How to migrate to GraphDB in 10 easy to follow steps
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GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database. GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database. Designed with a view to making your transitioning to GraphDB frictionless and resource-effective, GraphDB Migration Service provides the technical support and expertise you and your team of developers need to build a highly efficient architecture for semantic annotation, indexing and retrieval of digital assets. With GraphDB Migration Services you will: * Optimize the cost of managing the RDF database; * Improve the performance of your system; * Get the maximum value from your semantic solution.
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Learn about GraphDB through the solutions it offers in a simple and easy to understand way. In the slides below we have unpacked GraphDB for you, using as little tech talk as possible.
It Don’t Mean a Thing If It Ain’t Got Semantics
With the tons of bits of data around enterprises and the challenge to turn these data into knowledge, meaning is arguably in the systems of the best database holder.
Turning data pieces into actionable knowledge and data-driven decisions takes a good and reliable database. The RDF database is one such solution.
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In-Hadoop, In-Database and In-Memory Processing for Predictive Analytics
The document discusses using RapidMiner and Hadoop (Radoop) together for predictive analytics. It notes that while Hadoop is useful for large-scale batch analytics, it is slow for smaller datasets and real-time analytics. Radoop allows users of different backgrounds to easily design predictive analytics processes on Hadoop clusters through RapidMiner's visual interface. This facilitates collaboration and empowers business users and analysts to automate decision-making based on predictions. Runtime comparisons show Radoop can outperform Hadoop for smaller datasets depending on factors like algorithms and cluster size.
Data Modeling in SAP Gateway – maximize performance at all levels
Data Modeling in SAP Gateway – maximize performance at all levelsInnovapptive Global Solutions Pvt Ltd.
With SAP Netweaver Gateway becoming the platform to seamlessly connect across several devices, it is imperative that data modelling plays a pivotal role in developing applications. Needless to say, the data model you create consists of the operations you want to perform in runtime, mapped to specie data and attributes. Against this backdrop, this white paper probes into the concepts and functionalities of using Data modelling in SAP Gateway with relevant notes and screen shots, wherever applicable.Real-time analysis using an in-memory data grid - Cloud Expo 2013
ScaleOut technical session at Cloud Expo 2013 in NY. Covers the use of in-memory data grids for real-time analysis of fast-changing data. Includes a financial services example.
Understanding big data testing
Implement Big Data Testing in Order to Successfully Generate Analytics. This Blog is ideal for software testers and anyone else who wants to understand big data testing.
Operational Intelligence Using Hadoop
The document discusses enabling operational intelligence using Hadoop MapReduce on an in-memory data grid (IMDG). Operational intelligence analyzes live data in real-time to provide immediate feedback, unlike traditional batch-oriented business intelligence. The IMDG stores operational data in-memory, runs MapReduce jobs without data movement between nodes to accelerate performance by over 40x, and provides real-time alerts and updates. Examples demonstrate how financial trading, ecommerce, and telecommunications systems benefit from these capabilities.
Rda step by step
The document provides steps to configure Real-time Data Acquisition (RDA) in SAP BI 7.0 to load transactional data from a source system into the SAP BI system every minute. It involves creating a generic data source in the source system, an info package for the data source with the "Initialize Delta Process" option, a data store object, transformation, and data transfer process with type "Real Time Data Acquisition". It also requires setting up a daemon process to trigger the real-time data loading through the info package on a scheduled interval.
Data sevice architecture
SAP BusinessObjects Data Services is a data integration platform that includes standard components such as the Designer, Repository, Job Server, Engine, and Access Server. It also includes optional components and management tools. The software has a distributed architecture that allows components to be installed across an organization's network and hardware infrastructure.
November 2013 HUG: Real-time analytics with in-memory grid
This document discusses enabling real-time analytics using Hadoop MapReduce on an in-memory data grid (IMDG). It describes implementing MapReduce using parallel method invocation on an IMDG to eliminate batch scheduling overhead and analyze live data. Sample use cases are presented for applications in financial services, ecommerce, and other industries that require real-time analysis of large, changing datasets.
