The document discusses the use of automated data-driven synthesis (DDS) to rapidly generate massive knowledge repositories directly from data. DDS uses a compiler-based approach to synthesize large quantities of high-quality, semantically linked content at low cost. This overcomes limitations of manual knowledge repository construction, including high costs, errors, and limited scale. DDS can synthesize a wide range of knowledge structures, from simple data records and reports to complex digital libraries and knowledge repositories.
This document provides an overview and introduction to Content Analytics and IBM Watson Explorer. It discusses how the amount of data in the world is growing exponentially and will soon reach 180 zettabytes. It explains that content analytics is about gaining understanding from vast stores of data and putting that knowledge to work. Watson Explorer is presented as an IBM software application that connects users to information from various sources and mines unstructured content to reveal trends and insights. It utilizes cognitive computing through Watson Developer Cloud services to further analyze and interpret content.
Re-examining the Jennex Olfman KM Success ModelSIKM
The document discusses Murray Jennex's efforts to re-examine and update his 2006 Knowledge Management Success Model. It provides background on Jennex and the original model. Jennex then proposes a revised model based on technological advances and new research. The revised model adds constructs like knowledge governance and expands areas like technical infrastructure. Future work involves validating the new model through surveys. A separate study discusses measuring KM success, presenting survey results and identifying key success measures across business, knowledge, leadership, and strategy dimensions.
IT leaders from across North America were invited to share their viewpoint and perspective on delivering Agile IT. The study reflects the responses and trends related to their ability to deliver on business demands and readiness of existing technology to support those needs. We aggregated the results into following major themes: Strategy vs Reality, Agility & Technology Readiness, and Culture, Structure & People.
Extended discourse on the importance of data science governance for production ML and how GDPR can become the catalyst but also generate value for organizations!
Data Prep - A Key Ingredient for Cloud-based AnalyticsDATAVERSITY
Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.
SharePoint Saturday London - The Nuts and Bolts of Metadata Tagging and Taxon...Concept Searching, Inc
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so.
Explore how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
Understand the Return on Investment from an effective infrastructure solution for search, security, compliance, eDiscovery, records management, knowledge management, collaboration, and migration activities.
• Watch multi-term metadata being automatically generated.
• Learn how easy it is to use taxonomy tools and interactive features, such as auto-clue suggestion, instant feedback, and assigning weights to terms.
• Discover the value of dynamic screen updating to immediately see the impact of taxonomy changes.
• View how document movement feedback enables you to see the cause and effect of changes without re-indexing.
Understand must-have functionality, to help you evaluate classification and taxonomy software.
Starting with the importance of multi-term metadata, learn about the pros and cons of differing technologies, which questions to ask vendors, and what suits your organization.
Go beyond the basics, to find out what it takes to manage a taxonomy and integrate it with the SharePoint Term Store.
Take away an understanding of:
• Metadata generation – why it is so important.
• Auto-classification – why you can’t live without it.
• Taxonomy approaches that are manageable – by the staff you already have.
DM Radio Webinar: Adopting a Streaming-Enabled ArchitectureDATAVERSITY
Architecture matters. That's why today's innovators are taking a hard look at streaming data, an increasingly attractive option that can transform business in several ways: replacing aging data ingestion techniques like ETL; solving long-standing data quality challenges; improving business processes ranging from sales and marketing to logistics and procurement; or any number of activities related to accelerating data warehousing, business intelligence and analytics.
Register for this DM Radio Deep Dive Webinar to learn how streaming data can rejuvenate or supplant traditional data management practices. Host Eric Kavanagh will explain how streaming-first architectures can relieve data engineers from time-consuming, error-prone processes, ideally bidding farewell to those unpleasant batch windows. He'll be joined by Kevin Petrie of Attunity, who will explain why (with real-world story successes) streaming data solutions can keep the business fueled with trusted data in a timely, efficient manner for improved business outcomes.
How to Get Started with Your MongoDB Pilot ProjectDATAVERSITY
Open source, high performance database MongoDB can be used for a pilot project. The document discusses finding a non-critical initial project, getting experience with MongoDB, benchmarking performance, and presenting the business case for broader use. It also outlines steps for moving a successful pilot to production, including using MongoDB's auto-sharding, replication, and commercial support options.
This document provides an overview and introduction to Content Analytics and IBM Watson Explorer. It discusses how the amount of data in the world is growing exponentially and will soon reach 180 zettabytes. It explains that content analytics is about gaining understanding from vast stores of data and putting that knowledge to work. Watson Explorer is presented as an IBM software application that connects users to information from various sources and mines unstructured content to reveal trends and insights. It utilizes cognitive computing through Watson Developer Cloud services to further analyze and interpret content.
Re-examining the Jennex Olfman KM Success ModelSIKM
The document discusses Murray Jennex's efforts to re-examine and update his 2006 Knowledge Management Success Model. It provides background on Jennex and the original model. Jennex then proposes a revised model based on technological advances and new research. The revised model adds constructs like knowledge governance and expands areas like technical infrastructure. Future work involves validating the new model through surveys. A separate study discusses measuring KM success, presenting survey results and identifying key success measures across business, knowledge, leadership, and strategy dimensions.
