Using semantics to analyze body camera video could help law enforcement agencies better manage the large amounts of video data. The semantic analysis can extract text, metadata and identify key ideas and words to allow agencies to more easily search and query large volumes of video. However, fully realizing this solution will require integrating multiple systems for video storage, semantic analysis, and query capabilities to address challenges such as privacy regulations and budget constraints across different jurisdictions.
Presenter:
K. K. Mookhey, PCI QSA, CISA, CISSP, CISM, CRISC
Founder & Director
Network Intelligence (I) Pvt. Ltd.
Institute of Information Security
Analytics
Mobility
Social Media
Cloud
In the age of Big Data, filtering mechanisms have to professionalized to increase accessibility to data. This presentation, held at Knowledge Management Academy in Vienna, shows how technologies derived from the Semantic Web can help to establish more efficient means to manage data and information.
Knowledge organization systems like taxonomies or thesauri can benefit from linked data approaches and vice versa. In recent years SKOS became very popular in various industries due to its simplicity, not only for information retrieval purposes but also for knowledge modelling itself. For many organizations SKOS turned out to be the entry point to the Semantic Web.
A rather novel approach is to use SKOS as means for data integration. In combination with RDF mapping and linked data alignment technologies complex knowledge bases can be built to use them for the following application scenarios which we will demonstrate by using the PoolParty platform.
• How the creation of thesauri can become more efficient when built upon existing linked data sources
• how SKOS thesauri can be aligned with LOD sources and can be published as an LOD source itself
• How linked data mechanisms can be used to improve decentralized vocabulary management
• How SKOS and linked data alignment can be used for efficient schema mapping and value mapping
• How SKOS thesauri can be enriched with linked data to realise semantic search engines in a very efficient way
• How collaborative platforms like Sharepoint or Confluence can benefit from SKOS based knowledge models in combination with linked data
Presenter:
K. K. Mookhey, PCI QSA, CISA, CISSP, CISM, CRISC
Founder & Director
Network Intelligence (I) Pvt. Ltd.
Institute of Information Security
Analytics
Mobility
Social Media
Cloud
In the age of Big Data, filtering mechanisms have to professionalized to increase accessibility to data. This presentation, held at Knowledge Management Academy in Vienna, shows how technologies derived from the Semantic Web can help to establish more efficient means to manage data and information.
Knowledge organization systems like taxonomies or thesauri can benefit from linked data approaches and vice versa. In recent years SKOS became very popular in various industries due to its simplicity, not only for information retrieval purposes but also for knowledge modelling itself. For many organizations SKOS turned out to be the entry point to the Semantic Web.
A rather novel approach is to use SKOS as means for data integration. In combination with RDF mapping and linked data alignment technologies complex knowledge bases can be built to use them for the following application scenarios which we will demonstrate by using the PoolParty platform.
• How the creation of thesauri can become more efficient when built upon existing linked data sources
• how SKOS thesauri can be aligned with LOD sources and can be published as an LOD source itself
• How linked data mechanisms can be used to improve decentralized vocabulary management
• How SKOS and linked data alignment can be used for efficient schema mapping and value mapping
• How SKOS thesauri can be enriched with linked data to realise semantic search engines in a very efficient way
• How collaborative platforms like Sharepoint or Confluence can benefit from SKOS based knowledge models in combination with linked data
PoolParty PowerTagging and sOnr are add-ons for Atlassian Confluence. They provide features to annotate and categorize content automatically. sOnr is a collaborative tool for market observer and trend scouts. User benefit from semantic search, automatic text analysis, entity extraction, sentiment analysis, faceted search and content recommendation. Its basis is built by thesauri and controlled vocabularies which are maintained with PoolParty Server.
This webinar in the course of the LOD2 webinar series will present the release 2.0 of the LOD2 stack, which contains updates to the components Ontowiki, Silk
* the assisting sparql editor SPARQLED (DERI),
* the LOD enabled Open Refine (previously Google Refine) (ZEMANTA),
* the extended version of SILK with link suggestion management from LATC (DERI),
* the rdfAuthor library which allows to manage structured information from RDFa-enhanced websites (ULEI),
* the SPARQLPROXY which is a PHP based forward proxy for remote access to SPARQL end points (ULEI)
Release 2.0 contains also a first contributed debian package for a component which is maintained by a group outside the LOD2 consortium. With the help of ULEI a package for the STANBOL engine (http://stanbol.apache.org/) has been contributed.