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Watch full webinar here: https://bit.ly/32TT2Uu
Data virtualization is not just for self-service, it’s also a first-class citizen when it comes to modern data platform architectures. Technology has forced many businesses to rethink their delivery models. Startups emerged, leveraging the internet and mobile technology to better meet customer needs (like Amazon and Lyft), disrupting entire categories of business, and grew to dominate their categories.
Schedule a complimentary Data Virtualization Discovery Session with g2o.
Traditional companies are still struggling to meet rising customer expectations. During this webinar with the experts from g2o and Denodo we covered the following:
- How modern data platforms enable businesses to address these new customer expectation
- How you can drive value from your investment in a data platform now
- How you can use data virtualization to enable multi-cloud strategies
Leveraging the strategy insights of g2o and the power of the Denodo platform, companies do not need to undergo the costly removal and replacement of legacy systems to modernize their systems. g2o and Denodo can provide a strategy to create a modern data architecture within a company’s existing infrastructure.
Skilwise Big data
IBM's Big Data platform provides tools for managing and analyzing large volumes of structured, unstructured, and streaming data. It includes Hadoop for storage and processing, InfoSphere Streams for real-time streaming analytics, InfoSphere BigInsights for analytics on data at rest, and PureData System for Analytics (formerly Netezza) for high performance data warehousing. The platform enables businesses to gain insights from all available data to capitalize on information resources and make data-driven decisions.
Big Data Tools: A Deep Dive into Essential Tools
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
Skillwise Big Data part 2
IBM's Big Data platform provides tools for managing and analyzing large volumes of data from various sources. It allows users to cost effectively store and process structured, unstructured, and streaming data. The platform includes products like Hadoop for storage, MapReduce for processing large datasets, and InfoSphere Streams for analyzing real-time streaming data. Business users can start with critical needs and expand their use of big data over time by leveraging different products within the IBM Big Data platform.
freeDatamap presentation - data visualization BI & GIS -
Mind Mapping + Business Intelligence = freeDatamap.
Unchain your data with the lightest and most intuitive self-service BI platform. Try a new data browsing experience thanks to a holistic and organization-wide dashboard to understand all the key aspects of your business in a unified data map.
With freeDatamap, access the right data, share the knowledge, break silos, help data to go “social”, make data available and collectively enriched.
• Find your way in an overwhelming amount of information.
• Visualize your data in a centralized trusted map.
• Display your business process across your organization.
• Navigate into the map and drill down to find the root cause of an indicator.
• Find any atomic data thanks to a powerful and immediate search engine.
• Reduce time to make fact based decisions.
Concept to production Nationwide Insurance BigInsights Journey with Telematics
This document summarizes Nationwide Insurance's use of IBM BigInsights to process telematics data from their SmartRide program. It discusses the architecture used, which included 6 management nodes and 16 data nodes of IBM BigInsights. It also describes the various phases of data processing, including acquiring raw trip files from HDFS, standardizing the data, scrubbing and calculating events, and summarizing the data for loading into HBase. Key benefits included improving processing performance and enabling customers to access insights about their driving through a web portal.
Enabling Fast Data Strategy: What’s new in Denodo Platform 6.0
In this presentation, you will see the new functionalities of the Denodo 6.0 detailing dynamic query optimization engine, managing enterprise deployments, and using information self-service for discovery and search.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DzRtkg.
How is sap data services unique for sap hana integration
SAP Data Services is uniquely suited for SAP HANA integration in the following ways:
1. The DS Workbench provides a visual environment that improves developer productivity for migrating data into HANA from various sources.
2. Metadata from HANA views and tables can be published to SAP Information Steward to perform data lineage and impact analyses.
3. Data transformations can be pushed down to leverage HANA attribute, analytical, and calculation views for faster and more efficient processes.
Danny Bickson - Python based predictive analytics with GraphLab Create
Dato is presenting on their machine learning platform GraphLab Create. Key points include:
- GraphLab Create allows users to build intelligent applications using machine learning across different data types like images, text, graphs and tables.
