Boosting Data Science in Geochemistry: We Need Global Geochemical Data Standa...Kerstin Lehnert
Presentation at AGU Fall Meeting 2018: Large-scale, global geochemical data syntheses like EarthChem and GEOROC have, for nearly two decades, inspired and made possible a vast range of scientific studies and new discoveries, facilitating the analysis and mining of geochemical data and creating new paradigms in geochemical data analysis such as statistical geochemistry. These syntheses provide easy access to fully integrated compilations of thousands of datasets (‘data fusion’) with millions of geochemical measurements that are accompanied by comprehensive and harmonized metadata for context and provenance to search, filter, sort, and evaluate the data.
The syntheses have been assembled and maintained through manual labor by data managers, who extract data and metadata from text, tables, and supplements of publications for inclusion in the databases, a time-consuming task due to the multitude of data formats, units, normalizations, vocabularies, etc., i.e. lack of best practices for geochemical data reporting. In order to support and advance future science endeavors that rely on access to and analysis of large volumes of geochemical data, we need to develop and implement global standards for geochemical data that not only make geochemical data FAIR (Findable, Accessible, Interoperable, Re-usable), but ready for data fusion. As more geochemical data systems are emerging at national, programmatic, and subdomain levels in response to Open Access policies and science needs, standard protocols for exchanging geochemical data among these systems will need to be developed, implemented, and governed.
Critical is the alignment with existing standards such as the Semantic Sensor Network (SSN) ontology, a recent joint W3C and OGC standard that standardizes description of sensors, observation, sampling, and actuation, with sufficient flexibility to allow details of these elements to be defined in different domains. New initiatives within the International Council for Science and CODATA are working towards coordinating the International Science Unions to identify and endorse the more authoritative standards (including vocabularies and ontologies). These initiatives present a timely opportunity for geochemical data to ensure that they are born ‘connected’ within and across disciplines.
Airbnb aims to democratize data within the company by building a graph database of all internal data resources connected by relationships. This graph is queried through a search interface to help employees explore, discover, and build trust in company data. Challenges include modeling complex data dependencies and proxy nodes, merging graph updates from different sources, and designing a data-dense interface simply. Future goals are to gamify content production, deliver recommendations, certify trusted content, and analyze the information network.
Large Scale Graph Analytics with RDF and LPG Parallel ProcessingCambridge Semantics
Analytics that traverse large portions of large graphs have been problematic for both RDF and LPG graph engines. In this webinar Barry Zane, former co-founder of Netezza, Paraccel and SPARQL City and current VP of Engineering at Cambridge Semantics, discusses the native parallel-computing approach taken in AnzoGraph to yield interactive, scalable performance for RDF and LPG graphs.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
The document discusses developing a Personalized Health Knowledge Graph (PHKG) to support personalized preventative healthcare applications. PHKG integrates medical knowledge and personal health data to provide context-specific and personalized insights. It proposes an architecture with a knowledge graph, rule-based inference engine, and integration of knowledge from ontology catalogs. Challenges include modeling personalization/context, analyzing IoT data, and reusing knowledge from existing health resources. The solution is demonstrated for asthma management using the KHealth dataset and ontologies. Future work includes additional disease cases and dynamic knowledge graph evolution.
TFF2016, Rudi Studer, Smarte Dienstleistungen mit semantischen TechnologienTourismFastForward
This document discusses using semantic technologies to enable smart services in tourism. It describes two use cases: 1) building agile systems through fast integration of heterogeneous data and programmable interfaces using semantic technologies, and 2) collaborative development of business processes through semantic modeling, analysis, and execution of processes. The document outlines challenges with current approaches and how semantic technologies can help address these challenges through linked data, linked services, and semantic descriptions of processes and APIs.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Boosting Data Science in Geochemistry: We Need Global Geochemical Data Standa...Kerstin Lehnert
Presentation at AGU Fall Meeting 2018: Large-scale, global geochemical data syntheses like EarthChem and GEOROC have, for nearly two decades, inspired and made possible a vast range of scientific studies and new discoveries, facilitating the analysis and mining of geochemical data and creating new paradigms in geochemical data analysis such as statistical geochemistry. These syntheses provide easy access to fully integrated compilations of thousands of datasets (‘data fusion’) with millions of geochemical measurements that are accompanied by comprehensive and harmonized metadata for context and provenance to search, filter, sort, and evaluate the data.