If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!
http://lod2.eu/BlogPost/webinar-series
OGD2011: Tassilo Pellegrini - Linked Government Data als nachhaltige Maßnahme...Semantic Web Company
Vortrag bei der OGD2011: Tassilo Pellegrini (Semantic Web Company, SWC): Linked Government Data als nachhaltige Maßnahme für eine digitale Infrastruktur
See how taxonomies and thesauri serve as a core element of a linked data strategy and how large knowledge graphs can be built around it. Based on semantic web standards like SKOS, OWL, and SPARQL, enterprises can develop highly agile data integration platforms.
PoolParty Semantic Suite 5.5 has been released in August 2016. Further integrations like with Elasticsearch or Stardog strengthen PoolParty’s position as the leading semantic middleware at the cognitive computing market. Knowledge engineers and users benefit from an even more sophisticated combination of semantic computing and machine learning. The new features support context aware knowledge modelling and include an extended data quality management module.
Presentation: Study: #Big Data in #Austria, Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH & Martin Köhler, Austrian Institute of Technology, AIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
We are excited to announce that our new State of Software Security (SOSS) rep...Ampliz
We are excited to announce that our new State of Software Security (SOSS) report is officially available.
We encourage you to download the report and check out some of the key findings.
For instance, new Research Finds 20x Increase in Software Security Scanning Over the Past Decade.
New Veracode State of Software Security Report
Available Now: https://bit.ly/3tTHT2I
The State of Software Security 2022 SOSS - SolutionNeelKamalSingh8
We are excited to announce that our new State of Software Security (SOSS) report is officially available.
We encourage you to download the report and check out some of the key findings.
Available Now: https://bit.ly/3tTHT2I
Presentation I gave at the Business Fundamentals Bootcamp (March 25, 2011) hosted by Supporting Strategies and Acceleration Partners at the Cambridge Innovation Center.
We are all consumers of financial services more or less. We have bank accounts, possibly life insurance, some of us have credit cards, some of us have fixed deposits, some of us may be doing share trading and investment, some of us are borrowers of loans. These are all financial services. Financial Technology or FinTech is a way of delivering or improving the delivery of financial services using technology and innovation.
The use of smartphones and internet to improve the services in banking, investing, lending and borrowing etc are examples of technologies aiming to make financial services more accessible to the people. The use of Artificial intelligence, Machine learning, Blockchain, Cryptocurrency etc are redefining the way we are used to receiving financial services. FinTech is an emerging industry. Startups, established financial institutions as well as technology companies are disrupting this space to replace or enhance the usage of currently existing financial services.
In this video we will restrict ourselves to the usage of AI in FinTech.
We will learn about different areas where FinTech is already serving a great deal.
We will learn about the areas where we look forward to seeing more disruptions and innovations to make financial services more secure and accessible to the general public.
Software is only successful if someone can use it. Good developers need to do more than just follow specifications, they need to visualize the people who will use it and understand what they need. Get to know your users and the questions you need to ask to make your implementation a success on all fronts.
Looking at a Body Camera Initiative from an IT Infrastructure PerspectiveePlus
Implementing body cameras for your force brings many benefits but getting started can raise many complex questions. ePlus and EMC can help you build a surveillance storage foundation to optimize your body camera deployment. Contact ePlus today to start leveraging the industry's largest and most advanced surveillance validation labs and explore your body camera initiative.
PoolParty PowerTagging and sOnr are add-ons for Atlassian Confluence. They provide features to annotate and categorize content automatically. sOnr is a collaborative tool for market observer and trend scouts. User benefit from semantic search, automatic text analysis, entity extraction, sentiment analysis, faceted search and content recommendation. Its basis is built by thesauri and controlled vocabularies which are maintained with PoolParty Server.