- It provides tools for data ingestion, transformation, model building, and deployment in a machine learning pipeline.
- Some benefits of GraphLab Create are its efficient storage, ability to handle large datasets that exceed RAM size, and support for multi-core processing. It also has additional algorithms and automatic feature expansion compared to sklearn.
- Example intelligent applications that can be built include recommenders, fraud detection, ad targeting, personalized medicine, and more.
Similar a How to migrate to GraphDB in 10 easy to follow steps (20)
Artur Borycki - Beyond Lambda - how to get from logical to physical - code.ta...
Artur Borycki - Beyond Lambda - how to get from logical to physical - code.ta...
In-Hadoop, In-Database and In-Memory Processing for Predictive Analytics
In-Hadoop, In-Database and In-Memory Processing for Predictive Analytics
Data Modeling in SAP Gateway – maximize performance at all levels
Data Modeling in SAP Gateway – maximize performance at all levels
Real-time analysis using an in-memory data grid - Cloud Expo 2013
Real-time analysis using an in-memory data grid - Cloud Expo 2013
November 2013 HUG: Real-time analytics with in-memory grid
November 2013 HUG: Real-time analytics with in-memory grid
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...
freeDatamap presentation - data visualization BI & GIS -
freeDatamap presentation - data visualization BI & GIS -
Concept to production Nationwide Insurance BigInsights Journey with Telematics
Concept to production Nationwide Insurance BigInsights Journey with Telematics
Enabling Fast Data Strategy: What’s new in Denodo Platform 6.0
Enabling Fast Data Strategy: What’s new in Denodo Platform 6.0
How is sap data services unique for sap hana integration
How is sap data services unique for sap hana integration
Danny Bickson - Python based predictive analytics with GraphLab Create
Danny Bickson - Python based predictive analytics with GraphLab Create
Más de Ontotext
Property graph vs. RDF Triplestore comparison in 2020
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
A presentation of Ontotext’s CEO Atanas Kiryakov, given during Semantics 2018 - an annual conference that brings together researchers and professionals from all over the world to share knowledge and expertise on semantic computing.
The Bounties of Semantic Data Integration for the Enterprise
Semantic data integration allows enterprises to connect heterogeneous data sources through a common language. This creates a unified 360-degree view of enterprise data and facilitates knowledge management and use. Semantic integration aims to enrich existing data with external knowledge and provide a single access point for enterprise assets. It addresses challenges of accessing and storing data from various internal resources by building a well-structured integrated whole to enhance business processes.
[Webinar] GraphDB Fundamentals: Adding Meaning to Your Data
In this webinar, Desislava Hristova demonstrated how to install and set-up GraphDB™ and how one can generate RDF dataset. She also showed how one can quickly integrate complex and highly interconnected data using RDF, how to write some simple SPARQL queries and more.
In a nutshell, this webinar is suitable for those who are new to RDF databases and would like to learn how they can smartly manage their data assets with GraphDB™.
[Conference] Cognitive Graph Analytics on Company Data and News
Ontotext introduced their cognitive analytics platform that performs cognitive graph analytics on company data and news. The platform builds large knowledge graphs by integrating data from multiple sources and uses text mining to link news articles to entities in the knowledge graph. It provides functionality for node ranking, similarity analysis and data cleaning to consolidate and reconcile company records across datasets. The platform was demonstrated through a knowledge graph containing over 2 billion facts built by integrating datasets like DBpedia, Geonames, and news article metadata.
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018
These are slides from a live webinar taken place January 2018.
GraphDB™ Fundamentals builds the basis for working with graph databases that utilize the W3C standards, and particularly GraphDB™. In this webinar, we demonstrated how to install and set-up GraphDB™ 8.4 and how you can generate your first RDF dataset. We also showed how to quickly integrate complex and highly interconnected data using RDF and SPARQL and much more.
With the help of GraphDB™, you can start smartly managing your data assets, visually represent your data model and get insights from them.
Hercule: Journalist Platform to Find Breaking News and Fight Fake Ones
Hercule: a platform to help journalists detect emerging news topics, check their veracity, track an event as it unfolds and find the various angles in a story as it develops.