The syntheses have been assembled and maintained through manual labor by data managers, who extract data and metadata from text, tables, and supplements of publications for inclusion in the databases, a time-consuming task due to the multitude of data formats, units, normalizations, vocabularies, etc., i.e. lack of best practices for geochemical data reporting. In order to support and advance future science endeavors that rely on access to and analysis of large volumes of geochemical data, we need to develop and implement global standards for geochemical data that not only make geochemical data FAIR (Findable, Accessible, Interoperable, Re-usable), but ready for data fusion. As more geochemical data systems are emerging at national, programmatic, and subdomain levels in response to Open Access policies and science needs, standard protocols for exchanging geochemical data among these systems will need to be developed, implemented, and governed.
Critical is the alignment with existing standards such as the Semantic Sensor Network (SSN) ontology, a recent joint W3C and OGC standard that standardizes description of sensors, observation, sampling, and actuation, with sufficient flexibility to allow details of these elements to be defined in different domains. New initiatives within the International Council for Science and CODATA are working towards coordinating the International Science Unions to identify and endorse the more authoritative standards (including vocabularies and ontologies). These initiatives present a timely opportunity for geochemical data to ensure that they are born ‘connected’ within and across disciplines.
Airbnb aims to democratize data within the company by building a graph database of all internal data resources connected by relationships. This graph is queried through a search interface to help employees explore, discover, and build trust in company data. Challenges include modeling complex data dependencies and proxy nodes, merging graph updates from different sources, and designing a data-dense interface simply. Future goals are to gamify content production, deliver recommendations, certify trusted content, and analyze the information network.
Large Scale Graph Analytics with RDF and LPG Parallel ProcessingCambridge Semantics
Analytics that traverse large portions of large graphs have been problematic for both RDF and LPG graph engines. In this webinar Barry Zane, former co-founder of Netezza, Paraccel and SPARQL City and current VP of Engineering at Cambridge Semantics, discusses the native parallel-computing approach taken in AnzoGraph to yield interactive, scalable performance for RDF and LPG graphs.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
The document discusses developing a Personalized Health Knowledge Graph (PHKG) to support personalized preventative healthcare applications. PHKG integrates medical knowledge and personal health data to provide context-specific and personalized insights. It proposes an architecture with a knowledge graph, rule-based inference engine, and integration of knowledge from ontology catalogs. Challenges include modeling personalization/context, analyzing IoT data, and reusing knowledge from existing health resources. The solution is demonstrated for asthma management using the KHealth dataset and ontologies. Future work includes additional disease cases and dynamic knowledge graph evolution.
TFF2016, Rudi Studer, Smarte Dienstleistungen mit semantischen TechnologienTourismFastForward
This document discusses using semantic technologies to enable smart services in tourism. It describes two use cases: 1) building agile systems through fast integration of heterogeneous data and programmable interfaces using semantic technologies, and 2) collaborative development of business processes through semantic modeling, analysis, and execution of processes. The document outlines challenges with current approaches and how semantic technologies can help address these challenges through linked data, linked services, and semantic descriptions of processes and APIs.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
While Graph Databases have come of age, Data Warehousing seems to be broken in an increasing dynamic world. Are Graph Databases a smarter version of Data Lakes?
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Numerous organizations already discovered Enterprise Linked Data as a powerful solution for a 360-degree view on various business objects. But how do they solve the big challenge of connecting their data pools in heterogeneous and highly dynamic information landscapes?
Learn more about the manifold application scenarios of linked data and semantic technologies. Dive into your data pools to gain new insights and knowledge!
