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This document provides an overview of a Drupal training covering various topics from September 12-20, 2014. The training will introduce participants to core Drupal concepts and components including nodes, content types, taxonomies, views, panels, modules, themes, and the database layer. It will cover setting up a development environment, installing Drupal, configuring the system, and extending Drupal through custom modules and themes. Participants will learn how Drupal handles user requests and its event-driven hook system. The document also provides contact information for the trainer.
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The presentation is about why Drupal is a good choice as a framework to build your next big product / service idea if you are a startup. The presentation covers the reasons and also introduces concepts in Drupal that will allow a startup to get their web application up and running without writing a line of code. Then again the idea is to never write a line of code but also about picking a platform where you can get started fast and then build and customize later.
This document discusses using Drupal to build business applications rather than just websites. It argues that Drupal is a content management platform rather than just a CMS due to its modularity, user management, and other features. Examples of non-CMS applications built in Drupal include e-commerce sites and calendars. The document also describes how Drupal was successfully used to build a large and complex enterprise application, and how its flexibility allowed unanticipated needs to be met. Key Drupal modules for application development include Entity API, Entity Construction Kit, References, and Views.
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This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.
The survey results show that Hydra projects have an average team size of 6 people. Agile Scrum is the most commonly used methodology. Jira and GitHub are popular tools for managing requirements and source control. The main benefits of Hydra cited are the active community for sharing knowledge and best practices, and the reusable technology including Ruby on Rails and Fedora. The biggest challenges are obtaining resources and avoiding technical debt as the software evolves.
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Drupal with CONTENTdm Digital Collections, Drupal Camp Vancouver 2012Marcus Emmanuel Barnes
CONTENTdm is a digital collections management application that provides several important administration features of value when undertaking a digitization project. Many institutions already use Drupal to power their web presence. CONTENTdm's native interface makes creating a single integrated website difficult. The CONTENTdm Integration Modules project was created by Mark Jordan of Simon Fraser University Library to solve this issue by providing a series of Drupal modules that help create a single integrated website - allowing the searching of digital collections hosted in a CONTENTdm server from within a Drupal website.
By the end of this talk, you will have a better understanding of:
*Why you would want to use CONTENTdm rather than simply Drupal for digital collections management;
*How the CONTENTdm Integration Modules work under the hood;
*How to install and setup these modules with Drupal to help present an integrated website.
This talk will be of particular interest to those who develop Drupal websites for use in libraries, archives, or museums, but also to Drupal developers and administrators in general.
LITA Preconference: Getting Started with Drupal (handout)Rachel Vacek
This document provides an overview of popular modules for the content management system Drupal, focusing on modules useful for libraries. It discusses modules for administration, content management, performance, navigation, user management, and library-specific functions. Popular modules are highlighted for tasks like custom fields, views, panels, web forms, images, editors, spam prevention, taxonomy, scheduling, groups, analytics, events, authentication, searching catalogs and databases. Resources for learning Drupal like books, tutorials, communities and publications are also listed.
This document provides an overview of a Drupal training covering various topics from September 12-20, 2014. The training will introduce participants to core Drupal concepts and components including nodes, content types, taxonomies, views, panels, modules, themes, and the database layer. It will cover setting up a development environment, installing Drupal, configuring the system, and extending Drupal through custom modules and themes. Participants will learn how Drupal handles user requests and its event-driven hook system. The document also provides contact information for the trainer.
Drupal as a Rapid Application Development (RAD) Framework for StartupsZyxware Technologies
The presentation is about why Drupal is a good choice as a framework to build your next big product / service idea if you are a startup. The presentation covers the reasons and also introduces concepts in Drupal that will allow a startup to get their web application up and running without writing a line of code. Then again the idea is to never write a line of code but also about picking a platform where you can get started fast and then build and customize later.