GraphDB Cloud: Enterprise Ready RDF Database on Demand
GraphDB Cloud is an enterprise grade RDF graph database providing high-performance querying over large volumes of RDF data. On this webinar, Ontotext demonstrates how to instantly create and deploy a fully managed Graph Database, then import & query data with the (OpenRDF) GraphDB Workbench, and finally explore and visualize data with the build in visualization tools.
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...
This webinar continues series are demonstrating how linked open data and semantic tagging of news can be used for comprehensive media monitoring, market and business intelligence. The platform for the demonstrations is FactForge: a hub for news and data about people, organizations, and locations (POL). FactForge embodies a big knowledge graph (BKG) of more than 1 billion facts that allows various analytical queries, including tracing suspicious patterns of company control; media monitoring of people, including companies owned by them, their subsidiaries, etc.
Efficient Practices for Large Scale Text Mining Process
Text mining is a need when managing large scale textual collections. It facilitates access to, otherwise, hard to organise unstructured and heterogeneous documents, allows for extraction of hidden knowledge and opens new dimensions in data exploration.
In this webinar, Ivelina Nikolova, PhD, shares best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.
The Power of Semantic Technologies to Explore Linked Open Data
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
The Knowledge Discovery Quest
How semantic search is transforming information retrieval and changing the way we access, manage and use data for the better.
Best Practices for Large Scale Text Mining Processing
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
Build Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Many issues are faced by scholars, book researchers, museum directors who try to find the underlying connection between resources. Scholars in particular continuously emphasizes the role of digital humanities and the value of linked data in cultural heritage information systems.
Semantic Data Normalization For Efficient Clinical Trial Research
This document discusses semantic data normalization of clinical trial data to make it more structured and amenable to analysis. It describes converting unstructured clinical data like conditions, interventions, adverse events and eligibility criteria into RDF triples. The goal is to extract key phrases and concepts, identify qualifiers and relationships to formally represent the data. Examples show how condition texts, drug annotations and criteria can be modeled. Current work has normalized over 215,000 clinical studies from ClinicalTrials.gov into over 80 million RDF triples. The normalized data is pre-loaded in GraphDB and Ontotext S4 Cloud and can be explored and analyzed more easily.
Gain Super Powers in Data Science: Relationship Discovery Across Public Data
The document summarizes a webinar on relationship discovery across public data. It outlines the webinar agenda which includes use cases of relation discovery and media monitoring. It also describes examples of relationship discovery from datasets like the Panama Papers and media monitoring examples. It discusses linking news to knowledge graphs and semantic media monitoring. Finally, it covers mapping additional datasets to DBPedia to facilitate relationship discovery.
Gaining Advantage in e-Learning with Semantic Adaptive Technology
In this presentation, we will introduce you to a solution that involves adaptive semantic technology for educational institutions and e-learning providers. You will learn how to integrate 3rd party resources, legacy assets, and other content sources to create the so-called knowledge graph of all structured and unstructured data.
Cooking up the Semantic Web
How is the Semantic Web vision unfolding and what does it take for the Web to fully reach its potential and evolve from a Web of Documents to a Web of Data through universal data representation standards.
Más de Ontotext (20)
Property graph vs. RDF Triplestore comparison in 2020
Property graph vs. RDF Triplestore comparison in 2020
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking
The Bounties of Semantic Data Integration for the Enterprise
The Bounties of Semantic Data Integration for the Enterprise
[Webinar] GraphDB Fundamentals: Adding Meaning to Your Data
[Webinar] GraphDB Fundamentals: Adding Meaning to Your Data
[Conference] Cognitive Graph Analytics on Company Data and News
[Conference] Cognitive Graph Analytics on Company Data and News
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018
Hercule: Journalist Platform to Find Breaking News and Fight Fake Ones
Hercule: Journalist Platform to Find Breaking News and Fight Fake Ones
GraphDB Cloud: Enterprise Ready RDF Database on Demand
GraphDB Cloud: Enterprise Ready RDF Database on Demand
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...