Government GraphSummit: Optimizing the Supply ChainNeo4j
Michael Moore Ph.D., Principal, Partner Solutions and Neo4j Technology, Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
The document discusses the key components and concepts of a National Spatial Data Infrastructure (NSDI). An NSDI aims to integrate distributed geospatial data through partnerships between different levels of government and private organizations. It establishes standards, frameworks and metadata to facilitate discovery and sharing of geospatial data. Central to an NSDI is a clearinghouse that allows users to search metadata from distributed servers according to common protocols. When properly implemented through the coordination of stakeholders, an NSDI can help reduce data duplication, lower costs and make critical spatial information more accessible.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
This document outlines a presentation on the history and future of business intelligence. It begins with introductions and definitions of key concepts like business intelligence, data warehousing, and analytics. It then reviews the early origins of data-driven decision making from the Roman census to Napoleon's Russian campaign. The current landscape section examines major vendors, capabilities, and platforms. The future section speculates on emerging technologies like machine learning, augmented reality, and the integration of IoT data. The presentation aims to both educate attendees and provoke thought about the evolution of the field.
BCS BISSG - Business Intelligence - Past Present and Future 20150624 FINALGary Nuttall MBCS CITP
This document outlines a plan for presenting on the history and future of business intelligence. It begins with introductions and definitions of key terms like business intelligence, data warehousing, analytics, and big data. It then discusses the early origins of data collection and analysis dating back to the Roman census and Napoleon's Russian campaign. The current landscape section describes major vendors, capabilities, and platforms. The future section examines trends, from the hype cycle to possibilities like machine learning, augmented reality, geospatial data, and more. Useful resources are also listed.
Spark at NASA/JPL-(Chris Mattmann, NASA/JPL)Spark Summit
The document discusses NASA's use of Apache Spark for big data analytics. It provides context on Chris Mattmann's involvement with Spark through his roles at NASA JPL and the Apache Software Foundation. It outlines some of NASA's big data challenges around handling large volumes of Earth observation data from instruments and simulations. NASA is interested in using Spark for tasks like data triage, archiving, and knowledge extraction to help address these challenges and enable new scientific insights.
The ARIADNE project aims to integrate archaeological datasets across Europe by overcoming data fragmentation. It involves 24 partners from 17 countries. The project will provide services for resource discovery, integrated access based on geography and time, interoperability across datasets, and visualization of images, videos and 3D objects. Key challenges include differing data traditions and languages, fragmentation of micro-archives, and incorporating new technologies for "big data" in archaeology.
The document describes analyzing clickstream data using Spark clustering algorithms. It discusses parsing raw clickstream data containing user agent strings to extract useful fields like device type, OS, and timezone. It then applies distributed K-modes clustering in Spark to group users into subsets exhibiting similar patterns. The results show 10 clusters with different proportions of country, timezone, device and other fields, revealing distinct user types within the dataset.
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Data Tactics Semantic and Interoperability Summit Feb 12, 2013DataTactics
Data Tactics Corporation held a Semantic and Ontology Interoperability Summit on February 12, 2013. Data Tactics is an open source advocate that provides data architecture, engineering, and management services to defense and intelligence agencies. They have over 200 employees, including data scientists and semantic researchers, and have deployed multiple open source clouds globally for the DNI, NSA, Army, Air Force, and DARPA to help customers gather, correlate, and analyze increasing data sources.
Talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
The first step toward understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many Data Management disciplines inherent in good systems development and is perhaps the most mislabeled and misunderstood of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and can also enable you to combine more sophisticated Data Management techniques in support of larger and more complex business initiatives.In this webinar, we will:Illustrate how to leverage Metadata Management in support of your business strategyDiscuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
Workshop: Introduction to Cytoscape at UT-KBRIN Bioinformatics Summit 2014 (4...Keiichiro Ono
This document summarizes a presentation given by Keiichiro Ono on the open source software platform Cytoscape. Ono introduced Cytoscape as a tool for biological network analysis and visualization. He discussed how it can integrate network and attribute data, perform network analysis functions like filtering and calculating statistics, and visualize networks through customizable layouts and visual styles. Ono also highlighted Cytoscape's ecosystem of apps that extend its functionality and its use of open standards to import a variety of network and attribute data formats.