This document discusses using Drupal to build business applications rather than just websites. It argues that Drupal is a content management platform rather than just a CMS due to its modularity, user management, and other features. Examples of non-CMS applications built in Drupal include e-commerce sites and calendars. The document also describes how Drupal was successfully used to build a large and complex enterprise application, and how its flexibility allowed unanticipated needs to be met. Key Drupal modules for application development include Entity API, Entity Construction Kit, References, and Views.
Big Data Developers Moscow Meetup 1 - sql on hadoopbddmoscow
This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.
The survey results show that Hydra projects have an average team size of 6 people. Agile Scrum is the most commonly used methodology. Jira and GitHub are popular tools for managing requirements and source control. The main benefits of Hydra cited are the active community for sharing knowledge and best practices, and the reusable technology including Ruby on Rails and Fedora. The biggest challenges are obtaining resources and avoiding technical debt as the software evolves.
An overview of the Hydra digital repository framework and the community that builds and maintains it. Presented at Open Repositories 2013 in Charlottetown, Prince Edward Island, Canada.
Drupal with CONTENTdm Digital Collections, Drupal Camp Vancouver 2012Marcus Emmanuel Barnes
CONTENTdm is a digital collections management application that provides several important administration features of value when undertaking a digitization project. Many institutions already use Drupal to power their web presence. CONTENTdm's native interface makes creating a single integrated website difficult. The CONTENTdm Integration Modules project was created by Mark Jordan of Simon Fraser University Library to solve this issue by providing a series of Drupal modules that help create a single integrated website - allowing the searching of digital collections hosted in a CONTENTdm server from within a Drupal website.
By the end of this talk, you will have a better understanding of:
*Why you would want to use CONTENTdm rather than simply Drupal for digital collections management;
*How the CONTENTdm Integration Modules work under the hood;
*How to install and setup these modules with Drupal to help present an integrated website.
This talk will be of particular interest to those who develop Drupal websites for use in libraries, archives, or museums, but also to Drupal developers and administrators in general.
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This document provides an overview of popular modules for the content management system Drupal, focusing on modules useful for libraries. It discusses modules for administration, content management, performance, navigation, user management, and library-specific functions. Popular modules are highlighted for tasks like custom fields, views, panels, web forms, images, editors, spam prevention, taxonomy, scheduling, groups, analytics, events, authentication, searching catalogs and databases. Resources for learning Drupal like books, tutorials, communities and publications are also listed.
This document discusses scalable machine learning using Apache Hadoop and Apache Mahout. It describes what scalable machine learning means in the context of large datasets, provides examples of common machine learning use cases like search and recommendations, and outlines approaches for scaling machine learning algorithms using Hadoop. It also describes the capabilities of the Apache Mahout machine learning library for collaborative filtering, clustering, classification and other tasks on Hadoop clusters.
This presentation shows reco4j features and vision. In particular we add the new concept of context aware recommendation and how we integrate it into reco4j. In this new presentation there is also some piece of code that show how simple is integrate our software. See the project site for more details here: http://www.reco4j.org
This document provides an introduction to the open source content management system (CMS) Drupal. It discusses what Drupal is, its advantages over other CMS platforms like its large user community and flexibility. The document also covers when not to use Drupal, such as when requirements are too complex. Case studies of sites using Drupal are presented, and instructions are provided on how to find and install Drupal.
Drupal and the semantic web - SemTechBiz 2012scorlosquet
This document provides a summary of a presentation on leveraging the semantic web with Drupal 7. The presentation introduces Drupal and its uses as a content management system. It discusses Drupal 7's integration with the semantic web through its built-in RDFa support and contributed modules that add additional semantic web capabilities like SPARQL querying and JSON-LD serialization. The presentation demonstrates these semantic web features in Drupal through examples and demos. It also introduces Domeo, a web-based tool for semantically annotating online documents that can integrate with Drupal.
Apache Mahout is a scalable machine learning library built on Hadoop. It allows Hadoop to perform data mining tasks like collaborative filtering, clustering, and classification by breaking complex problems into parallel tasks across Hadoop clusters. Mahout's stable release is version 0.9 from February 2014. It provides machine learning functionality that other open source libraries lack, such as community support, documentation, scalability, and applicability beyond research.