This webinar in the course of the LOD2 webinar series will present the release 2.0 of the LOD2 stack, which contains updates to the components Ontowiki, Silk
* the assisting sparql editor SPARQLED (DERI),
* the LOD enabled Open Refine (previously Google Refine) (ZEMANTA),
* the extended version of SILK with link suggestion management from LATC (DERI),
* the rdfAuthor library which allows to manage structured information from RDFa-enhanced websites (ULEI),
* the SPARQLPROXY which is a PHP based forward proxy for remote access to SPARQL end points (ULEI)
Release 2.0 contains also a first contributed debian package for a component which is maintained by a group outside the LOD2 consortium. With the help of ULEI a package for the STANBOL engine (http://stanbol.apache.org/) has been contributed.
If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!
http://lod2.eu/BlogPost/webinar-series
OGD2011: Tassilo Pellegrini - Linked Government Data als nachhaltige Maßnahme...Semantic Web Company
Vortrag bei der OGD2011: Tassilo Pellegrini (Semantic Web Company, SWC): Linked Government Data als nachhaltige Maßnahme für eine digitale Infrastruktur
See how taxonomies and thesauri serve as a core element of a linked data strategy and how large knowledge graphs can be built around it. Based on semantic web standards like SKOS, OWL, and SPARQL, enterprises can develop highly agile data integration platforms.
PoolParty Semantic Suite 5.5 has been released in August 2016. Further integrations like with Elasticsearch or Stardog strengthen PoolParty’s position as the leading semantic middleware at the cognitive computing market. Knowledge engineers and users benefit from an even more sophisticated combination of semantic computing and machine learning. The new features support context aware knowledge modelling and include an extended data quality management module.
Presentation: Study: #Big Data in #Austria, Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH & Martin Köhler, Austrian Institute of Technology, AIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
We are excited to announce that our new State of Software Security (SOSS) rep...Ampliz
We are excited to announce that our new State of Software Security (SOSS) report is officially available.
We encourage you to download the report and check out some of the key findings.
For instance, new Research Finds 20x Increase in Software Security Scanning Over the Past Decade.
New Veracode State of Software Security Report
Available Now: https://bit.ly/3tTHT2I
The State of Software Security 2022 SOSS - SolutionNeelKamalSingh8
We are excited to announce that our new State of Software Security (SOSS) report is officially available.
We encourage you to download the report and check out some of the key findings.
Available Now: https://bit.ly/3tTHT2I
Presentation I gave at the Business Fundamentals Bootcamp (March 25, 2011) hosted by Supporting Strategies and Acceleration Partners at the Cambridge Innovation Center.
We are all consumers of financial services more or less. We have bank accounts, possibly life insurance, some of us have credit cards, some of us have fixed deposits, some of us may be doing share trading and investment, some of us are borrowers of loans. These are all financial services. Financial Technology or FinTech is a way of delivering or improving the delivery of financial services using technology and innovation.
The use of smartphones and internet to improve the services in banking, investing, lending and borrowing etc are examples of technologies aiming to make financial services more accessible to the people. The use of Artificial intelligence, Machine learning, Blockchain, Cryptocurrency etc are redefining the way we are used to receiving financial services. FinTech is an emerging industry. Startups, established financial institutions as well as technology companies are disrupting this space to replace or enhance the usage of currently existing financial services.
In this video we will restrict ourselves to the usage of AI in FinTech.
We will learn about different areas where FinTech is already serving a great deal.
We will learn about the areas where we look forward to seeing more disruptions and innovations to make financial services more secure and accessible to the general public.
Software is only successful if someone can use it. Good developers need to do more than just follow specifications, they need to visualize the people who will use it and understand what they need. Get to know your users and the questions you need to ask to make your implementation a success on all fronts.
Looking at a Body Camera Initiative from an IT Infrastructure PerspectiveePlus
Implementing body cameras for your force brings many benefits but getting started can raise many complex questions. ePlus and EMC can help you build a surveillance storage foundation to optimize your body camera deployment. Contact ePlus today to start leveraging the industry's largest and most advanced surveillance validation labs and explore your body camera initiative.
This slideshow will explain the present and future usage of AI in FinTech. The author has identified more than 10 areas where AI is positioned to play a very important role in FinTech.
Automatic video censoring system using deep learningIJECEIAES
Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications.
