This document discusses Hortonworks Data Platform (HDP) updates and releases. It notes that HDP will have more frequent releases of components like Spark, Hive, and Ambari, while having longer release cycles for core Hadoop components. HDP 2.5 is highlighted as including interactive Hive queries using LLAP, enterprise Spark support in Zeppelin notebooks, real-time applications support in Storm and HBase/Phoenix, streamlined operations using Ambari, and dynamic security with Atlas and Ranger integration.
Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.
Supporting Financial Services with a More Flexible Approach to Big DataHortonworks
Financial services companies can reap tremendous benefits from 'Big Data' and they have moved quickly to deploy it. But these companies also place heavy demands on 'Big Data' infrastructure for flexibility, reliability and performance. In this webinar, Hortonworks joins WANDisco to look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn:
How to leverage data from across an entire global enterprise
How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions
What industry leaders have put in place
Hortonworks Data In Motion Series Part 4Hortonworks
How real-world enterprises leverage Hortonworks DataFlow/Apache NiFi to to create real-time data flows in record time to enable new business opportunities, improve customer retention, accelerate big data projects from months to minutes through increased efficiency and reduced costs.
On-Demand webinar: http://hortonworks.com/webinar/paradigm-shift-business-usual-real-time-dataflows-record-time/
With its large install base in production, the Storm 1.x line has proven itself as a stable and reliable workhorse that scales well horizontally. Much has been learnt from evolving the 1.x line that we can now leverage to build the next generation execution engine. Under the STORM-2284 umbrella, we are working hard to bring you this new engine which is being redesigned at a fundamental level for Storm 2.0. The goal is to dramatically improve performance and enhance Storm's abilities without breaking compatibility.
This improved vertical scaling will help meet the needs of the growing user base by delivering more performance with less hardware.
In this talk, we will take an in-depth look at the existing and proposed designs for Storm's threading model and the messaging subsystem. We will also do a quick run-down of the major proposed improvements and share some early results from the work in progress.
Speaker
Roshan Naik, Senior MTS, Hortonworks
Discover HDP 2.2: Apache Falcon for Hadoop Data GovernanceHortonworks
Hortonworks Data Platform 2.2 includes Apache Falcon for Hadoop data governance. In this 30-minute webinar, we discussed why the enterprise needs Falcon for governance, and demonstrated data pipeline construction, policies for data retention and management with Ambari. We also discussed new innovations including: integration of user authentication, data lineage, an improved interface for pipeline management, and the new Falcon capability to establish an automated policy for cloud backup to Microsoft Azure or Amazon S3.
Double Your Hadoop Hardware Performance with SmartSenseHortonworks
Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations.
View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/
Enabling the Real Time Analytical EnterpriseHortonworks
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
Introduction: This workshop will provide a hands-on introduction to Apache Spark using the HDP Sandbox on students’ personal machines.
Format: A short introductory lecture about Apache Spark components used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache Spark. This lab will use the following Spark and Apache Hadoop components: Spark, Spark SQL, Apache Hadoop HDFS, Apache Hadoop YARN, Apache ORC, and Apache Ambari User Views. You will learn how to move data into HDFS using Spark APIs, create Apache Hive tables, explore the data with Spark and Spark SQL, transform the data and then issue some SQL queries.
Pre-requisites: Registrants must bring a laptop that can run the Hortonworks Data Cloud.
Speaker:
Robert Hryniewicz, Developer Advocate, Hortonworks
Webinar Series Part 5 New Features of HDF 5Hortonworks
Overview of the newest features of Hortonworks DataFlow highlighting the new processors, new user interface, edge intelligence powered by Apache MiNiFi and new support for multi-tenancy and new zero master clustering architecture
In 2012, we released Hortonworks Data Platform powered by Apache Hadoop and established partnerships with major enterprise software vendors including Microsoft and Teradata that are making enterprise ready Hadoop easier and faster to consume. As we start 2013, we invite you to join us for this live webinar where Shaun Connolly, VP of Strategy at Hortonworks, will cover the highlights of 2012 and the road ahead in 2013 for Hortonworks and Apache Hadoop.
Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks
This is Mark Ledbetter's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?” Mark is vice president for industry solutions at Hortonworks. He has more than twenty-five years experience in the software industry with a focus on Retail and supply chain.
Discover HDP 2.1: Apache Falcon for Data Governance in HadoopHortonworks
Beginning with HDP 2.1, Hortonworks Data Platform ships with Apache Falcon for Hadoop data governance. Himanshu Bari, Hortonworks senior product manager, and Venkatesh Seetharam, Hortonworks co-founder and committer to Apache Falcon, lead this 30-minute webinar, including:
+ Why you need Apache Falcon
+ Key new Falcon features
+ Demo: Defining data pipelines with replication; policies for retention and late data arrival; managing Falcon server with Ambari
Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.
