense Denmark
8. COSI
Example Query
?p
author comment
?v1 ?v3
faculty friends
faculty
University in
MD Italy ?v2
Simple query, yet already
difficult to answer by hand
8
13. COSI
COSI Graph Partitioning
How should we partition the graph?
GOAL: Find a way to partition the
graph DB into “blocks” across the k
storage nodes so that expected
time to answer queries is small.
13
14. COSI
Example Query & Naive Approach
Jones
Dooley ?p
author
Smith comment
?v1 ?v3
faculty friends
faculty
University in
MD Italy ?v2
14
15. COSI
Co-Retrieval
Paper “ABC”
?p
author comment
Jones ?v3
faculty friends
faculty
University in
MD Italy ?v2
Co-retrieval:
Jones – Paper “ABC“
15
16. COSI
Cost Model
Query trace: A query trace w.r.t. a query plan x
for query Q consists of
- All vertices in the DB whose neighborhood is
retrieved during execution of x
- All pairs (u,v) of vertices where x retrieves
v’s nbhd immediately after retrieving u’s
nbhd.
• Intuition: Try to put u,v on same storage node.
• Assumption: Retrieved nbhds are cached in
memory.
16
17. COSI
Cost Model (continued)
Assume fixed but arbitrary distribution
over the set of all queries.
This induces a pdf over the set of all
feasible query plans qp(Q) for query Q.
- (x)= Q œ , qp(Q)=x (Q).
- Prob of query plan “x” is the sum of the probs of
queries requiring query plan x.
Let E(v) be the event that v is retrieved by
a query trace of a random query plan for
Q.
17
18. COSI
Cost Model (continued)
Prob that vertex v occurs in the trace of a
randomly chosen query plan is
(E(v)) = x œ qp(Q) ⁄ v œ qt(x,DB) (x).
Prob that (u,v) occurs in the trace of a randomly
chosen query plan is
(E(u,v)) = x œ qp(Q) ⁄ (u,v ) œ qt(x,DB) (x).
18
19. COSI
Cost Model (continued)
Key Theorem
Suppose vertex retrieval and inter-node comms
are uniform across storage nodes. The partition
of the DB graph that minimizes query exec time
coincides with the partition that minimizes edge
cut cost in the graph (V,VV) with weight
function w(u,v)= (E(u,v))+ (E(v,u)).
SO MIN EDGE-CUTS IN COMPLETE GRAPHS IS
CLOSELY RELATED TO MINIMIZING QUERY
EXECUTION TIME.
19
20. COSI
Partitioning Algorithm
Challenges
- Finding MIN EDGE-CUT is NP-complete.
- We want to process graphs containing 100s of
millions of edges.
So we want an algorithm that is
- Very fast
- Produces good edge cuts
• but maybe not optimal
To achieve speed, we focus on partition strategies that
permanently assign vertices to blocks.
20
21. COSI
Individual edge insertion
Suppose we have a partition P={P1,..,Pk}.
We are inserting the edge (v,p,o).
Vertex force vectors: Measures how strongly
each Pi “pulls” a vertex.
- |v|[i] = fP( y œ (nbhd(v) … Pi) w(v,y))
- fP maps positive reals to reals and is an “affinity”
measure.
- |v|[i] sums up the weights of edges from v to each
neighbor in Pi. Insert v into block Pi with highest |v
|[i].
21
22. COSI
Affinity Measures
Must satisfy 3 properties
- Connectedness of a vertex to a partition
block. This helps minimize edge cut.
- Imbalance of block sizes.
• E.g. standard deviation of block sizes,
normalized by expected DB size.
- Excessive size should be punished.
22
23. COSI
Batch insertion
Adding a set of edges at once.
Idea: Find strongly connected
components using modularity
maximization and assign those to the
partition block with highest affinity.