Getting Started with Drupal - HandoutsRachel Vacek
This document provides an overview of popular contributed and core modules for the content management system Drupal. It is organized into sections on administration, content management, performance, navigation, publishing, user management, SEO/analytics, events/calendars, authentication, and library-specific modules. Key modules highlighted include Views, CCK, Context, Panels, Webform, Taxonomy Menu, Pathauto, Organic Groups, Google Analytics, Date, Calendar, LDAP, and library-focused modules like LT4L, Question/Answer for email reference, and Fedora REST API. Resources for learning more about Drupal like books, online tutorials, communities and publications are also listed.
These slides were presentet at Munich Meetup of April 18th. They present the reco4j project, its high view and it vision.
See the project site for more details here: http://www.reco4j.org
Demystifying Decoupled Drupal for Developers & Content AuthorsRachel Wandishin
Today, with the diversity of customer experiences, developers require a WCM that provides flexibility and creativity in display output, and the ability to build innovative experiences that take advantage of diverse front-ends (i.e. JavaScript frameworks and libraries).
Join our session to learn how Acquia’s WCM, Drupal, delivers universal content flexibility — providing the greatest creative flexibility to front-end developers and content authors to build content-rich experiences for any channel, device or mode of interaction.
We’ll cover how the Acquia platform supports decoupled Drupal architectures and how you might use Drupal in three different modes that cover the “best of all worlds” - traditional, decoupled, and progressively decoupled WCM. As a result, developers have full flexibility and creativity, and content creators have full content management control - only Drupal provides this flexibility to all stakeholders.
During this webinar, we will investigate the following topics:
- An intro to decoupled Drupal concepts, options & supported features
- Decoupled Drupal best practices and trade offs
- Acquia customer case studies using decoupled Drupal
- Decoupled Drupal improvements and upcoming releases
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ScalaCourses.com has been serving online Scala and Play training material to students for over two years. ScalaCourses.com teaches courses on the same technology stack that the web site runs on. The Cadenza application that powers ScalaCourses.com is a Play Framework 2 application, written in Scala and using Akka, Slick, AWS and Postgres. Some of the architectural features in Cadenza that allow a modest-sized Play application to serve large amounts of multimedia data efficiently is discussed, including technical details of how to work with an immutable domain model that can be modified.
Over the last 2+ years the underlying technology has changed a lot; a brief history of Play Framework will be recounted, and how that impacted Cadenza. The talk concludes with a proposal regarding Play Framework's future.
This document outlines the objectives, content, and structure of a 28-30 hour training course on Hadoop and its ecosystem. The course will provide both theoretical and hands-on instruction on topics such as Hadoop architecture and components, HDFS, MapReduce, YARN, Hive, Pig, Sqoop, HBase, and Oozie. Participants will learn how to install Hadoop clusters, develop MapReduce programs, integrate Hadoop components, and apply best practices for Hadoop development, administration, and management. The goal is for attendees to gain the skills needed to architect Hadoop projects and leverage its ecosystem for data analysis and storage.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
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Drupal and the Semantic Web - ESIP Webinarscorlosquet
This document summarizes a presentation about using semantic web technologies like the Resource Description Framework (RDF) and Linked Data with Drupal 7. It discusses how Drupal 7 maps content types and fields to RDF vocabularies by default and how additional modules can add features like mapping to Schema.org and exposing SPARQL and JSON-LD endpoints. The presentation also covers how Drupal integrates with the larger Semantic Web through technologies like Linked Open Data.
This document contains the resume of Vipin KP, who has over 5 years of experience as a Big Data Hadoop Developer. He has extensive experience developing Hadoop applications for clients such as EMC, Apple, Dun & Bradstreet, Neilsen, Commonwealth Bank of Australia, and Nokia Siemens Network. He has expertise in technologies such as Hadoop, Hive, Pig, Sqoop, Oozie, and Spark and has developed ETL processes, data pipelines, and analytics solutions on Hadoop clusters. He holds a Master's degree in Computer Science and is Cloudera certified in Hadoop development.