Supporting Financial Services with a More Flexible Approach to Big DataHortonworks
Financial services companies can reap tremendous benefits from 'Big Data' and they have moved quickly to deploy it. But these companies also place heavy demands on 'Big Data' infrastructure for flexibility, reliability and performance. In this webinar, Hortonworks joins WANDisco to look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn:
How to leverage data from across an entire global enterprise
How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions
What industry leaders have put in place
Hortonworks Data In Motion Series Part 4Hortonworks
How real-world enterprises leverage Hortonworks DataFlow/Apache NiFi to to create real-time data flows in record time to enable new business opportunities, improve customer retention, accelerate big data projects from months to minutes through increased efficiency and reduced costs.
On-Demand webinar: http://hortonworks.com/webinar/paradigm-shift-business-usual-real-time-dataflows-record-time/
With its large install base in production, the Storm 1.x line has proven itself as a stable and reliable workhorse that scales well horizontally. Much has been learnt from evolving the 1.x line that we can now leverage to build the next generation execution engine. Under the STORM-2284 umbrella, we are working hard to bring you this new engine which is being redesigned at a fundamental level for Storm 2.0. The goal is to dramatically improve performance and enhance Storm's abilities without breaking compatibility.
This improved vertical scaling will help meet the needs of the growing user base by delivering more performance with less hardware.
In this talk, we will take an in-depth look at the existing and proposed designs for Storm's threading model and the messaging subsystem. We will also do a quick run-down of the major proposed improvements and share some early results from the work in progress.
Speaker
Roshan Naik, Senior MTS, Hortonworks
Discover HDP 2.2: Apache Falcon for Hadoop Data GovernanceHortonworks
Hortonworks Data Platform 2.2 includes Apache Falcon for Hadoop data governance. In this 30-minute webinar, we discussed why the enterprise needs Falcon for governance, and demonstrated data pipeline construction, policies for data retention and management with Ambari. We also discussed new innovations including: integration of user authentication, data lineage, an improved interface for pipeline management, and the new Falcon capability to establish an automated policy for cloud backup to Microsoft Azure or Amazon S3.
Double Your Hadoop Hardware Performance with SmartSenseHortonworks
Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations.
View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/
Enabling the Real Time Analytical EnterpriseHortonworks
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
Introduction: This workshop will provide a hands-on introduction to Apache Spark using the HDP Sandbox on students’ personal machines.
Format: A short introductory lecture about Apache Spark components used in the lab followed by a demo, lab exercises and a Q&A session. The lecture will be followed by lab time to work through the lab exercises and ask questions.
Objective: To provide a quick and short hands-on introduction to Apache Spark. This lab will use the following Spark and Apache Hadoop components: Spark, Spark SQL, Apache Hadoop HDFS, Apache Hadoop YARN, Apache ORC, and Apache Ambari User Views. You will learn how to move data into HDFS using Spark APIs, create Apache Hive tables, explore the data with Spark and Spark SQL, transform the data and then issue some SQL queries.
Pre-requisites: Registrants must bring a laptop that can run the Hortonworks Data Cloud.
Speaker:
Robert Hryniewicz, Developer Advocate, Hortonworks
Webinar Series Part 5 New Features of HDF 5Hortonworks
Overview of the newest features of Hortonworks DataFlow highlighting the new processors, new user interface, edge intelligence powered by Apache MiNiFi and new support for multi-tenancy and new zero master clustering architecture
In 2012, we released Hortonworks Data Platform powered by Apache Hadoop and established partnerships with major enterprise software vendors including Microsoft and Teradata that are making enterprise ready Hadoop easier and faster to consume. As we start 2013, we invite you to join us for this live webinar where Shaun Connolly, VP of Strategy at Hortonworks, will cover the highlights of 2012 and the road ahead in 2013 for Hortonworks and Apache Hadoop.
Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks
This is Mark Ledbetter's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?” Mark is vice president for industry solutions at Hortonworks. He has more than twenty-five years experience in the software industry with a focus on Retail and supply chain.
Discover HDP 2.1: Apache Falcon for Data Governance in HadoopHortonworks
Beginning with HDP 2.1, Hortonworks Data Platform ships with Apache Falcon for Hadoop data governance. Himanshu Bari, Hortonworks senior product manager, and Venkatesh Seetharam, Hortonworks co-founder and committer to Apache Falcon, lead this 30-minute webinar, including:
+ Why you need Apache Falcon
+ Key new Falcon features
+ Demo: Defining data pipelines with replication; policies for retention and late data arrival; managing Falcon server with Ambari
Cloud deployments of Apache Hadoop are becoming more commonplace. Yet Hadoop and it's applications don't integrate that well —something which starts right down at the file IO operations. This talk looks at how to make use of cloud object stores in Hadoop applications, including Hive and Spark. It will go from the foundational "what's an object store?" to the practical "what should I avoid" and the timely "what's new in Hadoop?" — the latter covering the improved S3 support in Hadoop 2.8+. I'll explore the details of benchmarking and improving object store IO in Hive and Spark, showing what developers can do in order to gain performance improvements in their own code —and equally, what they must avoid. Finally, I'll look at ongoing work, especially "S3Guard" and what its fast and consistent file metadata operations promise.