23
25. COSI
Graph modularity
Mod(P) = Pi œ P(W(Pi,Pi)/2|E| -
degW(Pi) 2/(2|E|)2)
Where
- W(X,Y) is the sum of the weights of
edges (x,y) with x in X, y in Y.
- degW(v) is the sum of the weights of
edges (v,-) and
- degW(Pi) is the sum of the degW(v)’s for
v in Pi.
25
27. COSI
Query Answering
Graph Data Client B ?X
?Z C
A ?Y
load Receive query -
Return results
Dispatch query
Query answer
Forward (partially
Answered) query
28. COSI
Example Query
?p
author comment
?v1 ?v3
faculty friends
faculty
University in
MD Italy ?v2
P1
28
29. COSI
Example Query
Jones : P2
Dooley : P2 ?p
author
Smith : P3 comment
?v1 ?v3
faculty friends
faculty
University in
MD Italy ?v2
29
30. COSI
Example Query
Paper “ABC” : P2
Paper “HIJ” : P3
?p
author comment
P2 Calero : P2
Dooley ?v3
faculty friends
faculty
University in
MD Italy ?v2
Where to send query next?
30
31. COSI
Query answering
Basic: Next substitution arbitrary
COSI_Heur is a heuristic version that makes
intelligent choices about the next variable
to be substituted.
- Branching Factor # possible substitutions
- Communication cost # messages to be sent
- Workload distribution partitions hosting
vertices
31
33. COSI
COSI implementation
Implementation is in Java (approx
10,000 loc)
778M edges social network DB
- Flickr, Orkut, Livejournal, Youtube
- [Mislove ‘07]
16-node compute cluster
- 8 GB of RAM
- 30 GB HDs
- 8 core Intel CPU
33
34. COSI
Partitioning quality
Comparison of Partitioning Methods
40.0%
35.0%
30.0%
25.0% Edge Cut
20.0%
Improvement
15.0%
Imbalance
10.0%
5.0%
0.0%
Single Greedy Batch Greedy Batch Partition
COSI_Partition achieves a 36% improvement in
edge-cut with only slightly higher imbalance.
Took 7.5 h to load with individual triple insertion, 10.5 h with batch.
34
35. COSI
Logarithmic
Query answering time scale
10000000
Query Times by Cost Model (in ms)
1000000
100000
ms
10000
1000
100
6 Edges / 7 Edges / 8 Edges / 9 Edges / 10 Edges / 11 Edges / 11 Edges / 14 Edges / 16 Edges / 17 Edges / 23 Edges /
3 Vars 4 Vars 3 Vars 3 Vars 3 Vars 4 Vars 5 Vars 5 Vars 7 Vars 5 Vars 6 Vars
Cost Model A
Cost Model 2.0/0.5 Cost Model B
Cost Model 1.2/0.1 Cost Model C
Cost Model 8.0/5.0 No Cost Model
No Cost Model
COSI_heur does very well, answering
pretty complex queries in under a second.
X-axis shows number of edges and variable vertices.
35
36. COSI
Logarithmic
Partitioning Effect scale
100000
10000
Time (ms)
1000
100
6E/3V 7E/4V 8E/3V 9E/3V 10E/3V 11E/4V 11E/5V 14E/5V 16E/7V 17E/5V 23E/6V
Size of the query (# edges / # vertices)
COSI Batch Partition Individual Edge Insertion
COSI_heur does very well, answering
pretty complex queries in under a second.
36
38. COSI
Related Work
Systems Pros Cons
Single Neo4j, DEO, Latency, Speed Limited size
Machine Hypergraph, Limited Throughput
RDF-3X, OWLIM,
AllegroGraph, etc
Orchestrated YARS 2, system Size Scalability Latency
Distribution extensions Limited Throughput
Asynchronous COSI Size Scalability Latency
Cloud Throughput
oriented Scalability
Resource Elasticity
38
39. COSI
Conclusion
COSI is a general, scalable and fast
graph database framework for social
network analysis
Demonstrated scalability and speed on
the problem of subgraph identification
39