The document announces a Cascading Meetup on March 5th, 2013 in Cupertino, CA to discuss enterprise data workflows, ANSI SQL support, and test-driven development. It provides examples of how large organizations use data workflows between front-end applications, back-office systems, logs, and Hadoop clusters to drive analytics and reporting. Main Street firms are also migrating workflows to Hadoop for cost savings and scalability.
Chris Wendt, Group Program Manager-MT, Microsoft Research
Chris will outline factors influencing the translation quality of a statistical machine translation system, providing a short description of the feedback collection mechanism in use at Microsoft, and the metrics on observed on its MT deployments. Chris will provide examples from the Microsoft Developer Network blogs and documentation, and the customer support information to present the Microsoft view that more data equals better machine translation.
Chris Wendt, Group Program Manager-MT, Microsoft Research
Chris will outline factors influencing the translation quality of a statistical machine translation system, providing a short description of the feedback collection mechanism in use at Microsoft, and the metrics on observed on its MT deployments. Chris will provide examples from the Microsoft Developer Network blogs and documentation, and the customer support information to present the Microsoft view that more data equals better machine translation.
My talk at the Long Now Foundation seminars on Long Term Thinking on September 5, 2012. Overlaps with a number of other talks, but contains material not found anywhere else. Audio and video are available at http://longnow.org/seminars/02012/sep/05/birth-global-mind/
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
What Android Can Learn from Steve JobsTim O'Reilly
A meditation on Jobs' quote that design is an expression of the "fundamental soul" of a human creation. What is the fundamental soul of Google, and how should it be reflected in Android?
A presentation by SMART Infrastructure Facility's Geomatics Research Fellow, Dr Tomas Holderness, and Vice Chancellor's Post Doctoral Research Fellow, Dr Etienne Turpin, to the International Symposium For Next Generation Infrastructure (ISNGI), Vienna September 2014.
معماری مبتنی بر سرویس، اصول و اجزا
این مبحث یکی از فناوری هایی است که در درس مهندسی فناوری اطلاعات 2 برای دانشجویان مهندسی فناوری اطلاعات ارائه می دهم.
Comment le picture marketing permet de développer ses ventes en ligne et en b...Emilie Marquois
Avec l’explosion des usages et applications mobiles, découvrez les notions et outils clés pour faire la différence grâce à la communication par l’image.
The Clothesline Paradox and the Sharing Economy (Keynote file)Tim O'Reilly
My keynote at OSCON 2012 in Portland, July 18, 2012. Focuses on the contribution of open source software to the economy, using the metaphor of "the clothesline paradox" first articulated by Steve Baer in CoEvolution Quarterly in 1975
My talk at the Stanford Technology Ventures Program on March 6, 2013. I talk about some technical and business lessons from Square, Uber, AirBnB, and the Google Autonomous Vehicle that are applicable to today's startups.
OSCON 2012 US Patriot Act Implications for Cloud Computing - Diane Mueller, A...OSCON Byrum
Presented by Diane Mueller, ActiveState @pythondj
Are you unsure what the security and privacy implications are for sensitive corporate data? US Patriot Act is causing many of us to hesitate on leveraging the cloud.
Organizations are thinking long and hard about the legal and regulatory implications of cloud computing. When it comes to actual corporate data, no matter what the efficiency gains are, legal departments are often directing IT departments to steer clear of any service that eliminates their ability to keep potential sensitive information out of the hands of Federal prosecutors.
Despite all the hype about every application moving into the cloud, some practical patterns are starting to emerge in the types of data corporations are willing to move to the cloud.
Covered in this session:
(a) Introduction to the US Patriot Act and Data Privacy issues Implications for on Cloud Computing Jurisdictional Issues
(b) Best Practices & Practical Patterns Classes of applications that best leverage the cloud
(c)What types of applications should stay on-premise Private Cloud Model(s) Building a Compliant Cloud Strategy
For more information:
email me at dianem {at} activestate {period} com
or ping me on twitter at @pythondj
visit http://activestate.com/stackato
Columbia Law School - Decentralized Ledgers Presentation on 4/7/2014Ldger, Inc
Principals from Tillit explore the business and legal implications of advanced blockchain technologies, including smart contracts, digital stored value and the concepts of "code as law"
A talk given in Austin, Texas on 2013-05-28 about how cognitive bias interferes with leveraging distributed systems for large-scale apps. Also, about design patterns for Enterprise data workflows. http://hadoop-and-beyond-austin.eventbrite.com/
Mesosphere lightening talk presented at the first Mesos Townhall Meeting 2013-11-19 https://www.eventbrite.com/e/mesostownhall-meeting-1119-tickets-9104464699
Enterprise Data Workflows with CascadingPaco Nathan
Cascading meetup held jointly with Enterprise Big Data meetup at Tata Consultancy Services in Santa Clara on 2012-12-17
http://www.meetup.com/cascading/events/94079162/
My talk at the Long Now Foundation seminars on Long Term Thinking on September 5, 2012. Overlaps with a number of other talks, but contains material not found anywhere else. Audio and video are available at http://longnow.org/seminars/02012/sep/05/birth-global-mind/
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
What Android Can Learn from Steve JobsTim O'Reilly
A meditation on Jobs' quote that design is an expression of the "fundamental soul" of a human creation. What is the fundamental soul of Google, and how should it be reflected in Android?
