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.
What is GraphDB and how can it help you run a smart data-driven business?
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 SemanticsOntotext
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.
It captures and analyzes large volumes of diverse data while at the same time is able to manage and retrieve each and every connection these data ever get to enter in.
In our latest slides, you will find out why we believe RDF graph databases work wonders with serving information needs and handling the growing amounts of diverse data every organization faces today.
Why and how a graph database can serve you better (and at a lower cost) than a relational database when it comes to representing, storing and querying highly interconnected data
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
What is GraphDB and how can it help you run a smart data-driven business?
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 SemanticsOntotext
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.
It captures and analyzes large volumes of diverse data while at the same time is able to manage and retrieve each and every connection these data ever get to enter in.
In our latest slides, you will find out why we believe RDF graph databases work wonders with serving information needs and handling the growing amounts of diverse data every organization faces today.
Why and how a graph database can serve you better (and at a lower cost) than a relational database when it comes to representing, storing and querying highly interconnected data
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricCambridge Semantics
The world of database management is changing. Cloud adoption is accelerating, offering a path for companies to increase their database capabilities while keeping costs in line. To help IT decision-makers survive and thrive in the cloud era, DBTA hosted this special roundtable webinar.
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
The core idea behind Hadoop is to distribute both the data and user software on individual shards within the cluster. The Bigdata Replay method is drastically different in that it packs user software into batches on a single multicore machine and uses circuit emulation to maximize throughout when bringing data shards for replay. The effect from hotspots, defined as drastically higher access frequency to a small portion of (popular) data, is different in the two platforms. This paper models the difference numerically but in a relative form, which makes it possible to compare the two platforms.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Fireside Chat with Bloor Research: State of the Graph Database Market 2020Cambridge Semantics
Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
Linkurious Enterprise is compatible with Azure Cosmos DB and offers investigation teams a turnkey solution to detect and investigate threats hidden in graph data. In this post, we explain how Linkurious Enterprise connects to Cosmos DB graph database.
Graph-driven Data Integration: Accelerating and Automating Data Delivery for ...Cambridge Semantics
In our webinar "A Data Fabric Market Update with Guest Speaker, VP, Principal Analyst Noel Yuhanna" Ben Szekely, Cambridge Semantics’ Co-founder and SVP of Field Operations, and guest speaker, Noel Yuhanna, VP and Principal Analyst at Forrester and author of the “The Forrester Wave™: Enterprise Data Fabric, Q2 2020”, discuss the state of the Data Fabric Market. These are Ben's slides from that webinar.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
LendingClub RealTime BigData Platform with Oracle GoldenGateRajit Saha
LendingClub RealTime BigData Platform with Oracle GoldenGate BigData Adapter. This was presented at Oracle Open World 2017 at San Francisco.
Speaker :
Rajit Saha
Vengata Guruswami
As Twitch grew, both the amount of data we received and the number of employees interested in the data grew rapidly. In order to continue empowering decision making as we scaled, we turned to using Druid and Imply to provide self service analytics to both our technical and non technical staff allowing them to drill into high level metrics in lieu of reading generated reports.
In this talk, learn how Twitch implemented a common analytics platform for the needs of many different teams supporting hundreds of users, thousands of queries, and ~5 billion events each day. This session will explain our Druid architecture in detail, including:
-The end-to-end architecture deployed on Amazon that includes Kinesis, RDS, S3, Druid, Pivot and Tableau
-How the data is brought together to deliver a unified view of live customer engagement and historical trends
-Operational best practices we learnt scaling Druid
-An example walk through using the platform
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Using Cloud Automation Technologies to Deliver an Enterprise Data FabricCambridge Semantics
The world of database management is changing. Cloud adoption is accelerating, offering a path for companies to increase their database capabilities while keeping costs in line. To help IT decision-makers survive and thrive in the cloud era, DBTA hosted this special roundtable webinar.
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
The core idea behind Hadoop is to distribute both the data and user software on individual shards within the cluster. The Bigdata Replay method is drastically different in that it packs user software into batches on a single multicore machine and uses circuit emulation to maximize throughout when bringing data shards for replay. The effect from hotspots, defined as drastically higher access frequency to a small portion of (popular) data, is different in the two platforms. This paper models the difference numerically but in a relative form, which makes it possible to compare the two platforms.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Fireside Chat with Bloor Research: State of the Graph Database Market 2020Cambridge Semantics
Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
Linkurious Enterprise is compatible with Azure Cosmos DB and offers investigation teams a turnkey solution to detect and investigate threats hidden in graph data. In this post, we explain how Linkurious Enterprise connects to Cosmos DB graph database.