This document provides an overview of the data science process, including historical notes on related frameworks like KDD, CRISP-DM, and big data. It discusses the typical stages in the knowledge discovery process, including business understanding, data understanding, data preparation, model building, evaluation, and deployment. It also provides an example walking through these stages to predict power failures in Manhattan, covering opportunities assessment, data acquisition and cleaning, model building, policy construction, and evaluation.
The document discusses tools for analyzing dark data and dark matter, including DeepDive and Apache Spark. DeepDive is highlighted as a system that helps extract value from dark data by creating structured data from unstructured sources and integrating it into existing databases. It allows for sophisticated relationships and inferences about entities. Apache Spark is also summarized as providing high-level abstractions for stream processing, graph analytics, and machine learning on big data.
Go Code Colorado held events with over 900 participants across 5 locations. There were 31 teams that participated in the Challenge Weekend and were given $25,000 each for the top 3 teams. 176 datasets were published through the program. Successful open data applications and analytics use data in combination from multiple sources. The role of a data liaison is important to bridge the gap between data providers and end users by having knowledge of both worlds and helping with tasks like data interpretation and metadata. High quality data portals and catalogs have centralized, predictable, discoverable, non-redundant data with good documentation and metadata to help users understand what the data is, what it contains, how often it is updated and how it was created.
Valliappa Lakshmanan says: “Ask someone a question in Google and you are likely to receive a link to a BigQuery view or query rather than the actual answer”. That’s what we are doing at Travelstart! I’ll present our DataOps approach and a way to create a culture of DIY, avoiding the BI bottleneck.
1. The document discusses future directions for software engineering research, including tools to support "citizen scientists" and proposed services for next-generation data repositories.
2. It suggests that data mining tools could provide more services beyond data repositories, such as supporting verification, compression, privacy, and streaming of data.
3. The talk outlines several topics, including software tools for citizen scientists, issues around decision software, and lessons learned regarding certification envelopes, goals, locality, and the need for repair and verification tools.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
This document outlines a presentation on the history and future of business intelligence. It begins with introductions and definitions of key concepts like business intelligence, data warehousing, and analytics. It then reviews the early origins of data-driven decision making from the Roman census to Napoleon's Russian campaign. The current landscape section examines major vendors, capabilities, and platforms. The future section speculates on emerging technologies like machine learning, augmented reality, and the integration of IoT data. The presentation aims to both educate attendees and provoke thought about the evolution of the field.
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This document outlines a plan for presenting on the history and future of business intelligence. It begins with introductions and definitions of key terms like business intelligence, data warehousing, analytics, and big data. It then discusses the early origins of data collection and analysis dating back to the Roman census and Napoleon's Russian campaign. The current landscape section describes major vendors, capabilities, and platforms. The future section examines trends, from the hype cycle to possibilities like machine learning, augmented reality, geospatial data, and more. Useful resources are also listed.
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The document discusses NASA's use of Apache Spark for big data analytics. It provides context on Chris Mattmann's involvement with Spark through his roles at NASA JPL and the Apache Software Foundation. It outlines some of NASA's big data challenges around handling large volumes of Earth observation data from instruments and simulations. NASA is interested in using Spark for tasks like data triage, archiving, and knowledge extraction to help address these challenges and enable new scientific insights.
The ARIADNE project aims to integrate archaeological datasets across Europe by overcoming data fragmentation. It involves 24 partners from 17 countries. The project will provide services for resource discovery, integrated access based on geography and time, interoperability across datasets, and visualization of images, videos and 3D objects. Key challenges include differing data traditions and languages, fragmentation of micro-archives, and incorporating new technologies for "big data" in archaeology.
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¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
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En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
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This document provides an overview of the data science process, including historical notes on related frameworks like KDD, CRISP-DM, and big data. It discusses the typical stages in the knowledge discovery process, including business understanding, data understanding, data preparation, model building, evaluation, and deployment. It also provides an example walking through these stages to predict power failures in Manhattan, covering opportunities assessment, data acquisition and cleaning, model building, policy construction, and evaluation.
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2. It suggests that data mining tools could provide more services beyond data repositories, such as supporting verification, compression, privacy, and streaming of data.