A presentation by SMART Infrastructure Facility's Geomatics Research Fellow, Dr Tomas Holderness, and Vice Chancellor's Post Doctoral Research Fellow, Dr Etienne Turpin, to the International Symposium For Next Generation Infrastructure (ISNGI), Vienna September 2014.
معماری مبتنی بر سرویس، اصول و اجزا
این مبحث یکی از فناوری هایی است که در درس مهندسی فناوری اطلاعات 2 برای دانشجویان مهندسی فناوری اطلاعات ارائه می دهم.
Comment le picture marketing permet de développer ses ventes en ligne et en b...Emilie Marquois
Avec l’explosion des usages et applications mobiles, découvrez les notions et outils clés pour faire la différence grâce à la communication par l’image.
The Clothesline Paradox and the Sharing Economy (Keynote file)Tim O'Reilly
My keynote at OSCON 2012 in Portland, July 18, 2012. Focuses on the contribution of open source software to the economy, using the metaphor of "the clothesline paradox" first articulated by Steve Baer in CoEvolution Quarterly in 1975
My talk at the Stanford Technology Ventures Program on March 6, 2013. I talk about some technical and business lessons from Square, Uber, AirBnB, and the Google Autonomous Vehicle that are applicable to today's startups.
OSCON 2012 US Patriot Act Implications for Cloud Computing - Diane Mueller, A...OSCON Byrum
Presented by Diane Mueller, ActiveState @pythondj
Are you unsure what the security and privacy implications are for sensitive corporate data? US Patriot Act is causing many of us to hesitate on leveraging the cloud.
Organizations are thinking long and hard about the legal and regulatory implications of cloud computing. When it comes to actual corporate data, no matter what the efficiency gains are, legal departments are often directing IT departments to steer clear of any service that eliminates their ability to keep potential sensitive information out of the hands of Federal prosecutors.
Despite all the hype about every application moving into the cloud, some practical patterns are starting to emerge in the types of data corporations are willing to move to the cloud.
Covered in this session:
(a) Introduction to the US Patriot Act and Data Privacy issues Implications for on Cloud Computing Jurisdictional Issues
(b) Best Practices & Practical Patterns Classes of applications that best leverage the cloud
(c)What types of applications should stay on-premise Private Cloud Model(s) Building a Compliant Cloud Strategy
For more information:
email me at dianem {at} activestate {period} com
or ping me on twitter at @pythondj
visit http://activestate.com/stackato
Columbia Law School - Decentralized Ledgers Presentation on 4/7/2014Ldger, Inc
Principals from Tillit explore the business and legal implications of advanced blockchain technologies, including smart contracts, digital stored value and the concepts of "code as law"
A talk given in Austin, Texas on 2013-05-28 about how cognitive bias interferes with leveraging distributed systems for large-scale apps. Also, about design patterns for Enterprise data workflows. http://hadoop-and-beyond-austin.eventbrite.com/
Mesosphere lightening talk presented at the first Mesos Townhall Meeting 2013-11-19 https://www.eventbrite.com/e/mesostownhall-meeting-1119-tickets-9104464699
Enterprise Data Workflows with CascadingPaco Nathan
Cascading meetup held jointly with Enterprise Big Data meetup at Tata Consultancy Services in Santa Clara on 2012-12-17
http://www.meetup.com/cascading/events/94079162/
A Data Scientist And A Log File Walk Into A Bar...Paco Nathan
Presented at Splunk .conf 2012 in Las Vegas. Includes an overview of the Cascading app based on City of Palo Alto open data. PS: email me if you need a different format than Keynote: @pacoid or pnathan AT concurrentinc DOT com
Intro to Data Science for Enterprise Big DataPaco Nathan
If you need a different format (PDF, PPT) instead of Keynote, please email me: pnathan AT concurrentinc DOT com
An overview of Data Science for Enterprise Big Data. In other words, how to combine structured and unstructured data, leveraging the tools of automation and mathematics, for highly scalable businesses. We discuss management strategy for building Data Science teams, basic requirements of the "science" in Data Science, and typical data access patterns for working with Big Data. We review some great algorithms, tools, and truisms for building a Data Science practice, and provide plus some great references to read for further study.