Log management and compliance: What's the real story? by Dr. Anton ChuvakinAnton Chuvakin
Title: Log management and compliance: What's the real story? by Dr. Anton Chuvakin
One of the problems in making an Enterprise Content Management (ECM) strategy work with compliance initiatives is that compliance needs accountability at a very granular level. Consequently, IT shops are turning to log management as a solution, with many of those solutions being deployed for the purposes of regulatory compliance. The language however, regarding log management solutions can sometimes be vague which can lead to confusion. This session will lend some clarity to the regulations that affect log management. Topics will include:
Best practices for how to best mesh compliance ECM and compliance strategies with log management
Tips and suggestions for monitoring and auditing access to regulated content, with a focus on Microsoft Sharepoint logging.
An examination of a handful of the regulations affecting how organizations view log management and information security including The Payment Card Industry Data Security Standard (PCI DSS), ISO 27001, The North American Electric Reliability Council (NERC), HIPAA and the HITECH Act.
Realeyes' Commercial Director Alex Slater took the stage alongside our partners MediaCom, to reveal how emotional intelligence can be used to build more engaging brand experiences.
With the increasing in the number of anti-social activates that have been taking place, security has been given utmost importance lately. Many Organizations have installed CCTVs for constant Monitoring of people and their interactions. For a developed Country with a population of 64 million, every person is captured by a camera 30 times a day. A lot of video data generated and stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are abnormal is near impossible task as requires a workforce and their constant attention. This creates a need to automate the same. Also , there is need to show in which frame and which part of it contain the unusual activity which aid the faster judgment of the unusual activity being abnormal. This is done by converting video into frames and analyzing the persons and their activates from the processed frame .Machine learning and Deep Learning Algorithms and techniques support us in a wide accept to make Possible.
Video delivery & experience, September 2022decodemai
Quality of video delivery is integral to the experience of an OTT platform. Here’s a quick landscape of all the video OTT platforms, and the issues around streaming, as spoken by the customers. It is an interesting insight into why some platforms have taken huge leads over other platforms. It’s not just the content, delivery also matters! The report lays out technology opportunities for start-ups and innovation teams/ researchers.
Fidelis Cybersecurity commissioned 360Velocity to conduct an enterprise study on the State of the SOC, including current trends and practices of threat detection and response. Join this webinar to listen to security experts Dr. Chenxi Wang of 360Velocity and Tim Roddy, VP of Cybersecurity Product Strategy at Fidelis examine how to standardize processes for threat detection and response & the case for and how to integrate network sensors and endpoint enforcement
Computer forensics services can include the review and categorising of photographic material. There is a constant striving for improved technology, efficiency and effectiveness, for more information visit http://www.cclgroupltd.com
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisation
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Deep Text Analytics - How to extract hidden information and aboutness from textSemantic Web Company
- Deep Text Analytics (DTA) is an application of Semantic AI
- DTA fuses methods and algorithms taken from language modeling, corpus linguistics, machine learning, knowledge representation and the semantic web result into Deep Text Analytics methods
- Main areas of use cases for DTA are Information retrieval, NLU, Question answering, and Recommender Systems
Leveraging Knowledge Graphs in your Enterprise Knowledge Management SystemSemantic Web Company
Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers.
In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips.
While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically.
We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint:
- Keeping Term Store and the semantic layer in sync
- Automatic tagging of SharePoint content
- Use of graph databases to store tags
- Entity-centric search & analytics applications
Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way.
The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.
Slides based on a workshop held at SEMANTiCS 2018 in Vienna. Introduces a methodology for knowledge graph management based on Semantic Web standards, ranging from taxonomies over ontologies, mappings, graph and entity linking. Further topics covered: Semantic AI and machine learning, text mining, and semantic search.
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Machines learn better with Semantics!
See how taxonomy management and the maintenance of knowledge graphs benefit from machine learning and corpus analysis, and how, in return, machine learning gets improved when using semantic knowledge models for further enrichment.
A quick introduction to taxonomies, and how they relate to ontologies and knowledge graph. See how they can serve as part of a semantic layer in your information architecture. Learn which use cases can be developed based on this.
PoolParty GraphSearch - The Fusion of Search, Recommendation and AnalyticsSemantic Web Company
See how Cognitive Search works when based on Semantic Knowledge Graphs.
We showcase the latest developments and new features of PoolParty GraphSearch:
- Navigate a semantic knowledge graph
- Ontology-based data access (OBDA)
- Search over various search spaces: Ontology-driven facets including hierarchies
- Sophisticated autocomplete including context information
- Custom views on entity-centric and document-centric search results
- Linked data: put various tagging services such as TRIT or PoolParty Extractor in series and benefit from comprehensive semantic enrichment
- Statistical charts to explain results from unified data repositories quickly
- Plug-in system for various recommendation and matchmaking algorithms
This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.