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The document discusses Symantec's use of Cloudbreak and Ambari to provision their big data platforms. Some key points:
- Symantec handles huge volumes of security data and needed a scalable analytics platform.
- They used Cloudbreak to provision Hadoop clusters on multiple clouds in a self-service manner for developers.
- Cloudbreak was customized to support their specific AWS configurations, Openstack integration, and monitoring/alerting needs.
- Ambari is used for cluster installation and management. Custom stacks were also developed.
- Monitoring is done via Ambari, OpenTSDB, Grafana and PagerDuty to ensure cluster health.
This document discusses how the Dachis Group uses Cassandra and Hadoop for social business intelligence. They collect raw social media data and normalize it for analysis in Cassandra. Hadoop is used to calculate foundational metrics. The data is enriched and analyzed using Pig and Oozie workflows. Metrics are stored in Postgres. They launched products like the Social Business Index and Social Performance Monitor to measure social media effectiveness for companies. Lessons learned include dealing with big data bugs and involvement in open source communities.
In this presentation we are talking about Drupal? What does it mean to a programmer? And basically we want to get the answer for "What's in it for me?" when we use Drupal.
The presentation also shows some of the main highlights of Drupal? How is Drupal structured and how does the information flow?
Also we have a list of the most commonly used modules and a list of some of the key new features of Drupal 8.
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Publishing Linked Open Data in a user-appealing way is still a challenge: Generic solutions to convert arbitrary RDF structures to HTML out-of-the-box are available, but leave users perplexed. Custom-built web applications to enrich web pages with semantic tags "under the hood" require high efforts in programming. Given this dilemma, content management systems (CMS) could be a natural enhancement point for data on the web. In the case of Drupal, one of the most popular CMS nowadays, Semantic Web enrichment is provided as part of the CMS core. In a simple declarative approach, classes and properties from arbitrary vocabularies can be added to Drupal content types and fields, and are turned into Linked Data on the web pages automagically. The embedded RDFa marked-up data can be easily extracted by other applications. This makes the pages part of the emerging Web of Data, and in the same course helps discoverability with the major search engines.
In the workshop, you will learn how to make use of the built-in Drupal 7 features to produce RDFa enriched pages. You will build new content types, add custom fields and enhance them with RDF markup from mixed vocabularies. The gory details of providing LOD-compatible "cool" URIs will not be skipped, and current limitations of RDF support in Drupal will be explained. Exposing the data in a REST-ful application programming interface or as a SPARQL endpoint are additional options provided by Drupal modules. The workshop will also introduce modules such as Web Taxonomy, which allows linking to thesauri or authority files on the web via simple JSON-based autocomplete lookup. Finally, we will touch the upcoming Drupal 8 version. (Workshop announcement)
A talk given by Ted Dunning on February 2013 on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
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Today, with the diversity of customer experiences, developers require a WCM that provides flexibility and creativity in display output, and the ability to build innovative experiences that take advantage of diverse front-ends (i.e. JavaScript frameworks and libraries).
Join our session to learn how Acquia’s WCM, Drupal, delivers universal content flexibility — providing the greatest creative flexibility to front-end developers and content authors to build content-rich experiences for any channel, device or mode of interaction.