Presented initially at the Enterprise Big Data meetup at Tata Consultancy Services, Santa Clara, 2012-08-20 http://www.meetup.com/Enterprise-Big-Data/events/77635202/
To View this webinar replay:
http://ecast.opensystemsmedia.com/316
As Operational Technologies (OT) like embedded devices, control and monitoring systems are increasingly integrated with Information Technology (IT) systems running in the back office, interaction patterns between systems are becoming more complex and diverse. Publish-Subscribe is the most commonly used messaging pattern for OT systems. It provides the real-time information access, scalability, and loose coupling required for integration of these types of systems. IT and OT integration, however, commonly requires messaging patterns that provide stronger end-to-end properties, such as Guaranteed Delivery, Request-Reply, and (load-balancing) Queues. RTI is greatly enhancing its infrastructure software with new messaging patterns that combine the performance, scalability, and reliability needed by OT systems with the integration and flexible messaging capabilities of IT systems.
Human in the loop: a design pattern for managing teams working with MLPaco Nathan
Strata CA 2018-03-08
https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/64223
Although it has long been used for has been used for use cases like simulation, training, and UX mockups, human-in-the-loop (HITL) has emerged as a key design pattern for managing teams where people and machines collaborate. One approach, active learning (a special case of semi-supervised learning), employs mostly automated processes based on machine learning models, but exceptions are referred to human experts, whose decisions help improve new iterations of the models.
Human-in-the-loop: a design pattern for managing teams that leverage MLPaco Nathan
Strata Singapore 2017 session talk 2017-12-06
https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/65611
Human-in-the-loop is an approach which has been used for simulation, training, UX mockups, etc. A more recent design pattern is emerging for human-in-the-loop (HITL) as a way to manage teams working with machine learning (ML). A variant of semi-supervised learning called active learning allows for mostly automated processes based on ML, where exceptions get referred to human experts. Those human judgements in turn help improve new iterations of the ML models.
This talk reviews key case studies about active learning, plus other approaches for human-in-the-loop which are emerging among AI applications. We’ll consider some of the technical aspects — including available open source projects — as well as management perspectives for how to apply HITL:
* When is HITL indicated vs. when isn’t it applicable?
* How do HITL approaches compare/contrast with more “typical” use of Big Data?
* What’s the relationship between use of HITL and preparing an organization to leverage Deep Learning?
* Experiences training and managing a team which uses HITL at scale
* Caveats to know ahead of time:
* In what ways do the humans involved learn from the machines?
* In particular, we’ll examine use cases at O’Reilly Media where ML pipelines for categorizing content are trained by subject matter experts providing examples, based on HITL and leveraging open source [Project Jupyter](https://jupyter.org/ for implementation).
Human-in-a-loop: a design pattern for managing teams which leverage MLPaco Nathan
Human-in-a-loop: a design pattern for managing teams which leverage ML
Big Data Spain, 2017-11-16
https://www.bigdataspain.org/2017/talk/human-in-the-loop-a-design-pattern-for-managing-teams-which-leverage-ml
Human-in-the-loop is an approach which has been used for simulation, training, UX mockups, etc. A more recent design pattern is emerging for human-in-the-loop (HITL) as a way to manage teams working with machine learning (ML). A variant of semi-supervised learning called _active learning_ allows for mostly automated processes based on ML, where exceptions get referred to human experts. Those human judgements in turn help improve new iterations of the ML models.
This talk reviews key case studies about active learning, plus other approaches for human-in-the-loop which are emerging among AI applications. We'll consider some of the technical aspects -- including available open source projects -- as well as management perspectives for how to apply HITL:
* When is HITL indicated vs. when isn't it applicable?
* How do HITL approaches compare/contrast with more "typical" use of Big Data?
* What's the relationship between use of HITL and preparing an organization to leverage Deep Learning?
* Experiences training and managing a team which uses HITL at scale
* Caveats to know ahead of time
* In what ways do the humans involved learn from the machines?
In particular, we'll examine use cases at O'Reilly Media where ML pipelines for categorizing content are trained by subject matter experts providing examples, based on HITL and leveraging open source [Project Jupyter](https://jupyter.org/ for implementation).
Humans in a loop: Jupyter notebooks as a front-end for AIPaco Nathan
JupyterCon NY 2017-08-24
https://www.safaribooksonline.com/library/view/jupytercon-2017-/9781491985311/video313210.html
Paco Nathan reviews use cases where Jupyter provides a front-end to AI as the means for keeping "humans in the loop". This talk introduces *active learning* and the "human-in-the-loop" design pattern for managing how people and machines collaborate in AI workflows, including several case studies.