IT leaders from across North America were invited to share their viewpoint and perspective on delivering Agile IT. The study reflects the responses and trends related to their ability to deliver on business demands and readiness of existing technology to support those needs. We aggregated the results into following major themes: Strategy vs Reality, Agility & Technology Readiness, and Culture, Structure & People.
Extended discourse on the importance of data science governance for production ML and how GDPR can become the catalyst but also generate value for organizations!
Data Prep - A Key Ingredient for Cloud-based AnalyticsDATAVERSITY
Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.
SharePoint Saturday London - The Nuts and Bolts of Metadata Tagging and Taxon...Concept Searching, Inc
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so.
Explore how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
Understand the Return on Investment from an effective infrastructure solution for search, security, compliance, eDiscovery, records management, knowledge management, collaboration, and migration activities.
• Watch multi-term metadata being automatically generated.
• Learn how easy it is to use taxonomy tools and interactive features, such as auto-clue suggestion, instant feedback, and assigning weights to terms.
• Discover the value of dynamic screen updating to immediately see the impact of taxonomy changes.
• View how document movement feedback enables you to see the cause and effect of changes without re-indexing.
Understand must-have functionality, to help you evaluate classification and taxonomy software.
Starting with the importance of multi-term metadata, learn about the pros and cons of differing technologies, which questions to ask vendors, and what suits your organization.
Go beyond the basics, to find out what it takes to manage a taxonomy and integrate it with the SharePoint Term Store.
Take away an understanding of:
• Metadata generation – why it is so important.
• Auto-classification – why you can’t live without it.
• Taxonomy approaches that are manageable – by the staff you already have.
DM Radio Webinar: Adopting a Streaming-Enabled ArchitectureDATAVERSITY
Architecture matters. That's why today's innovators are taking a hard look at streaming data, an increasingly attractive option that can transform business in several ways: replacing aging data ingestion techniques like ETL; solving long-standing data quality challenges; improving business processes ranging from sales and marketing to logistics and procurement; or any number of activities related to accelerating data warehousing, business intelligence and analytics.
Register for this DM Radio Deep Dive Webinar to learn how streaming data can rejuvenate or supplant traditional data management practices. Host Eric Kavanagh will explain how streaming-first architectures can relieve data engineers from time-consuming, error-prone processes, ideally bidding farewell to those unpleasant batch windows. He'll be joined by Kevin Petrie of Attunity, who will explain why (with real-world story successes) streaming data solutions can keep the business fueled with trusted data in a timely, efficient manner for improved business outcomes.
How to Get Started with Your MongoDB Pilot ProjectDATAVERSITY
Open source, high performance database MongoDB can be used for a pilot project. The document discusses finding a non-critical initial project, getting experience with MongoDB, benchmarking performance, and presenting the business case for broader use. It also outlines steps for moving a successful pilot to production, including using MongoDB's auto-sharding, replication, and commercial support options.
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
This document discusses the challenges of using SQL for data analysis and introduces Alteryx as an alternative. It notes that SQL can be difficult to understand and repeat, while Alteryx allows users to see the full data workflow, perform transformations without coding, and access different data sources flexibly. The presentation includes an agenda, overview of Alteryx's benefits, and demonstration of its capabilities.
This document discusses data science governance and Kensu's product, Adalog, which aims to address it. It defines data science governance as controlling data activities to meet standards and monitoring production data activity. This involves understanding who does what with which data. Kensu collects metadata on all data tools and processes, connects this information to create a map of all activities, and uses this for impact analysis, dependency analysis, and optimization. Adalog does this to provide accountability and transparency as required by GDPR. It collects data on activities and connects them to automatically generate a process registry and provide transparent reports across the processing chain.
The document discusses the system development life cycle, which includes 5 phases: planning, analysis, design, implementation, and operation/support/security. It describes the activities performed in each phase such as conducting feasibility studies, gathering requirements, designing system details, acquiring hardware/software, testing, and training users. Project management techniques like Gantt charts are used to plan and schedule development projects.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
The Nuts and Bolts of Metadata Tagging and Taxonomies Made Easy WebinarConcept Searching, Inc
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so with Concept Searching’s unique technologies.
Join Michael Paye, our CTO, to see how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
Understand the return on investment from an effective infrastructure solution for search, security, compliance, eDiscovery, records management, knowledge management, collaboration, and migration activities.
• Learn how our solution can meet either one challenge or several, and see how it works with different applications
• Watch multi-term metadata being automatically generated
• See how easy it is to use unique taxonomy tools and interactive features, such as clue suggestion, instant feedback, and assigning weights to terms
• Discover the value of dynamic screen updating to immediately see the impact of taxonomy changes
• View how document movement feedback enables you to see the cause and effect of changes without re-indexing
Key Elements for a Successful Service Analytics ProgramData Con LA
Data Con LA 2020
DescriptionThis talk will focus on providing the key elements that enable the successful roll out of a self service analytics program at any organization. I'll discuss my tenure at Qualcomm where I led a self service program there for 10 years and grew it to 500 developers, 3000 applications and 15000 end users. I'll also go over other client case studies like the California Department of Public Health and Illumina where we are developing similar self service programs and go over what works and what does not work.