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How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
HCL Notes and Domino License Cost Reduction in the World of DLAU
Recommendations in Drupal (Drupal DevDays Barcelona 2012)
1. Personalisation and
Recommendations using Drupal
• Keywords:
– Personalisation
– Recommendations
– Scalable machine learning
– Predictions
– Similarity
– Data Mining
– Big Data
– Trend Spotting
– Clustering
Drupal Developer Days Barcelona
2012.06.16
2. Kendra Initiative
• Mission
– Foster an Open Distributed Marketplace for Digital
Media
• EU funded
– P2P-Next
• http://www.p2p-next.org
– SARACEN = Socially Aware, collaboRative, scAlable
Coding mEdia distributioN
• http://www.saracen-p2p.eu
Drupal Developer Days Barcelona
2012.06.16
3. Deliverables
• Kendra Signpost
– Metadata interoperability, mapping and transformation
• Smart Filters
– Portable preferences and filters
• Kendra Social, Kendra Hub
– Social networking management tools
• Standards work
– OpenSocial extension
– Social API – see Abstracting Social Networking functionality in
Drupal sprint
• Kendra Match
– Searching and recommendation
Drupal Developer Days Barcelona
2012.06.16
4. Components
• Drupal Recommender API module
• Recommender helper modules
• async_command module
• Apache Mahout or cloud service
• Hadoop cluster (optional)
Drupal Developer Days Barcelona
2012.06.16
5. Industry Examples
• Amazon
• Netflix
• Spotify, Pandora
• Facebook, LinkedIn
• OKCupid
• iTunes: Genius; app store - not so much
Drupal Developer Days Barcelona
2012.06.16
6. Machine learning
• Collaborative Filtering
– AKA recommender engines
• Clustering
• Classification
Drupal Developer Days Barcelona
2012.06.16
7. Collaborative Filtering
• Input: preference data
• Output: predictions
• Preference = <uid1, (nid1 or uid2), w1>
– w1 = signed integer representing weight of uid1-
nid1 or uid1-uid2 correlation (affinity)
• Prediction = <uid1, (nid1or uid2), w2>
– w2 = float representing strength of uid1-nid1 or
uid1-uid2 correlation
Drupal Developer Days Barcelona
2012.06.16
8. Enter Mahout
• Apache Mahout is a scalable machine learning
library that supports large data sets.
• Launched Spring 2010
• Grew from the Apache Lucene project (basis
for Apache Solr)
• Merged with Taste project
Drupal Developer Days Barcelona
2012.06.16
9. Use Cases
• Recommendation mining
• Clustering
• Classification
• Frequent itemset mining
Drupal Developer Days Barcelona
2012.06.16
11. Hadoop
• Provides clustering capabilities
• Not trivial to set up
• Not yet implemented in Recommender API
(issue #1206840)
Drupal Developer Days Barcelona
2012.06.16
12. Recommender API
• Drupal 7 (alpha) & 6 (beta)
• Can run either on same server as Apache web
server or on a remote server
• Java helper program (was PHP)
• Uses JDBC and Java Persistence API (JPA)
• Drupal helper modules
Drupal Developer Days Barcelona
2012.06.16
13. Recommender API helper modules
• Browsing History Recommender
• OG Similar groups module
• Ubercart Products Recommender
• Fivestar Recommender
• Points Voting Recommender
• Flag Recommender
Drupal Developer Days Barcelona
2012.06.16
14. Asynchronous operation
• Async_command module
– Talks to Mahout
– Typically run via cron
• Results are stored directly in Drupal db
– Recommender tables
– Via JDBC
Drupal Developer Days Barcelona
2012.06.16
15. Hosting Solutions
• Self-hosted: all-in-one (web server, database
server, recommender server) - has its pro’s &
cons
• Recommender API Cloud Service - looking for
beta testers
• Amazon Elastic MapReduce (EMR)
Drupal Developer Days Barcelona
2012.06.16
16. Installing Mahout
• Prerequisites:
– Dedicated VM if possible
– Linux, Mac OSX Leopard 10.5.6 or later, Windows
(Cygwin)
– Java JDK 1.6
– Maven 2.0.11 or higher (maven.apache.org)
Drupal Developer Days Barcelona
2012.06.16
17. Installing Mahout
• Building
– Follow instructions
– https://cwiki.apache.org/MAHOUT/buildingmaho
ut.html
• Use maven to build examples