The talk also explores how O'Reilly Media leverages AI in Media, and in particular some of our use cases for active learning such as disambiguation in content discovery. We're using Jupyter as a way to manage active learning ML pipelines, where the machines generally run automated until they hit an edge case and refer the judgement back to human experts. In turn, the experts training the ML pipelines purely through examples, not feature engineering, model parameters, etc.
Jupyter notebooks serve as one part configuration file, one part data sample, one part structured log, one part data visualization tool. O'Reilly has released an open source project on GitHub called `nbtransom` which builds atop `nbformat` and `pandas` for our active learning use cases.
This work anticipates upcoming work on collaborative documents in JupyterLab, based on Google Drive. In other words, where the machines and people are collaborators on shared documents.
Humans in the loop: AI in open source and industryPaco Nathan
Nike Tech Talk, Portland, 2017-08-10
https://niketechtalks-aug2017.splashthat.com/
O'Reilly Media gets to see the forefront of trends in artificial intelligence: what the leading teams are working on, which use cases are getting the most traction, previews of advances before they get announced on stage. Through conferences, publishing, and training programs, we've been assembling resources for anyone who wants to learn. An excellent recent example: Generative Adversarial Networks for Beginners, by Jon Bruner.
This talk covers current trends in AI, industry use cases, and recent highlights from the AI Conf series presented by O'Reilly and Intel, plus related materials from Safari learning platform, Strata Data, Data Show, and the upcoming JupyterCon.
Along with reporting, we're leveraging AI in Media. This talk dives into O'Reilly uses of deep learning -- combined with ontology, graph algorithms, probabilistic data structures, and even some evolutionary software -- to help editors and customers alike accomplish more of what they need to do.
In particular, we'll show two open source projects in Python from O'Reilly's AI team:
• pytextrank built atop spaCy, NetworkX, datasketch, providing graph algorithms for advanced NLP and text analytics
• nbtransom leveraging Project Jupyter for a human-in-the-loop design pattern approach to AI work: people and machines collaborating on content annotation
Lessons learned from 3 (going on 4) generations of Jupyter use cases at O'Reilly Media. In particular, about "Oriole" tutorials which combine video with Jupyter notebooks, Docker containers, backed by services managed on a cluster by Marathon, Mesos, Redis, and Nginx.
https://conferences.oreilly.com/fluent/fl-ca/public/schedule/detail/62859
https://conferences.oreilly.com/velocity/vl-ca/public/schedule/detail/62858
Strata UK 2017. Computable content leverages Jupyter notebooks to make learning materials more powerful by integrating compute engines, data sources, etc. O’Reilly Media extended this approach to create the new Oriole Online Tutorial medium, publishing notebooks from authors along with video timelines. (A free public tutorial, Regex Golf, by Peter Norvig demonstrates what’s possible with this technology integration.) Each user session launches a Docker container on a Mesos cluster for fully personalized compute environments. The UX is entirely browser based.
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Use of standards and related issues in predictive analyticsPaco Nathan
My presentation at KDD 2016 in SF, in the "Special Session on Standards in Predictive Analytics In the Era of Big and Fast Data" morning track about PMML and PFA http://dmg.org/kdd2016.html
Presented 2015-08-24 at SF Bay ACM, held at the eBay south campus in San Jose.
http://meetup.com/SF-Bay-ACM/events/221693508/
Project Jupiter https://jupyter.org/ evolved from IPython notebooks, and now supports a wide variety of programming language back-ends. Notebooks have proven to be effective tools used in Data Science, providing convenient packages for what Don Knuth coined as "literate programming" in the 1980s: code plus exposition in markdown. Results of running the code appear in-line as interactive graphics -- all packaged as collaborative, web-based documents. Some have said that the introduction of cloud-based notebooks is nearly as large of a fundamental change in software practice as the introduction of spreadsheets.
O'Reilly Media has been considering the question, "What comes after books and video?" Or, as one might imagine more pointedly, what comes after Kindle? To that point we have collaborated with Project Jupyter to integrate notebooks into our content management process, allowing authors to generate articles, tutorials, reports, and other media products as notebooks that also incorporate video segments. Code dependencies are containerized using Docker, and all of the content gets managed in Git repositories. We have added another layer, an open source project called Thebe that provides a kind of "media player" for embedding the containerized notebooks into web pages
GalvanizeU Seattle: Eleven Almost-Truisms About DataPaco Nathan
http://www.meetup.com/Seattle-Data-Science/events/223445403/
Almost a dozen almost-truisms about Data that almost everyone should consider carefully as they embark on a journey into Data Science. There are a number of preconceptions about working with data at scale where the realities beg to differ. This talk estimates that number to be at least eleven, through probably much larger. At least that number has a great line from a movie. Let's consider some of the less-intuitive directions in which this field is heading, along with likely consequences and corollaries -- especially for those who are just now beginning to study about the technologies, the processes, and the people involved.