Speaker
Steve Rimar, Analytica Consulting, LLC, CEO & Founder
Data-Ed Online: Emerging Trends in Data JobsDATAVERSITY
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
BlueBrain Nexus Technical IntroductionBogdan Roman
BlueBrain Nexus is a data management platform that enables modeling of data from different domains according to FAIR principles. It uses semantic web technologies like JSON-LD and SHACL to describe, constrain, relate, and evolve data models over time. Nexus treats provenance as a first class citizen and provides semantic search, publishing, and integration capabilities for domain agnostic and interoperable data management.
Join Concept Searching and partner C/D/H for this thought-provoking webinar on what intelligent enterprise search should be.
Our solution is unique in the marketplace, and overcomes the limitations of other enterprise search engines. It was originally deployed as an enterprise search solution for engineers and support staff.
This webinar will focus on how one unified view of all unstructured, semi-structured, and structured data assets, including 2D and 3D images, can be integrated into the search interface, with previewers and navigational aids.
Both business and technical professionals will benefit from this session:
• Understand how the technology works, and how it can be set up with a platform and search engine of choice
• See how search returns results, and provides visual and navigational aids for all information retrieved
• Watch how to select an image based on color, size, or shape
• Learn how any business or artificial intelligence applications can benefit from the multi-term metadata created
• Find out why the search framework provides a responsive user interface for any tablet, PC or mobile device
View the companion webinar at: http://embt.co/1L8V6dI
Some claim that, in the age of Big Data, data modeling is less important or even not needed. However, with the increased complexity of the data landscape, it is actually more important to incorporate data modeling in order to understand the nature of the data and how they are interrelated. In order to do this effectively, the way that we do data modeling needs to adapt to this complex environment.
One of the key data modeling issues is how to foster collaboration between new groups, such as data scientists, and traditional data management groups. There are often different paradigms, and yet it is critical to have a common understanding of data and semantics between different parts of an organization. In this presentation, Len Silverston will discuss:
+ How Big Data has changed our landscape and affected data modeling
+ How to conduct data modeling in a more ‘agile’ way for Big Data environments
+ How we can collaborate effectively within an organization, even with differing perspectives
About the Presenter:
Len Silverston is a best-selling author, consultant, and a fun and top rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. He has helped many organizations world-wide to integrate their data, systems and even their people. He is well known for his work on "Universal Data Models", which are described in The Data Model Resource Book series (Volumes 1, 2, and 3).
A Year in Review - Building a Comprehensive Data Management ProgramDataWorks Summit
This document discusses Microsoft Research's efforts to build a centralized data management and processing platform. It provides an overview of big data and its importance to Microsoft. It outlines the vision, principles, goals, and architecture of the platform, which includes Hadoop, GPUs, HPC resources, Azure, and access to datasets like MNIST and Bing data. The platform aims to support research through centralized, compliant data storage and a flexible processing system. It also discusses ensuring data privacy, security, and ethical use of data on the platform.
Strategic imperative the enterprise data modelDATAVERSITY
With today's increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced AnalyticsDATAVERSITY
Reassessing the information management marketplace for your enterprise direction on an annual basis is too infrequent. The technology is changing too fast. Data and analytic maturity levels rapidly evolve. What is advanced today may be entry-level in two years. Let’s look at the high points for 1H 2020 in information management developments and how that may change what you are doing now. This can also be a strong data point for preparing 2021 budgets.
ARMA Calgary Spring Seminar: The Nuts and Bolts of Metadata Tagging and Taxon...Concept Searching, Inc
Michael Pay, Concept Searching's Chief Technology Officer, will be speaking at the ARMA Calgary Spring Seminar on Tuesday April 25th, 2017 on:
The Nuts and Bolts of Metadata Tagging and Taxonomies Made Easy
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so with Concept Searching’s unique technologies. Join Michael Paye, Concept Searching’s Chief Technology Officer, to see how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
This session focuses on records management challenges in Office 365, Microsoft Exchange, and file shares, demonstrating:
• Automated multi-term metadata generation
• Unique taxonomy tools and interactive features, such as clue suggestion, instant feedback, and assigning weights to terms
• Flexible and simple reporting across the three repositories
• Automated records identification
• Tagging of content directly in Office 365 and on file shares
How you can gain rapid insights and create more flexibility by capturing and storing data from a variety of sources and structures into a NoSQL database.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
CESSI is an organization in Argentina that produces knowledge-based content but had difficulties sharing it. They implemented kbee.docs, a document management system, to create a digital library. Kbee.docs allows for secure uploading, organizing, searching, and sharing of documents and multimedia content. It provides tools for classification, security policies, and collaboration without requiring technical expertise or ongoing maintenance.
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
This document discusses the challenges of using SQL for data analysis and introduces Alteryx as an alternative. It notes that SQL can be difficult to understand and repeat, while Alteryx allows users to see the full data workflow, perform transformations without coding, and access different data sources flexibly. The presentation includes an agenda, overview of Alteryx's benefits, and demonstration of its capabilities.
This document discusses data science governance and Kensu's product, Adalog, which aims to address it. It defines data science governance as controlling data activities to meet standards and monitoring production data activity. This involves understanding who does what with which data. Kensu collects metadata on all data tools and processes, connects this information to create a map of all activities, and uses this for impact analysis, dependency analysis, and optimization. Adalog does this to provide accountability and transparency as required by GDPR. It collects data on activities and connects them to automatically generate a process registry and provide transparent reports across the processing chain.