To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.
Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.
There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.
A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.
This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
See how you can configure your linked data eco-system based on PoolParty's semantic middleware configurator. Benefit from Shadow Concept Extraction by making implicit knowledge visible. Combine knowledge graphs with machine learning and integrate semantics into your enterprise information systems.
Technical Deep Dive: Learn more about the most complete Semantic Middleware on the market. See how to integrate semantic services into your Enterprise Information Systems.
Taxonomies and Ontologies – The Yin and Yang of Knowledge ModellingSemantic Web Company
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
This talk addresses two questions: “How can the quality of taxonomies be defined?” and “How can it be measured?” See how quality criteria vary depending on how a taxonomy is applied, such as automatic content classification in ecommerce or a knowledge graph for data integration in enterprises. Distinguish between formal quality, structural properties, content coverage, and network topology. Investigate the advantages of standards-based and machine-processable SKOS taxonomies to be able to measure the quality of taxonomies automatically, as well as several tools and techniques for quality assessment.
Consistency is crucial to a good user experience. Designers go to great lengths to create and test consistent visual designs. The structural design of an information environment, which is of equal importance to a good user experience, is too often ignored. Blumauer presents a “four-layered content architecture” for making sense of any information environment by clearly distinguishing between the content, metadata, and semantic layers and the navigation logic. He discusses several use cases for a taxonomy-driven user experience such as personalization or dynamically created topic pages.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
4. Takeaway Ideas
o Law enforcement needs a better way to
manage body camera video
o Using semantics to help analyze video can
reduce effort and improve usage
o Solution requires integrating multiple types
of systems and capabilities
8. What’s the Problem?
As more police agencies equip
officers with body cameras in
response to public pressure,
authorities are discovering they
create problems of their own:
how to analyze, process and
store the mountains of video
each camera generates.
9. What’s the Problem?
Right now, there are too many
body camera videos being
produced for law enforcement
agencies to effectively analyze
10. What’s the Problem?
This problem is being
compounded daily by more
cameras producing more videos
that are simply being stored to a
cloud system
11. What’s the Problem?
The cameras create an
enormous amount of data.
Who gets access to it? How
does it get stored? What
appeared to be a no-brainer in
terms of bringing accountability
to the force has raised a lot of
ancillary questions.
12. How Many Police Agencies
are There in the US?
765,000
officers
18,000
agencies
+5%
13. Who’s Got the Cameras?
4,500
agencies= have
13,500
agencies= want
Only 25%
currently
have cameras
14. How Much Storage is
Needed?
110
officers
8,000 – 10,000
videos /month
US$2.6M
year
Evidentia
ry vs.
Non-
Evidentia
ry
Example
using one
police
departme
nt
Estimat
ed cost
of
storag
e for
the
first
year
15. Our Areas of Research
Video
management
Semantic
Extraction
Standardized
vocabularies
How can
agencies
determine
if there
is
relativel
y
important
evidence
on a
How
should
video
be
create
d and
stored
?
Can we
create
a
unified
ontolo
gy for
all
levels
of law
enforc
18. What Does Each Provide?
Provid
es the
online
video
storag
e and
proces
sing,
plus
advanc
ed
video
manag
ement
19. What Does Each Provide?
Provid
es the
video
to
text
extrac
tion
servic
e plus
keywo
rd
taggin
g
capabi
lity
20. What Does Each Provide?
Provides
the
semantic
analysis
capabilit
y plus
the
ontology
21. What Does Each Provide?
Provid
es the
servic
es and
integr
ation
capabi
lity
22. Challenges Ahead
Scalability
REGULATION Privacy
50 states
have
different
regulatio
ns
regarding
video
usage
Most
police
agencie
s are
small
to
midsize
and
have
limited
budget
s
Just
becaus
e video
can be
used,
who
gets to
23. Our Next Steps
Proof of
Concept
Scaled Testing
Overcome
Doubt
Need to
validate
how
large the
solution
can be
Working
with a
few
agencie
s now
for PoC
Police
are
natura
lly
wary
of
being
record