Microservices, containers, and machine learningPaco Nathan
http://www.oscon.com/open-source-2015/public/schedule/detail/41579
In this presentation, an open source developer community considers itself algorithmically. This shows how to surface data insights from the developer email forums for just about any Apache open source project. It leverages advanced techniques for natural language processing, machine learning, graph algorithms, time series analysis, etc. As an example, we use data from the Apache Spark email list archives to help understand its community better; however, the code can be applied to many other communities.
Exsto is an open source project that demonstrates Apache Spark workflow examples for SQL-based ETL (Spark SQL), machine learning (MLlib), and graph algorithms (GraphX). It surfaces insights about developer communities from their email forums. Natural language processing services in Python (based on NLTK, TextBlob, WordNet, etc.), gets containerized and used to crawl and parse email archives. These produce JSON data sets, then we run machine learning on a Spark cluster to find out insights such as:
* What are the trending topic summaries?
* Who are the leaders in the community for various topics?
* Who discusses most frequently with whom?
This talk shows how to use cloud-based notebooks for organizing and running the analytics and visualizations. It reviews the background for how and why the graph analytics and machine learning algorithms generalize patterns within the data — based on open source implementations for two advanced approaches, Word2Vec and TextRank The talk also illustrates best practices for leveraging functional programming for big data.
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.
QCon São Paulo: Real-Time Analytics with Spark StreamingPaco Nathan
"Real-Time Analytics with Spark Streaming" presented at QCon São Paulo, 2015-03-26
http://qconsp.com/presentation/real-time-analytics-spark-streaming
This talk presents an overview of Spark and its history and applications, then focuses on the Spark Streaming component used for real-time analytics. We compare it with earlier frameworks such as MillWheel and Storm, and explore industry motivations for open-source micro-batch streaming at scale.
The talk will include demos for streaming apps that include machine-learning examples. We also consider public case studies of production deployments at scale.
We’ll review the use of open-source sketch algorithms and probabilistic data structures that get leveraged in streaming – for example, the trade-off of 4% error bounds on real-time metrics for two orders of magnitude reduction in required memory footprint of a Spark app.
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
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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/
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!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
Cascading meetup #4 @ BlueKai
1. Cascading Meetup #4
BlueKai
Cupertino, CA
2013-03-05
Copyright @2013, Concurrent, Inc.
Tuesday, 05 March 13 1
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LOB use cases drive the demand for Big Data apps
4. Enterprise Data Workflows
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An example… in the back office
Organizations have substantial investments Web
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Cluster
Reporting
Tuesday, 05 March 13 4
Enterprise organizations have seriously ginormous investments in existing back office practices:
people, infrastructure, processes
5. Enterprise Data Workflows
Customers
An example… for the heavy lifting!
“Main Street” firms are migrating Web
App
workflows to Hadoop, for cost
savings and scale-out
logs Cache
logs
Logs
Support
source
trap sink
tap
tap tap
Data
Modeling PMML
Workflow
source
sink
tap
tap
Analytics
Cubes customer
Customer
profile DBs
Prefs
Hadoop
Cluster
Reporting
Tuesday, 05 March 13 5
“Main Street” firms have invested in Hadoop to address Big Data needs,
off-setting their rising costs for Enterprise licenses from SAS, Teradata, etc.
6. Two Avenues…
Enterprise: must contend with
complexity at scale everyday…
incumbents extend current practices and
infrastructure investments – using J2EE,
complexity ➞
ANSI SQL, SAS, etc. – to migrate
workflows onto Apache Hadoop while
leveraging existing staff
Start-ups: crave complexity and
scale to become viable…
new ventures move into Enterprise space
to compete using relatively lean staff,
while leveraging sophisticated engineering
practices, e.g., Cascalog and Scalding
scale ➞
Tuesday, 05 March 13 6
Enterprise data workflows are observed in two modes: start-ups approaching complexity and incumbent firms grappling with complexity
7. Two Avenues…
Enterprise: must contend with
complexity at scale everyday…
incumbents extend current practices and
infrastructure investments – using J2EE,
complexity ➞
ANSI SQL, SAS, etc. – to migrate
workflows onto Apache Hadoop while
leveraging existing staff
Hadoop almost never gets used
in isolation; data workflows define
Start-ups: crave complexity and
scale to become viable… the “glue” required for system
new ventures move into Enterprise space of Enterprise apps
integration
to compete using relatively lean staff,
while leveraging sophisticated engineering
practices, e.g., Cascalog and Scalding
scale ➞
Tuesday, 05 March 13 7
Hadoop is almost never used in isolation.