The document discusses the system development life cycle, which includes 5 phases: planning, analysis, design, implementation, and operation/support/security. It describes the activities performed in each phase such as conducting feasibility studies, gathering requirements, designing system details, acquiring hardware/software, testing, and training users. Project management techniques like Gantt charts are used to plan and schedule development projects.
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
The Nuts and Bolts of Metadata Tagging and Taxonomies Made Easy WebinarConcept Searching, Inc
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so with Concept Searching’s unique technologies.
Join Michael Paye, our CTO, to see how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
Understand the return on investment from an effective infrastructure solution for search, security, compliance, eDiscovery, records management, knowledge management, collaboration, and migration activities.
• Learn how our solution can meet either one challenge or several, and see how it works with different applications
• Watch multi-term metadata being automatically generated
• See how easy it is to use unique taxonomy tools and interactive features, such as clue suggestion, instant feedback, and assigning weights to terms
• Discover the value of dynamic screen updating to immediately see the impact of taxonomy changes
• View how document movement feedback enables you to see the cause and effect of changes without re-indexing
Key Elements for a Successful Service Analytics ProgramData Con LA
Data Con LA 2020
DescriptionThis talk will focus on providing the key elements that enable the successful roll out of a self service analytics program at any organization. I'll discuss my tenure at Qualcomm where I led a self service program there for 10 years and grew it to 500 developers, 3000 applications and 15000 end users. I'll also go over other client case studies like the California Department of Public Health and Illumina where we are developing similar self service programs and go over what works and what does not work.
Speaker
Steve Rimar, Analytica Consulting, LLC, CEO & Founder
Data-Ed Online: Emerging Trends in Data JobsDATAVERSITY
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
BlueBrain Nexus Technical IntroductionBogdan Roman
BlueBrain Nexus is a data management platform that enables modeling of data from different domains according to FAIR principles. It uses semantic web technologies like JSON-LD and SHACL to describe, constrain, relate, and evolve data models over time. Nexus treats provenance as a first class citizen and provides semantic search, publishing, and integration capabilities for domain agnostic and interoperable data management.
Join Concept Searching and partner C/D/H for this thought-provoking webinar on what intelligent enterprise search should be.
Our solution is unique in the marketplace, and overcomes the limitations of other enterprise search engines. It was originally deployed as an enterprise search solution for engineers and support staff.
This webinar will focus on how one unified view of all unstructured, semi-structured, and structured data assets, including 2D and 3D images, can be integrated into the search interface, with previewers and navigational aids.
Both business and technical professionals will benefit from this session:
• Understand how the technology works, and how it can be set up with a platform and search engine of choice
• See how search returns results, and provides visual and navigational aids for all information retrieved
• Watch how to select an image based on color, size, or shape
• Learn how any business or artificial intelligence applications can benefit from the multi-term metadata created
• Find out why the search framework provides a responsive user interface for any tablet, PC or mobile device
View the companion webinar at: http://embt.co/1L8V6dI
Some claim that, in the age of Big Data, data modeling is less important or even not needed. However, with the increased complexity of the data landscape, it is actually more important to incorporate data modeling in order to understand the nature of the data and how they are interrelated. In order to do this effectively, the way that we do data modeling needs to adapt to this complex environment.
One of the key data modeling issues is how to foster collaboration between new groups, such as data scientists, and traditional data management groups. There are often different paradigms, and yet it is critical to have a common understanding of data and semantics between different parts of an organization. In this presentation, Len Silverston will discuss:
+ How Big Data has changed our landscape and affected data modeling
+ How to conduct data modeling in a more ‘agile’ way for Big Data environments
+ How we can collaborate effectively within an organization, even with differing perspectives
About the Presenter:
Len Silverston is a best-selling author, consultant, and a fun and top rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. He has helped many organizations world-wide to integrate their data, systems and even their people. He is well known for his work on "Universal Data Models", which are described in The Data Model Resource Book series (Volumes 1, 2, and 3).
A Year in Review - Building a Comprehensive Data Management ProgramDataWorks Summit
This document discusses Microsoft Research's efforts to build a centralized data management and processing platform. It provides an overview of big data and its importance to Microsoft. It outlines the vision, principles, goals, and architecture of the platform, which includes Hadoop, GPUs, HPC resources, Azure, and access to datasets like MNIST and Bing data. The platform aims to support research through centralized, compliant data storage and a flexible processing system. It also discusses ensuring data privacy, security, and ethical use of data on the platform.
Strategic imperative the enterprise data modelDATAVERSITY
With today's increasingly complex data ecosystems, the Enterprise Data Model (EDM) is a strategic imperative that every organization should adopt. An Enterprise Data Model provides context and consistency for all organizational data assets, as well as a classification framework for data governance. Enterprise modeling is also totally consistent with agile workflows, evolving incrementally to keep pace with changing organizational factors. In this session, IDERA’s Ron Huizenga will discuss the increasing importance of the EDM, how it serves as a framework for all enterprise data assets, and provides a foundation for data governance.
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced AnalyticsDATAVERSITY
Reassessing the information management marketplace for your enterprise direction on an annual basis is too infrequent. The technology is changing too fast. Data and analytic maturity levels rapidly evolve. What is advanced today may be entry-level in two years. Let’s look at the high points for 1H 2020 in information management developments and how that may change what you are doing now. This can also be a strong data point for preparing 2021 budgets.