Drupal Developer Days Barcelona
2012.06.16
19. Installing Recommender API
• See http://drupal.org/node/1207634
• Configuration
– sites/all/modules/async_command/config.propert
ies should match settings.php
• Download and enable async_command
• Check
/admin/config/search/recommender/admin
Drupal Developer Days Barcelona
2012.06.16
20. Usage
• Making recommendations
– User-user
– User-item
– Item-item
• Predictions/similarity feeds back into Drupal
• Blocks
• Views
Drupal Developer Days Barcelona
2012.06.16
21. Case study: Data Mining and
Recommendations in SARACEN
• SARACEN: http://www.saracen-p2p.eu/
• Feedback loop to measure subjective quality of
the recommendations
– Limited set of data, small user base
– API provides an initial set of recommended videos
– User can then watch a recommended video
– User’s actions are incorporated into their implicit
profile, feeds back to the recommender API
– Recommender API generates new predictions based
on the complete set of implicit profile metadata
Drupal Developer Days Barcelona
2012.06.16
23. Recommender data sources
• Explicit data
– SARACEN account data, including location and language
– Linked accounts and profiles
• e.g. Facebook user profile, “likes”, connections, metadata
• Implicit data
– Activity history recorded during the user’s sessions
– Searches
– Shared content
– Viewed content
– Albums (media containers)
– Content ratings
Drupal Developer Days Barcelona
2012.06.16
24. Scalability
• Don’t need Hadoop if
– Number of users is orders of magnitude larger
than the number of items
– Users browse anonymously most of the time
– Few users log in and need personalised
recommendations
– Item churn rate is relatively low
Drupal Developer Days Barcelona
2012.06.16
25. Worth Considering
• Decreased Transparency
• Decreased Serendipity
• Sleep deprivation
Drupal Developer Days Barcelona
2012.06.16
26. Resources: Recommender API
• http://drupal.org/project/recommender
• http://recommenderapi.com/cloud
• https://cwiki.apache.org/confluence/display/
MAHOUT
Drupal Developer Days Barcelona
2012.06.16
27. Resources: Mahout
• http://mahout.apache.org/
• Mahout in Action
– http://www.manning.com/owen/
– ISBN 9781935182689.
• The Optimality of Naive Bayes, Harry Zhang.
• http://aws.amazon.com/elasticmapreduce/
Drupal Developer Days Barcelona
2012.06.16
28. Acknowledgements
• Socially Aware, collaboRative, scAlable Coding
mEdia distributioN (SARACEN)
– http://www.saracen-p2p.eu
– Funded within the European Union’s Seventh
Framework Programme (FP7/2007-2013) under
grant agreement 248474
Drupal Developer Days Barcelona
2012.06.16
Scalable machine learningkeywords: Recommendations, Personalisation, Big Data, Data Mining, Trend Spotting, Predictions, Clusteringaudience: developers, experimenters - how many have already installed or played with Mahout? Recommender API? Built their own solutions?arch. overview: Drupal + Recommender API + Apache Mahout or cloud service; optionally run Mahout on Hadoop clusterasynchronous, using Mahout (Java) for heavy lifting; was PHP in early Rec. API but PHP sucks for computationally intensive or asynchronous tasks
Scalable machine learningkeywords: Recommendations, Personalisation, Big Data, Data Mining, Trend Spotting, Predictions, Clusteringaudience: developers, experimenters - how many have already installed or played with Mahout? Recommender API? Built their own solutions?arch. overview: Drupal + Recommender API + Apache Mahout or cloud service; optionally run Mahout on Hadoop clusterasynchronous, using Mahout (Java) for heavy lifting; was PHP in early Rec. API but PHP sucks for computationally intensive or asynchronous tasks
AmazonNetflixNetflix PrizeSpotify, PandoraFacebook, LinkedInOKCupidiTunes Genius; app store not so muchmany moreAs Amazon and others have demonstrated, recommenders can have concrete commercial value by enabling smart cross-selling opportunities. One firm reports that recommending products to users can drive an 8 to 12 percent increase in sales.