Enterprise data workflows are about system integration.
There are a couple different ways to arrive at the party.
8. Cascading Meetup
Document
Collection
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1. Enterprise Data Workflows
2. ANSI SQL Support
3. Test-Driven Development
Tuesday, 05 March 13 8
9. Cascading workflows – ANSI SQL
• collab with Optiq – industry-proven code base
Customers
• ANSI SQL parser/optimizer atop Cascading
flow planner Web
App
• JDBC driver to integrate into existing
tools and app servers logs
logs Cache
Logs
• relational catalog over a collection Support
source
of unstructured data trap
tap
tap sink
tap
• SQL shell prompt to run queries Modeling PMML
Data
Workflow
source
sink
tap
tap
Analytics
Cubes customer
Customer
profile DBs
Prefs
Hadoop
Cluster
Reporting
Tuesday, 05 March 13 9
ANSI SQL as “machine code” -- the lingua franca of Enterprise system integration.
Cascading partnered with Optiq, the team behind Mondrian, etc., with an Enterprise-proven code base for an ANSI SQL parser/optimizer.
10. Cascading workflows – ANSI SQL
• collab with Optiq – industry-proven code base
Customers
• ANSI SQL parser/optimizer atop Cascading
flow planner Web
App
• JDBC driver to integrate into existing
tools and app servers logs
logs Cache
Premise: most SQL in the world gets Logs
• relational catalog over a collection Support
of unstructured datawritten by machines… trap
tap
source
tap sink
tap
• SQL shell prompt to run isn’t a database; this is about making
This queries Modeling PMML
Data
Workflow
machine-to-machine communications sink
tap
source
tap
simpler and more robust at scale.
Analytics
Cubes customer
Customer
profile DBs
Prefs
Hadoop
Cluster
Reporting
Tuesday, 05 March 13 10
ANSI SQL as “machine code” -- the lingua franca of Enterprise system integration.
Cascading partnered with Optiq, the team behind Mondrian, etc., with an Enterprise-proven code base for an ANSI SQL parser/optimizer.
11. Cascading workflows – ANSI SQL
• enable analysts without retraining
on Hadoop, etc. Customers
• transparency for Support, Ops, Web
App
Finance, et al.
logs Cache
logs
Logs
Support
source
trap sink
tap
tap tap
Data
a language for queries – not a database, Modeling PMML
Workflow
but ANSI SQL as a DSL for workflows sink
tap
source
tap
Analytics
Cubes customer
Customer
profile DBs
Prefs
Hadoop
Cluster
Reporting
Tuesday, 05 March 13 11
ANSI SQL as “machine code” -- the lingua franca of Enterprise system integration.
Cascading partnered with Optiq, the team behind Mondrian, etc., with an Enterprise-proven code base for an ANSI SQL parser/optimizer.
12. ANSI SQL – reviews
Open Source 'Lingual' Helps SQL Devs Unlock Hadoop
Thor Olavsrud, 2013-02-22
cio.com/article/729283/Open_Source_Lingual_Helps_SQL_Devs_Unlock_Hadoop
Hadoop Apps Without MapReduce Mindsets
Adrian Bridgwater, 2013-02-28
drdobbs.com/open-source/hadoop-apps-without-mapreduce-mindsets/240149708
Concurrent gives old SQL users new Hadoop tricks
Jack Clark, 2013-02-20
theregister.co.uk/2013/02/20/hadoop_sql_translator_lingual_launches/
Concurrent Open Source Project Ties SQL to Hadoop
Michael Vizard, 2013-02-21
itbusinessedge.com/blogs/it-unmasked/concurrent-open-source-project-ties-sql-to-hadoop.html
Concurrent Releases Lingual, a SQL DSL for Hadoop
Boris Lublinsky, 2013-02-28
infoq.com/news/2013/02/Lingual
Tuesday, 05 March 13 12
13. ANSI SQL – CSV data in local file system
cascading.org/lingual
Tuesday, 05 March 13 13
The test database for MySQL is available for download from https://launchpad.net/test-db/
Here we have a bunch o’ CSV flat files in a directory in the local file system.
Use the “lingual” command line interface to overlay DDL to describe the expected table schema.