ARMA Calgary Spring Seminar: The Nuts and Bolts of Metadata Tagging and Taxon...Concept Searching, Inc
Michael Pay, Concept Searching's Chief Technology Officer, will be speaking at the ARMA Calgary Spring Seminar on Tuesday April 25th, 2017 on:
The Nuts and Bolts of Metadata Tagging and Taxonomies Made Easy
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so with Concept Searching’s unique technologies. Join Michael Paye, Concept Searching’s Chief Technology Officer, to see how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
This session focuses on records management challenges in Office 365, Microsoft Exchange, and file shares, demonstrating:
• Automated multi-term metadata generation
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Page 18Goal Implement a complete search engine. Milestones.docxsmile790243
Page 1/8
Goal: Implement a complete search engine. Milestones Overview
Milestone Goal #1 Produce an initial index for the corpus and a basic retrieval component
#2 Complete Search System
Page 2/8
PROJECT: SEARCH ENGINE Corpus: all ICS web pages We will provide you with the crawled data as a zip file (webpages_raw.zip). This contains the downloaded content of the ICS web pages that were crawled by a previous quarter. You are expected to build your search engine index off of this data. Main challenges: Full HTML parsing, File/DB handling, handling user input (either using command line or desktop GUI application or web interface) COMPONENT 1 - INDEX: Create an inverted index for all the corpus given to you. You can either use a database to store your index (MongoDB, Redis, memcached are some examples) or you can store the index in a file. You are free to choose an approach here. The index should store more than just a simple list of documents where the token occurs. At the very least, your index should store the TF-IDF of every term/document. Sample Index:
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COMPONENT 3 - RANKING:
At the very least, your ranking formula should include tf-idf scoring, but you should feel free to add additional components to this formula if you think they improve the retrieval. Optional (1 point for each parameter up to 2 points max): Extra credit will be given if your ranking formula includes par.
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How do you structure your information systems to enable collaboration? Through careful planning, proper structure, and
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How AI is transforming DevOps | Calidad InfotechCalidad Infotech
DevOps is a remarkable asset to start-ups. The growing technology over the last two decades has made it easier to build & scale all sizes of businesses & organizations. In this fast-paced growing technology world, DevOps has paved its way with its innovative & effective tools & practices that have turned out to be a… Continue reading.. https://calidadinfotech.com/devops-services
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Automatic and rapid generation of massive knowledge repositories from data
1. IF4IT
AUTOMATIC AND RAPID
GENERATION OF MASSIVE
KNOWLEDGE REPOSITORIES,
DIRECTLY FROM DATA
Author/Presenter: Frank Guerino
Chairman for The International Foundation for Information Technology (IF4IT)
Email: Frank.Guerino @ if4it.com
LinkedIn: https://www.linkedin.com/in/frankguerino/
Follow Us on Twitter: @IF4IT
Co-Author: Dr. Joel Kline, PhD.
Board of Advisors, The International Foundation for Information Technology (IF4IT)
Professor, Lebanon Valley College, PA-USA
1
2. IF4IT
The Future isAutomated Synthesis of Knowledge Repositories
Read More: https://www.if4it.com/knowledge-management-automated-content-generation-and-curation/
Meet Bob.
Bob is very competent.
Bob outperforms other people
by generating one great
knowledge article per hour.
Automated Content
Generation
Software
Meet Bob’s
replacement.
Bob’s replacement generates millions of
higher quality, highly curated, and
semantically inter-linked knowledge articles,
in the time it takes Bob to create just one… at
a fraction of the cost.
2
Few knowledge repositories,
limited content, poor curation,
lots of dead links, and no
semantic relationships.
More knowledge repositories,
far more content, greater
curation, almost no dead links,
and semantic relationships.
✖
✔
ACTOR ACTIONS RESULTS
3. IF4IT
The Wikipedia Problem
• The Wikipedia Community is NOT like an
Enterprise Work Community
- About 17 years to develop,
- Over 130M voluntary editors (i.e. free labor),
- Over 6M content articles
• People believe they can build internal
knowledge repositories (like libraries and intranets) using the same
manual content development paradigm as Wikipedia
• The end result is almost always the same… “Relatively empty and
low value Knowledge/Content Repositories”
People often can’t find the answers they need.
Read More: https://www.if4it.com/wikipedia-problem-understanding-enterprise-knowledge-repositories-fail/
3
4. IF4IT
The Problem is Manual Labor
Quantity: Low quantities of artifact delivery.
Quality: Higher levels of human-introduced errors.
Time: Longer artifact delivery times.
Money: High costs for delivery of artifacts.
Trend: Knowledge Repository Automation is very important because,
more often than not, teams that build them have very limited resource
(people & finances).
Trend: With the move to “Digital” the expectation of Knowledge
Repositories is even higher.
4
5. IF4IT
The Solution = Automation via Compilation
• The process is called Synthesis (a.k.a. Compilation)
• Compilation is the word used by software developers
• Synthesis is the word used by non-software developers
• Specifically, we use and recommend Data Driven
Synthesis (DDS)
• We use Compiler-based DDS to generate content, curate
content, interlink content, and automatically build and
provision Knowledge/Content repositories
Read More: https://www.if4it.com/understanding-data-driven-synthesis/
5
6. IF4IT
Many Decades of Successful Synthesis
Synthesis/Compilation of Software (Since 1970s)
Synthesis of Integrated Circuit Schematics (Since 1992)
- Inputs are Hardware Descriptive Languages (HDLs) like VHDL and Verilog.