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Metadata and Linked Data. Where is it all going?
1. Metadata and Linked Data.
Where is it all going?
By Nicholas Car for the ANZ MDWG, 2018-06-13
LAND & WATER
Supported by:
2. About me!
Nicholas Car
Senior Experimental Scientist
Environmental Informatics Group
CSIRO Land & Water
Brisbane
• Interested in the totality of Australia’s information
• Formerly at GA
• Now working across agencies, as best I can
• Co-chair of the Aust. Gov. Linked Data WG – linked.data.gov.au
• with Armin here!
Metadata and Linked Data. Where is it all going? | Nicholas Car2 |
3. Outline
• Profile upgrade opportunities
• Emergent graph
Metadata and Linked Data. Where is it all going? | Nicholas Car3 |
4. Outline
• Profile upgrade opportunities
• Emergent graph
Metadata and Linked Data. Where is it all going? | Nicholas Car4 |
5. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community – ANZ
3. Cater for an “emergent graph”
Metadata and Linked Data. Where is it all going? | Nicholas Car5 |
6. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
• Irina will walk you through GA’s requirements
Metadata and Linked Data. Where is it all going? | Nicholas Car6 |
7. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
• Irina will walk you through GA’s requirements
Metadata and Linked Data. Where is it all going? | Nicholas Car7 |
e.g. Metadata entity set information (MD_Metadata):
http://pid.geoscience.gov.au/def/schema/ga/ISO19115-1-2014
8. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community – ANZ
Metadata and Linked Data. Where is it all going? | Nicholas Car8 |
9. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community – ANZ
• Shared expectations
– Certain fields are expected from all participants
Metadata and Linked Data. Where is it all going? | Nicholas Car9 |
10. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community – ANZ
• Shared expectations
– Shared codelists
Our community can use a Profile to indicate particular codelists that we
nominate for community use.
• Particular keywords
• Particular catalogue item types
• Particular roles
• Particular agencies
Metadata and Linked Data. Where is it all going? | Nicholas Car10 |
http://pid.geoscience.gov.au/def/
schema/ga/ISO19115-1-2014
11. Profile upgrade opportunities
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
I will explain…
Metadata and Linked Data. Where is it all going? | Nicholas Car11 |
12. Outline
• Profile upgrade opportunities
• Emergent graph
Metadata and Linked Data. Where is it all going? | Nicholas Car12 |
13. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
We are used to this:
Metadata and Linked Data. Where is it all going? | Nicholas Car13 |
Metadata documents Metadata DB
indexation
14. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
And to some extent this:
Metadata and Linked Data. Where is it all going? | Nicholas Car14 |
Metadata documents Metadata DB
harvesting indexation
15. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
We want this:
Metadata and Linked Data. Where is it all going? | Nicholas Car15 |
Metadata documents Information graph
?
16. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
We want this:
Metadata and Linked Data. Where is it all going? | Nicholas Car16 |
Metadata documents Information graph
?
An information graph:
* better represents the way
we understand information
* If done using Linked Data,
can Join information
at any granularity and across
many systems
17. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
We want this:
Metadata and Linked Data. Where is it all going? | Nicholas Car17 |
Metadata documents Information graph
?
An information graph:
* why?
* The total information
we want is stored in many,
different systems
18. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
Actually this:
Metadata and Linked Data. Where is it all going? | Nicholas Car18 |
Metadata documents
19. Emergent graph
1. Tighten up parts of the standard for particular purposes
2. Implement things for an entire community - ANZ
3. Cater for an “emergent graph”
Actually this:
Metadata and Linked Data. Where is it all going? | Nicholas Car19 |
Metadata documents
how ?