14. ANSI SQL – shell prompt, catalog
cascading.org/lingual
Tuesday, 05 March 13 14
Use the “lingual” SQL shell prompt to run SQL queries interactively, show catalog, etc.
15. ANSI SQL – queries
cascading.org/lingual
Tuesday, 05 March 13 15
Here’s an example SQL query on that “employee” test database from MySQL.
16. ANSI SQL – layers
abstraction RDBMS JVM Cluster
parser ANSI SQL ANSI SQL
compliant parser compliant parser
optimizer logical plan, logical plan,
optimized based on stats optimized based on stats
planner physical plan API “plumbing”
machine query history, app history,
data table stats tuple stats
topology b-trees, etc. heterogenous, distributed:
Hadoop, IMDG, etc.
visualization ERD flow diagram
schema table schema tuple schema
catalog relational catalog tap usage DB
provenance (manual audit) data set
producers/consumers
Tuesday, 05 March 13 16
When you peel back the onion skin on a SQL query, each of the abstraction layers used in an RDBMS has an analogue (or better) in the context of Enterprise Data Workflows running on JVM clusters
17. ANSI SQL – JDBC driver
public void run() throws ClassNotFoundException, SQLException {
Class.forName( "cascading.lingual.jdbc.Driver" );
Connection connection =
DriverManager.getConnection( "jdbc:lingual:local;schemas=src/main/resources/data/example" );
Statement statement = connection.createStatement();
ResultSet resultSet = statement.executeQuery(
"select *n"
+ "from "EXAMPLE"."SALES_FACT_1997" as sn"
+ "join "EXAMPLE"."EMPLOYEE" as en"
+ "on e."EMPID" = s."CUST_ID"" );
while( resultSet.next() ) {
int n = resultSet.getMetaData().getColumnCount();
StringBuilder builder = new StringBuilder();
for( int i = 1; i <= n; i++ ) {
builder.append( ( i > 1 ? "; " : "" )
+ resultSet.getMetaData().getColumnLabel( i ) + "=" + resultSet.getObject( i ) );
}
System.out.println( builder );
}
resultSet.close();
statement.close();
connection.close();
}
Tuesday, 05 March 13 17
Note that in this example the schema for the DDL has been derived directly from the CSV files.
In other words, point the JDBC connection at a directory of flat files and query as if they were already loaded into SQL.
18. ANSI SQL – JDBC driver
$ gradle clean jar
$ hadoop jar build/libs/lingual-examples–1.0.0-wip-dev.jar
CUST_ID=100; PROD_ID=10; EMPID=100; NAME=Bill
CUST_ID=150; PROD_ID=20; EMPID=150; NAME=Sebastian
Caveat: if you absolutely positively must have sub-second
SQL query response for Pb-scale data on a 1000+ node
cluster… Good luck with that! (call the MPP vendors)
This ANSI SQL library is primarily intended for batch
workflows – high throughput, not low-latency –
for many under-represented use cases in Enterprise IT.
It’s essentially ANSI SQL as a DSL.
Tuesday, 05 March 13 18
success
19. Cascading Meetup
Document
Collection
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token
M
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Stop Word token
List
RHS
Count
Word
Count
1. Enterprise Data Workflows
2. ANSI SQL Support
3. Test-Driven Development
Tuesday, 05 March 13 19
21. Test-Driven Development (TDD)
In terms of Big Data apps,TDD is not
generally part of the conversation
Tuesday, 05 March 13 21
TDD is not usually high on the list when people start discussing Big Data apps.
22. Traps – Cascading “exceptional data”
• assert patterns (regex) on the tuple streams
Customers
• adjust assert levels, like log4j levels
• define traps on branches Web
App
• tuples which fail asserts get trapped
logs Cache
logs
Logs
Support
source
trap sink
tap
tap tap
Data
Modeling PMML
Workflow
source
sink
tap
tap
Analytics
Cubes customer
Customer
profile DBs
Prefs
Hadoop
Cluster
Reporting
Tuesday, 05 March 13 22
An innovation in Cascading was to introduce the notion of a “data exception”,
based on setting stream assertion levels as part of the business logic of an app.
23. Traps – example code
// set up...
Pipe etlPipe = new Pipe( "etlPipe" );
// some processing...
AssertMatches assertMatches = new AssertMatches( ".*true" );
etlPipe = new Each( etlPipe, AssertionLevel.STRICT, assertMatches );
// some processing...