- Outputs are used for Simulation, Acceleration, Emulation, and Fabrication
Synthesis of APIs and software code (i.e. Scaffolding for Software
Developers, such as for Java Spring and Ruby on Rails)
Synthesis of large volumes of test data to exercise complex systems
Synthesis of chemical Compounds for Drug Discovery
Synthesis of Health Care Pathways (Diagnosis + Treatments)
Synthesis of (computer generated) Music and Art
Synthesis of Electronic Documentation
(i.e. data driven content)
Synthesis of Digital Libraries (massive web sites)
Synthesis of Semantic Data Graphs (SDGs)
6
7. IF4IT
Who cares about DDS-based automation?
• Internet and Intranet Web Content Managers & Developers
• Technical Writers / Technical Communicators
• Architects (Enterprise/Solutions/Business/Applications/Data/etc.)
• Enterprise Models
• Software Developers (Using Compilation for about 5 Decades)
• API Documentation
• Software Configuration Documentation
• Engineers (Using Synthesis for about 3 Decades)
• Hardware, Network, Communications, & Semiconductor Documentation
• Anyone who documents topics, curates, and who publishes results to
web pages in some Content/Knowledge Repository
7
8. IF4IT
Common Use Cases Driving DDS
• Strategic Planning – Enterprise Portfolio Impact Analysis
• Faster Domain Documentation, - More inter-linked documentation,
with interactive data and with fewer errors, @ far lower costs
• Better Customer Support – Rapid and more accurate Incident Impact
Analysis
• Better Operational Work - Faster Knowledge Discovery = faster &
better work decisions
• Lower Development Costs – Synthesis helps eliminate significant
Software Development
• Better Search & Discovery – Synthesis helps yield better & more
accurate Search Results
Higher Levels of Customer / End-User Satisfaction
8
9. IF4IT
Synthesis is Compiler-based
Data
Compiler/Synthes
izer
Baseline Input
Data
Processing
Rules
Synthesized
Output(s)
Outputs are used for
machines like computers
AND for Humans.
Flat files like *.csv
sourced from spreadsheets
and systems.
Controls ontologies,
formatting, view controls,
report generation, semantic
relationship harvesting, etc.
9
Software
Compiler/Synthes
izer
Source Code
Files
Compiled
Software
Software
Compilation/Synthesis
Data
Compilation/Synthesis
10. IF4IT
Benefits of DDS
Agile: Changes can be made iteratively and in
seconds/minutes
• Simple CSV flat files can be compiled
• No long software development cycles
Scalable: Hundreds of Thousands or Millions of content
pages can be generated in minutes
Stable: Elimination of human errors, like dead links, leads
to far higher levels of quality.
Affordable: The cost per content page (including both
Quantity and Quality) is a small fraction of manually
generated content
10
11. IF4IT
The Synthesis Sequence of Events
Application Data
(e.g. .CSV File)
Capability Data
(e.g. .CSV File)
Human Resource Data
(e.g. .CSV File)
Product Data
(e.g. .CSV File)
Service Data
(e.g. .CSV File)
Etc. Data
(e.g. .CSV File)
Facility Data
(e.g. .CSV File)
Organization Data
(e.g. .CSV File)
…Synthesizer Inputs
Fromspreadsheetsandsystems.
1
Processing Rules
for
• Relationship Discovery
• Data Formatting
• View Generation
• Report Calculations
• Etc.
2
Data Synthesizer/
Data Compiler
3
Node Views
Data Graph/Network
Relationships
CI (z)
CI (y)
CI (x)
Business Intelligence
• Inventories
• Reports
• Graphs & Charts
• Glossaries
• Dashboards
• Visualizations
• Abbreviations
• Acronyms
Data Indexes
Catalogs
Intranet/
Digital Library
4
11
12. IF4IT
Real Business Impacts
12
Your Compiler
Intranets / Content Management Systems
(Confluence, Jive, Drupal, MediaWiki, etc.)
Architecture Modeling Tools (AMTs)
(Troux, Mega, Adaptive, System Architect, etc.)
Configuration Management Databases (CMDBs)
(HP, BMC, ServiceNow, etc.)
Stand-Alone Knowledge Management Systems
(Madcap, KPS, Bitrix, SalesForce, ServiceNow, etc.)
Library Management Systems (LMSs)
(Koha, Soft Link, NGL, LibSys, Folet, etc.)
Semantic Data Systems
(Cambridge Semantics, Protégé, Swoop, LDIF, etc.)
The Traditional Way = $$$$$$$$$$$$$$$$$$$
(Too many complex, expensive, difficult to deliver & operate systems
and tools… just to get to a comprehensive view of your enterprise!)
ExpensiveIntegration
ExpensiveBusinessIntelligence&Reporting
ExpensivePeoplewithSpecificSkills
DDS Results = $
(A very simple, very quick, and very
affordable “Compiler Based Approach”)
Your Data
Your Branded Digital Libraries
(Complete with Catalogs, Indexes,
Relationships, Data Views, Reports,
Dashboards, Visualizations, etc.)