20. Emergent graph - pattern
Metadata and Linked Data. Where is it all going? | Nicholas Car20 |
Research Data Alliance
21. Emergent graph – how, at GA
Metadata and Linked Data. Where is it all going? | Nicholas Car21 |
(but first why: information in multiple places/systems)
22. Metadata and Linked Data. Where is it all going? | Nicholas Car22 |
Pattern 12.2
As implemented in test at GA
23. Emergent graph – how, at GA
Metadata and Linked Data. Where is it all going? | Nicholas Car23 |
http://pid.geoscience.gov.au/def/ont/ga/pdm
GA’s top-level data model relates items within IS19115-1 catalogues and others
25. Emergent graph – how, at GA
Metadata and Linked Data. Where is it all going? | Nicholas Car25 |
http://pid.geoscience.gov.au/def/schema/ga/ISO19115-3-
2016/codelist/assocTypeCode_codelist.html
26. Emergent graph – how, at GA
Metadata and Linked Data. Where is it all going? | Nicholas Car26 |
<mri:associatedResource>
<mri:MD_AssociatedResource>
….
</mri:MD_AssociatedResource>
</mri:associatedResource>
<cit:CI_Citation>
…
</cit:CI_Citation>
<mri:associationType>
<mri:DS_AssociationTypeCode codeList="codeListLocation#
DS_AssociationTypeCode" codeListValue="wasDerivedFrom" />
</mri:associationType>
<cit:CI_OnlineResource>
<cit:linkage>
<gco:CharacterString>
http://pid.geoscience.gov.au/dataset/ga/70908
</gco:CharacterString>
</cit:linkage>
…
Dataset 82033 was derived from Dataset 70908 - wasDerivedFrom
27. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car27 |
http://pid.geoscience.gov.au/def/ont/ga/link
28. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car28 |
29. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car29 |
National
Dataset
FSDF
Product
An Org
30. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car30 |
National
Dataset
FSDF
Product
An Org
wasDerivedFrom
31. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car31 |
Input
Dataset X
National
Dataset
FSDF
Product
FSDF
Activity
wasDerivedFromwasGeneratedBy
Input
Dataset Y
Input
Dataset Z
used
32. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car32 |
Input
Dataset X
National
Dataset
FSDF
Product
FSDF
Activity
wasDerivedFromwasGeneratedBy
Input
Dataset Y
Input
Dataset Z
used
An Org
Other
Org
Other,
Other Org
33. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car33 |
Input
Dataset X
National
Dataset
FSDF
Product
FSDF
Activity
Input
Dataset Y
Input
Dataset Z
Certainty of continuation?
34. Emergent graph – FSDF
Metadata and Linked Data. Where is it all going? | Nicholas Car34 |
Input
Dataset X
National
Dataset
FSDF
Product
FSDF
Activity
Input
Dataset Y
Input
Dataset Z
Mandate
1
Mandate
2
Mandate
3
X
35. Emergent graph – Linked Data
• Use URIs to identify everything
• Datasets, Orgs, Mandates
• Elements within datasets
Metadata and Linked Data. Where is it all going? | Nicholas Car35 |
36. Emergent graph – Linked Data
• Use URIs to identify everything
• Datasets, Orgs, Mandates
• Elements within datasets
• Use the Internet to hop across systems / orgs
• Items in one catalog link to items in another via URIs
Metadata and Linked Data. Where is it all going? | Nicholas Car36 |
37. Emergent graph – Linked Data
• Use URIs to identify everything
• Datasets, Orgs, Mandates
• Elements within datasets
• Use the Internet to hop across systems / orgs
• Items in one catalog link to items in another via URIs
• Use LD mechanics to get different views of things
• Purse ISO19115-1
• ANZLIC Profile
• Profile X
• PROV
Metadata and Linked Data. Where is it all going? | Nicholas Car37 |
46. <http://test.linked.data.gov.au/org/O-000928> a
auorg#NonCorporateCommonwealthEntity ,
org:Organization ;
rdfs:label "Australian Bureau of Statistics" ;
…
auorg:budgetAppropriations "368919000"^^auorg:AustralianDollars ;
auorg:portfolio <http://test.linked.data.gov.au/portfolio/78921> ;
…
owl:seeAlso <http://www.abs.gov.au> ;
vcard:hasStreetAddress <http://gnafld.net/address/GAACT714857871> ;
…
Metadata and Linked Data. Where is it all going? | Nicholas Car46 |