Fast Spark Access To Your Complex Data - Avro, JSON, ORC, and ParquetOwen O'Malley
The landscape for storing your big data is quite complex, with several competing formats and different implementations of each format. Understanding your use of the data is critical for picking the format. Depending on your use case, the different formats perform very differently. Although you can use a hammer to drive a screw, it isn’t fast or easy to do so.
The use cases that we’ve examined are:
reading all of the columns
reading a few of the columns
filtering using a filter predicate
While previous work has compared the size and speed from Hive, this presentation will present benchmarks from Spark including the new work that radically improves the performance of Spark on ORC. This presentation will also include tips and suggestions to optimize the performance of your application while reading and writing the data.
Introduction to Hadoop, Hive, Spark, HDFS, NiFi, Zeppelin, Ambari and other Hadoop / Apache Big Data Tools. With a focus on pure open source and HDP 2.6 from Hortonworks.
Keynote slides from Big Data Spain Nov 2016. Has some thoughts on how Hadoop ecosystem is growing and changing to support the enterprise, including Hive, Spark, NiFi, security and governance, streaming, and the cloud.
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3DataWorks Summit
Deep learning is useful for enterprises tasks in the field of speech recognition, image classification, AI chatbots, and machine translation, just to name a few.
In order to train deep learning/machine learning models, applications such as TensorFlow, MXNet, Caffe, and XGBoost can be leveraged. And sometimes these applications will be used together to solve different problems.
To make distributed deep learning/machine learning applications easily launched, managed, and monitored, we introduced, in Apache Hadoop 3.x, YARN native services along with other improvements such as first-class GPU support, container-DNS support, scheduling improvements, etc. These improvements make distributed deep learning/machine learning applications run on YARN as simple as running it locally, which can let machine learning engineers focus on algorithms instead of worrying about underlying infrastructure. Also, YARN can better manage a shared cluster which runs deep learning/machine learning and other services and ETL jobs with these improvements.
In this session, we will take a closer look at these improvements and show how to run these applications on YARN with demos. Audiences can start trying running these applications on YARN after this talk.
Speakers
Wanga Tan, Staff Software Engineer, Hortonworks
Sunil Govindan, Staff Engineer, Hortonworks
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first-class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speakers
Wangda Tan, Staff Software Engineer, Hortonworks
Billie Rinaldi, Principal Software Engineer I, Hortonworks
In this session we'll be looking at a number of different organisations who are on their big data cybersecurity journey with Apache Metron, we will take a look at the different usecases they are investigating, the data sources they used, the analytics they performed and in some cases the results they were able to find.
We'll also spend some time talking about the common themes in these projects, there are some common approaches to using Apache Metron as a phased project in a project, we'll review some of the common pitfalls and give some concrete suggestions about the things you should (and shouldn't) do when you're getting started.
Finally we'll try and tackle some of the key FAQ's that come up when people are first investigating the potential usage of Apache Metron in the real world based on over a year of interacting with customers and prospects as they look deeper into Apache Metron to see how it fits in to their cybersecurity portfolio.
Speaker
Dave Russell, Principal Solutions Engineer, Hortonworks
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
Fast Access to Your Data - Avro, JSON, ORC, and ParquetOwen O'Malley
The landscape for storing your big data is quite complex, with several competing formats and different implementations of each format. Understanding your use of the data is critical for picking the format. Depending on your use case, the different formats perform very differently. Although you can use a hammer to drive a screw, it isn’t fast or easy to do so.
The use cases that we’ve examined are:
* reading all of the columns
* reading a few of the columns
* filtering using a filter predicate
While previous work has compared the size and speed from Hive, this presentation will present benchmarks from Spark including the new work that radically improves the performance of Spark on ORC. This presentation will also include tips and suggestions to optimize the performance of your application while reading and writing the data.
Finally, the value of having open source benchmarks that are available to all interested parties is hugely important and all of the code is available from Apache.
今回のウェビナーでは、Hadoop1.xからみなさまに深く親しまれてきたApache Hiveが昨今、どのような形で高速化されてきたかについて話します。MapReduceからTezに変わった実行エンジン、インデックスを持ったカラムナーファイルフォーマットであるORC、モダンなCPUを最大限に活用するVectorization、Apache Calciteを利用したCost Based Optimizerによる実行計画の最適化、そして1秒以下のクエリレスポンスを実現するLLAPについて説明します。いずれの機能も数行の設定やコマンドで活用可能なものばかりですが、今回はそれらの背景でどんな仕組みが動いているのか、どんな仕組みで実現されているのかということについて話します。
The story about how to figure out what to measure, and how you can benchmark that. This slide deck tells the idea of benchmarking and does not tell actual commercial/open source benchmark tools.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.