Graph-driven Data Integration: Accelerating and Automating Data Delivery for ...Cambridge Semantics
In our webinar "A Data Fabric Market Update with Guest Speaker, VP, Principal Analyst Noel Yuhanna" Ben Szekely, Cambridge Semantics’ Co-founder and SVP of Field Operations, and guest speaker, Noel Yuhanna, VP and Principal Analyst at Forrester and author of the “The Forrester Wave™: Enterprise Data Fabric, Q2 2020”, discuss the state of the Data Fabric Market. These are Ben's slides from that webinar.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
LendingClub RealTime BigData Platform with Oracle GoldenGateRajit Saha
LendingClub RealTime BigData Platform with Oracle GoldenGate BigData Adapter. This was presented at Oracle Open World 2017 at San Francisco.
Speaker :
Rajit Saha
Vengata Guruswami
As Twitch grew, both the amount of data we received and the number of employees interested in the data grew rapidly. In order to continue empowering decision making as we scaled, we turned to using Druid and Imply to provide self service analytics to both our technical and non technical staff allowing them to drill into high level metrics in lieu of reading generated reports.
In this talk, learn how Twitch implemented a common analytics platform for the needs of many different teams supporting hundreds of users, thousands of queries, and ~5 billion events each day. This session will explain our Druid architecture in detail, including:
-The end-to-end architecture deployed on Amazon that includes Kinesis, RDS, S3, Druid, Pivot and Tableau
-How the data is brought together to deliver a unified view of live customer engagement and historical trends
-Operational best practices we learnt scaling Druid
-An example walk through using the platform
IBM® dashDB™ is a fast, fully managed, cloud data warehouse that utilizes integrated analytics to rapidly deliver answers. dashDB’s unique in-database analytics, R predictive modeling and business intelligence tools free you to analyze your data and get precise insights, quicker. dashDB is simple to get up and running with rapid provisioning in IBM Bluemix™. You can test the solution or start using dashDB for no charge, for up to one gigabyte of data and then just $50 US
per month for 20 gigabytes of data storage. Larger instance sizes with multi-terabyte capacity are available as you grow your data, and as your users require a dedicated environment. Massively Parallel Processing (MPP) enables even faster query speeds as well as larger scale data sets.
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 2013ScaleOut Software
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.
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.
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Denodo
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.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
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.
freeDatamap presentation - data visualization BI & GIS -free datamap
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.
Enabling Fast Data Strategy: What’s new in Denodo Platform 6.0Denodo
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.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
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 RecipesOntotext
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 LinkingOntotext
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 Ontotext
If you are looking for solutions that allow you not only to manage all of your data (structured, semi-structured and unstructured) but to also make the most out of them, using a common language is critical.
Adding Semantic Technology to data integration is the glue that holds together all your enterprise data and their relationships in a meaningful way.
Learn how you can quickly design data processing jobs and integrate massive amounts of data and see what semantic integration can do for your data and your business.
www.ontotext.com
[Webinar] GraphDB Fundamentals: Adding Meaning to Your DataOntotext
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 NewsOntotext
Atanas Kiryakov, Ontotext's CEO, presented at the Data Day Texas 2018 conference, which took place in Austin, TX, USA, on January 27th.
Ontotext's talk was part of the Graph Day Sessions and its focus was 'Cognitive graph analytics on company data and news', aiming to demonstrate the power of Graph Analytics to create links between various datasets and lead to knowledge discovery.
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018Ontotext
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 OnesOntotext
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 DemandOntotext
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 ...Ontotext
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 ProcessOntotext
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 DataOntotext
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 CloudOntotext
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.
Best Practices for Large Scale Text Mining ProcessingOntotext
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 HeritageOntotext
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 ResearchOntotext
Clinical trials, both public and proprietary, hold a huge amount of valuable information. Acquiring knowledge from that information in a cost and time efficient manner is a major industry pain point.
Although information from clinical trials is stored in structured or semi-structured form, it is rarely coded with medical terminologies, which creates a significant level of ambiguity and increases the effort for data preparation for analytical purposes.
Gain Super Powers in Data Science: Relationship Discovery Across Public DataOntotext
What data scientists know better than anybody else is that data relationship is what matters the most. You can’t understand your data if you look at it as pieces in data silos.
In this webinar we’ll showcase how to discover relationships across public data.
Gaining Advantage in e-Learning with Semantic Adaptive TechnologyOntotext
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.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
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?