3
4
Your Data + Your Rules1
Complexity Simplicity
2
Data Synthesizer/
Data Compiler
✖ ✔
Many Years & Countless Resources Minutes/Hours & Small # of Resources
13. IF4IT
Compiler-based DDS helps generate
“Knowledge Structures”
1. Content – High quantities, richly formatted, highly
structured, and strongly inter-linked
2. Interactive Data Visualizations - for Interactive
Analytics, Data Science, and Visual Discovery
3. Knowledge Repositories – fully curated structures
like advanced Intranets and Digital Libraries
Read More: https://www.if4it.com/knowledge-management-understanding-knowledge-structures/
13
14. IF4IT
1. Content: SFN over LFN
Raw and unstructured human
narrative in the form of “content”
(not “data”).
Highly structured data, based on
Name/Value pair paradigms
(e.g. CSV, JSON, etc.).
✖ ✔
14
15. IF4IT
2. Interactive Data Visualizations
VisualComplexity.com D3js.org
• Data Science and Data Scientists are VERY expensive.
• DDS creates a common set of fully integrated Data Visualizations
• DDS automatically creates many more out-of-the-box and ready-
to-use Data Visualizations, faster and at far lower costs.
15
16. IF4IT
Geographic Maps
Interactive Data Visualization Examples…
Force Directed Graphs Bubbles
Condegram Spirals
Bars, Pies, Lines
Sankey FlowsChords Multivariate Grids
See many interactive examples in the gallery at: http://www.d3js.org
16
18. IF4IT
The Spectrum of Synthesizable Knowledge Structures
Range of Synthesizable Knowledge Structures
• Data Records/Nodes
• Tables & Inventories
• Charts (Pie, Bar, Area,
Bubble, etc.)
• Graphs (Line, Multi-
Line, etc.)
• Web Pages
• Catalogs
• Indexes
• Reports
• Semantic Relationships
• Semantic Predicates
Simple Knowledge
Structures
• Dashboards
• Data Visualizations
(many different
visualizations)
• Semantic Data Graphs
(SDGs) / Semantic Data
Networks (SDNs)
• HTML Link Networks
• Navigation Taxonomies
• Classification
Taxonomies
Moderately Complex
Knowledge
Structures
• General Web Sites
• Intranets
• Architecture Models
• Architecture
Repositories
• Configuration
Management
Databases (CMDBs)
• Domain-specific
Knowledge
Repositories
Complex Knowledge
Structures
• Multi-Context/Multi-
Domain Digital Libraries
that include all other
structures in the
spectrum (all columns
to the left)
• Industry Specific
Determinations…
- Automatic Claim
Processing
- New Viable Drugs
- Healthcare Care
Pathways
- High Frequency Auto-
Investing
- Etc.
Super Complex
Knowledge
Structures
Example Formats = TXT, CSV, TSV, JSON, XML, HTML, SVG, PDF, Etc.
Simplest Most Complex
• Bits and Bytes
• Built-In Types and
Constants
• Lists, Arrays, and Hash
Tables
• Stacks and Heaps
• For Loops, Do Loops,
and While Loops
• Formulas and
Algorithms
• Buffers, Streams and
Files
• Classes and Objects
Simplest Knowledge
Structures
Read More: https://www.if4it.com/knowledge-management-understanding-knowledge-structures/
18
19. IF4IT
DDS Solves the Wikipedia Problem for Enterprises...
Quantity: Much higher quantities of artifact delivery.
Quality: Much higher levels quality.
Time: Much shorter times for artifact delivery (i.e.
much higher quantities with higher quality).
Money: Much lower costs to deliver artifacts
(especially for Data Science & Data Visualizations).
FASTER & BETTER
KNOWLEDGE DISCOVERY
AND DECISION MAKING
19
20. IF4IT
The Benefits of DDS
• More and Better Knowledge Repositories
- Far higher quantities of more advanced content
- More advanced features and capabilities
- Dynamic integration of data with content
- Higher quality of content (e.g. far fewer dead links)
- Far less investment of time and funds
• Higher stakeholder satisfaction and engagement
20
21. IF4IT
Getting Started with DDS
1. Acquire a Data Compiler/Synthesizer
• Contact IF4IT for a free NOUNZ Lite compiler https://www.if4it.com/contact-us/
2. Start with simple Spreadsheet-based Inventories (and Sharepoint List
Structure extracts)
3. Incrementally customize small data sets to meet your needs and your
desired look-and-feel
4. Slowly progress to more complicated Data Extracts (from proprietary
systems)
5. Keep in mind that Time-To-Learn is “incremental” [you don’t have to
start with big projects]
Crawl Walk Run
21
22. IF4IT
Questions and Discussion
22
Frank Guerino
CEO & Chairman
The International Foundation for
Information Technology (IF4IT)
Email: Frank.Guerino@if4it.com
Twitter: @IF4IT
23. IF4IT
Read More:
• Automated Content Generation & Curation: https://www.if4it.com/knowledge-
management-automated-content-generation-and-curation/
• The Wikipedia Problem: https://www.if4it.com/wikipedia-problem-understanding-
enterprise-knowledge-repositories-fail/
• Understanding Data Driven Synthesis: https://www.if4it.com/understanding-data-
driven-synthesis/
• Understanding Knowledge Structures: https://www.if4it.com/knowledge-management-
understanding-knowledge-structures/
• Learn about D3 and Interactive Visualizations: http:www.d3js.org
• Understanding Knowledge Structures: https://www.if4it.com/knowledge-management-
understanding-knowledge-structures/
• Learn about the IF4IT NOUNZ Data Compilation Platform:
https://www.if4it.com/nounz/
• See Interactive Example of DDS-generated Generic Digital Library:
http://nounz.if4it.com (Less than 3 minutes to generate.)