FlowDef flowDef = FlowDef.flowDef().setName( "etl" )
.addSource( etlPipe, jsonTap )
.addTrap( etlPipe, trapTap )
.addTailSink( etlPipe, cacheTap );
if( options.has( "assert" ) )
flowDef.setAssertionLevel( AssertionLevel.STRICT );
else
flowDef.setAssertionLevel( AssertionLevel.NONE );
Tuesday, 05 March 13 23
Example use in Cascading code
24. Traps – redirect exceptions in production
shunt the trapped exceptional data to other
parts of the organization: Customers
• Ops: notifications Web
App
• QA: investigate data anomalies
• Support: review customer records logs
logs
Logs
Cache
•
Finance: audit Support
source
trap sink
tap
tap tap
Data
Modeling PMML
Workflow
source
sink
tap
tap
Analytics
Cubes customer
Customer
profile DBs
Prefs
Hadoop
Cluster
Reporting
Tuesday, 05 March 13 24
25. TDD – practice at scale
1. assert expected patterns in raw input
2. run just that, to find edge cases
3. handle the edge cases for input data
4. assert expected patterns after first chunk of processing
5. run just that, to verify failure
6. code until test passes GIS Regex
tree
Scrub
export parse-tree species
7. repeat #4 for each chunk
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
M R
Tuesday, 05 March 13 25
26. TDD – Cascalog features
consider that TDD is about asserting and negating logical
predicates…
• Cascalog is based on logical predicates
• function definitions as composable subqueries
• functions are not particularly far from being unit tests
• Midje: facts, mocks
sritchie.github.com/2011/09/30/testing-cascalog-with-midje.html
sritchie.github.com/2012/01/22/cascalog-testing-20.html
Tuesday, 05 March 13 26
Moreover, the Cascalog language by Nathan Marz, Sam Ritchie, et al., nearly uses TDD as its methodology --
in the transition from ad-hoc queries as logic predicates, then composing those predicates into large-scale apps.
27. Cascading Meetup
Document
Collection
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Tokenize
token
M
HashJoin Regex
Left token
GroupBy R
Stop Word token
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RHS
Count
Word
Count
1. Enterprise Data Workflows
2. ANSI SQL Support
3. Test-Driven Development
…plus, a proposal
Tuesday, 05 March 13 27
28. ANSI SQL – multiple flows
GIS Regex
tree
Scrub
export parse-tree species
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
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Suppose your organization is responsible
for an large-scale app…
Multiple teams develop reusable libraries…
Tuesday, 05 March 13 28
Suppose you have a app with a complex flow diagram like this, with contributions to the business logic from different departments…
29. ANSI SQL – multiple flows
GIS Regex
tree
Scrub
export parse-tree species
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
M R
Data Analysts: ANSI SQL queries
for data prep
(displaces Hive, etc.)
Tuesday, 05 March 13 29
Analysts are generally working with ANSI SQL queries in a DW, e.g., for ETL, data prep, pulling data cubes.
These can migrate into a Cascading app to run on Hadoop.
30. ANSI SQL – multiple flows
GIS Regex
tree
Scrub
export parse-tree species
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
M R
Server-side Engineering: HBase tap
for customer profiles
(integrating other components)
Tuesday, 05 March 13 30
Engineering provides integration with customer profiles, e.g., transactional data objects in HBase.
These can migrate into a Cascading app to run on Hadoop.
31. ANSI SQL – multiple flows
GIS Regex
tree
Scrub
export parse-tree species
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
M R
Ops + Support: Traps get
routed to customer review
(ties into notifications, etc.)
Tuesday, 05 March 13 31
Support needs to review exceptional data, via reports/notifications.
These can migrate into a Cascading app to run on Hadoop.
32. ANSI SQL – multiple flows
GIS Regex
tree
Scrub
export parse-tree species
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
M R
Data Scientists: R => PMML
for predictive models
(displaces SAS, etc.)
Tuesday, 05 March 13 32
Scientists perform their model creation work in R, Weka, SAS, Microstrategy, etc., which can export as PMML.
These can migrate into a Cascading app to run on Hadoop.
33. ANSI SQL – multiple flows
GIS Regex
tree
Scrub
export parse-tree species
M M
Estimate
Join Geohash
height
Regex
src
parse-gis
Tree Filter
tree
Metadata height
Failure M
Traps
Calculate Filter Sum
Join
distance distance moment Filter
sum_moment
Estimate R M R M
road
road
Regex
traffic
parse-road
shade
Estimate Road
Join
Albedo Segments
Geohash Join
M
R
Road
Metadata gps R
gps reco
logs
Count
Geohash Max
gps_count
recent_visit
M R
App Engineering: Java/Scala/Clojure
for business logic in data pipelines
(displaces Pig, etc.)
Tuesday, 05 March 13 33
Generally the revenue apps require some custom business logic -- representing business process for LOB.
These can migrate into a Cascading app to run on Hadoop.