Recommendation mining: aggregate a user’s behavior and use it to find other items they might likeClustering: take documents and group them by topicClassification: learn from exisitingcategorised documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category.Frequent itemset mining: take a set of item groups (terms in a query session, shopping cart content) and identify which individual items usually appear together
Provides clustering capabilitiesNot trivial to set upSee issue #1206840 re: Recommender API support for Hadoop Mahout actually support Hadoop clusters, so potentially the Recommender API can use Hadoop too for really large computational tasks. However, I’m not sure if Hadoop is really needed because the current implementation is already quite fast.
Http://drupal.org/project/recommender - Drupal 7 (alpha) & 6 (beta)A Java program that uses Apache Mahout to do the recommendation computationThe Java program can run either on the local Drupal server or on a remote computer with better CPU/RAM capacityUses JDBC and Java Persistence API (JPA) to directly access the required Drupal database tables on most JDBC-compliant databasesEarlier version was originally done in PHP but the current design is much more scalableA Drupal module (recommender)So that users can issue commands to the Java program through the Drupal interfaceThen the Java program will pick up those commands and execute accordingly.Drupal integration moodulesAll the nitty-gritty communication between Drupal and the Java program is handled by Recommender APIHelper modules just use Recommender API to calculate the recommendations
A feedback loop can be used to measure subjective quality of the recommendations:API provides an initial set of recommended items based on predictions using a limited set of dataUser is able to watch an item from the set of recommended items, or add them to his boxes for later viewingUser’s actions are incorporated into their implicit profile, feeds back to the recommender APIRecommender API generates new predictions based on the complete set of implicit profile metadata
The output of the classifier models will be fed into the recommender models, but not vice versa, to prevent the creation of feedback loops in the modelling process. The final recommendation and classifier outputs will then be fed back into the implicit data triple store, where they may be relayed to users for predictions and similarity.All the classifiers and recommenders, and the model combiners, will run concurrently and asynchronously, and, if necessary, in parallel on different nodes in the Kendra API environment. This method is preferred to the generation of recommendations and classifications on demand, because the relevant algorithms tend to produce results in batches for multiple users, as opposed to individual results one at a time.
ProcessingRecommendations are computed every 2 minutes during the initial implementation, using the Linux cron daemon.RationaleThis system has been chosen for a number of reasons:The overall multi-model and combiner system represents the state of the art in recommendation systems, and is well proven in other applications with similar problems.In spite of its apparent ad hoc approach, the model-combiner approach is known to be highly robust, and is thus a safe choice for the engineering goals of the projectSince it is impossible to know in advance of actual testing which classifiers will be successful, a model-combiner-based approach provides an objective means to select which algorithms should be used in the final system. Hand tuning is minimised, making results more objective and at the same time reducing project effort.This approach allows work on the project to progress incrementally, with the ability to generate partial results at an early stage in the development process, thereby increasing the probability of a successful project outcome.At the same time, this approach allows Kendra to take a novel research direction in producing a novel recommender algorithm, without detracting from the engineering goal of providing a working recommendation system for the project.The overall framework will then allow the assessment of the effectiveness of this recommender relative to the effectiveness of existing algorithms, in an objective manner.
Deploying a massively scalable recommender system with Apache Mahout focuses on use cases different from SARACEN, but still useful:Use cases for HadoopNumber of users is orders of magnitude larger than the number of itemsUsers browse anonymously most of the timeFew users log in and need personalised recommendationsYour item churn rate is relatively low, items are available for weeks or months and it’s ok to have a waiting time of half a day or more until new items are included in the recommendationsI.e. most e-commerce sites and many video portals.
Decreased transparencyhow are my previous choices influencing what I see?Serendipityrandom recommendations will, by definition, not receive as many clicks, but may add to system’s valueSleep deprivationif you’re in charge of setting up and maintaining a Hadoop cluster