• See Interactive Example of DDS-generated KM Body of Knowledge:
http://km.if4it.com (Only seconds to generate.)
23
25. IF4IT
Global Biopharmaceutical
25
-- TOTAL Administration Category Noun Instances = 5: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Assay Noun Instances = 749: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Biological Matrix Category Noun Instances = 42: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Biomarker Noun Instances = 42: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Company Noun Instances = 18: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Disease Mechanism Noun Instances = 17: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Facility Noun Instances = 3: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Immunoassay Platform Noun Instances = 6: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Instrument Category Noun Instances = 5: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Instrument Noun Instances = 37: Time = Wednesday June 15, 2016 at 10:04:08
-- TOTAL Offering Noun Instances = 516: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Program Category Noun Instances = 5: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Study Type Noun Instances = 17: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL White Paper Noun Instances = 28: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Application Noun Instances = 1000: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Business Domain Noun Instances = 9: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Capability Noun Instances = 32: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Computing Server Noun Instances = 100: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Contract Noun Instances = 1166: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Country Noun Instances = 251: Time = Wednesday June 15, 2016 at 10:04:09
-- TOTAL Customer Noun Instances = 150: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Database Noun Instances = 100: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Data Transport Technology Noun Instances = 4: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Environment Noun Instances = 8: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Frequently Asked Question Noun Instances = 32: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Information Category Noun Instances = 16: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Interface Noun Instances = 99: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Language Code Noun Instances = 504: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Letter Noun Instances = 26: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Location Noun Instances = 50: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Market Sector Noun Instances = 2: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Market Segment Noun Instances = 2: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL News Article Noun Instances = 6: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Number Noun Instances = 9: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Organization Noun Instances = 29: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Policy Noun Instances = 100: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Process Noun Instances = 26: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Product Noun Instances = 25: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Project Noun Instances = 1000: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Resource Noun Instances = 14: Time = Wednesday June 15, 2016 at 10:04:10
-- TOTAL Sales Transaction Noun Instances = 886: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL SDLC Activity Noun Instances = 353: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL SDLC Phase Noun Instances = 14: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL Service Noun Instances = 561: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL Software Noun Instances = 100: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL Glossary Term Noun Instances = 235: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL Vendor Noun Instances = 100: Time = Wednesday June 15, 2016 at 10:04:11
-- TOTAL Undefined Noun Type Noun Instances = 1: Time = Wednesday June 15, 2016 at 10:04:11
TOTAL Number of Unique Noun Types = 48: Time = Wednesday June 15, 2016 at 10:04:11
TOTAL Noun Instances registered = 8500: Time = Wednesday June 15, 2016 at 10:04:11
TOTAL Number of Unique Abbreviations or Acronyms = 655: Time = Wednesday June 15, 2016 at 10:04:11
TOTAL Number of Unique Semantic Relationships = 30767: Time = Wednesday June 15, 2016 at 10:04:15
TOTAL Number of Unique Semantic Relationship Predicates = 97: Time = Wednesday June 15, 2016 at 10:04:15
TOTAL Minimum Number of HTML Links = 113536: Time = Wednesday June 15, 2016 at 10:07:27
Spreadsheets were used to easily and quickly
collect, organize, and supply data to NOUNZ
Compiler in 1st Normal Form CSV formats.
Vertical industry and business data was collected
from public Biopharma web site, organized and
cleansed in about 5 hours.
Generic IT Data was intentionally comingled with
Biopharma vertical industry and business data, in
order to show the effects of mixing different data
types.
TOTALS:
Total unique Noun Types (Data Types) = 48
Total Catalogs = 50
Total Noun Instances (across all Noun Types = 8500
Total Semantic Relationships = 30767
Total Semantic Predicates = 97
Total Abbreviations and Acronyms = 655
Total “minimum” # of HTML links = 113536
Total Compile Time = 3 Minutes and 27 Seconds
26. IF4IT
Regional Health Care Payer/Insurer
26
• 47 defined Noun Types (a.k.a. Data Types),
• almost 49,000 Noun Instances (a.k.a. Data Instances or Records) that are sourced
from the different Noun Types,
• Almost 294,000 automatically synthesized web pages with different views of data
and information,
• Over 300K automatically discovered and harvested Semantic Relationships that
translate directly to over 1,100,000 contextual and meaningful HTML links.
• 46 total Catalogs, Including a Master Catalog, 47 Noun Domain Specific Catalogs
(one for each Noun Type), an Abbreviations/Acronyms Catalog, and a Relationship
Predicates Catalog
• 288 unique Indexing Categories with 2582 unique Data Indexes
• 869 harvested and curated Abbreviations and Acronyms
• Over 1,600 unique semantic relationship descriptors (i.e. Predicates)
• 47 Domain Specific Dashboards (one for each Noun Type).
Total Compiler Time = Approximately 15 minutes