Efficient Practices for Large Scale Text Mining Process
Efficient Practices for Large Scale Text Mining Process
The Power of Semantic Technologies to Explore Linked Open Data
The Power of Semantic Technologies to Explore Linked Open Data
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
Best Practices for Large Scale Text Mining Processing
Best Practices for Large Scale Text Mining Processing
Build Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Build Narratives, Connect Artifacts: Linked Open Data for Cultural Heritage
Semantic Data Normalization For Efficient Clinical Trial Research
Semantic Data Normalization For Efficient Clinical Trial Research
Gain Super Powers in Data Science: Relationship Discovery Across Public Data
Gain Super Powers in Data Science: Relationship Discovery Across Public Data
Gaining Advantage in e-Learning with Semantic Adaptive Technology
Gaining Advantage in e-Learning with Semantic Adaptive Technology
Último
HCL Notes and Domino License Cost Reduction in the World of DLAU
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
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Generating privacy-protected synthetic data using Secludy and Milvus
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.
Project Management Semester Long Project - Acuity
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
National Security Agency - NSA mobile device best practices
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.
Taking AI to the Next Level in Manufacturing.pdf
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.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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How to migrate to GraphDB in 10 easy to follow steps
- 1. How to Migrate to GraphDB in 10 easy to follow steps
- 2. 1 Getting an integrated view of enterprise data is paramount to taking the right action in our data-driven world.
- 3. M a k i n g s e n s e o f t e x t a n d d a t a 2 And this is exactly what GraphDB is built for.
- 4. 3 It’s purpose is to help organizations “know” more and do more with data.
- 5. Developed with the view to handle data only once, GraphDB is a future-proof system for managing your data. 4
- 6. Legacy data, master data, open data, third-party data, no matter what type, GraphDB offers a single entry point to all this knowledge, unleashing the power of data integration. 5
- 7. 6 It operates like a smart brain on top of legacy systems, capable of bringing meaning to your data from diverse external datasets. Getting this smart brain for your enterprise data is now easier.
- 8. 7 It takes only 10 steps
- 9. 8 Welcome to our GraphDB Migration Service! It’s an A-Z service that will help you prepare for migrating your data to GraphDB, walking you through the setup, and monitoring the performance of your systems. [Instituting GraphDB as your new RDF database technology is simple, even if you already have other RDF database in place.]
- 10. 9 Step 1: Validate Data Modeling We will help you analyze your current data modeling and validate if it is optimal.
- 11. 10 Step 2: Profile Slow Queries This is where we will analyze slow running client queries for potential optimizations.
- 12. We will export your data from the previous database and prepare it for import in GraphDB. 11 Step 3: Export Data
- 13. 12 In this step of the migration process, you will be selecting the most suitable infrastructure for optimal performance and then installing GraphDB on an infrastructure of choice. Step 5: Optimize Setup Step 4: Setup GraphDB Here, GraphDB setup will be optimized for the expected client use.
- 14. Step 6: Import Data 13 The data that have been exported from your earlier database will now be imported into the new GraphDB instance.
- 15. Step 7: Analyze “Slow Queries” 14 The previously slow running queries (detected in Step 2) will be analyzed again on the new GraphDB instance.
- 16. Step 8: Optimize Queries for Performance 15 The slow running queries will be rewritten so that you can take advantage of the strengths of GraphDB’s query optimizer.
- 17. Step 9: Monitor Repository Performance for a Month 15 This is where GraphDB’s performance will be closely monitored and the product will be tweaked for optimal performance with the new setup and potential new user behavior and workflow.
- 18. Step 10: Free Support for a Month 16 In this final stage of the migration process, we will be tackling all issues and incidents for a month to make sure the setup is stable and performant.
- 19. 17 Take the first step today! Call us and tell us about your specific business case so that we can assist you in solving your data challenges. ontotext.com/migration-service-processes/
- 20. www.ontotext.com You can also reach us via email at info@ontotext.com and directly by calling 1-866-972-6686 (North America), or +359 2 974 61 60 (Europe) Ready to know more and do more with data?