Building your laboratory informatics strategy: The benefit of reference architectures & data standardization.
Presented by:
Wolfgang Colsman, OSTHUS
Dana Vanderwall, Bristol-Myers Squibb
IQPC’s 5th Forum on Laboratory Informatics will provide strategies for overcoming challenges, including:
- In-depth regulatory compliance guidance
- Extensive ELN deployment and roll out projects, focusing on ROI maximization and impact on business performance
- Informatics systems in the biobanking environment
- Proactive approaches to address challenges of integration and interfacing
- Integrating and embracing knowledge management and social media tools
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...OSTHUS
During SmartLab Exchange 2015, Allotrope Foundation and OSTHUS presented the latest update on the Allotrope Framework. To learn more, please view the slides below.
Presented by:
Dana Vanderwall, BMS Research IT & Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS
Semantics for integrated laboratory analytical processes - The Allotrope Pers...OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats,
which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, the Allotrope Foundation develops a
comprehensive and innovative Framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its lifecycle. The
talk describes how laboratory data and their semantic metadata descriptions are brought together to ease the management of vast amount of data that underpin almost
every aspect of drug discovery and development.
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, Allotrope Foundation is developing a comprehensive and innovative framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its life cycle. In this talk we describe how laboratory data and semantic metadata descriptions are brought together to ease the management of a vast amount of data that underpins almost every aspect of drug discovery and development.
Allotrope foundation vanderwall_and_little_bio_it_world_2016OSTHUS
Allotrope Foundation is building a framework (a software toolkit) to embed a set of federated, public, non-proprietary standards for analytical data in software utilized throughout the entire analytical chemistry data lifecycle, and serves as a basis for providing controlled vocabularies and taxonomies for a variety of pharmaceutical and biotech R&D applications. This framework provides extended capabilities to build in business rules and other analytics on top of the standardized vocabularies allowing companies enhanced abilities to classify and manage their data. Legacy systems can be maintained more easily and new technologies including cloud databases, Big Data Analytics, or reasoning engines can be employed to allow researchers unprecedented access to important contextualized data, because the foundational class structure is common and highly extensible to new and expanding domains. We will briefly describe some of the current data integration and management challenges facing the industry, e.g., utilization of legacy data warehouses, the creation of new data lakes, integration of existing semantic models, cloud-scale applications and how the Allotrope Framework provides a semantic basis for improved metadata and master data management through the use of modularized semantic models that capture the most pertinent entities, attributes and relationships needed to capture the plethora of laboratory data. We will provide an update on the rapid progress of development and the release of the Allotrope Framework 1.0, including: the Allotrope Data Format (for data and semantically-described metadata), Allotrope Taxonomies, and the first release of APIs (application programming interfaces), and how Allotrope Member companies have begun to integrate these into their internal environments. We will then discuss some of the potential extensions of this framework, which in the future, could enable state-of-the-art data integration and analytics capabilities for various applications.
Reinventing Laboratory Data To Be Bigger, Smarter & FasterOSTHUS
• Big Data technologies, especially Data Lakes are spreading across many industries at the moment with the hopes that they will provide unprecedented capabilities for data integration and data analytics
• In spite of the popularity and promise of these technology approaches, many early adopters are not seeking immediate solutions to their complex problems. Answers are not simply appearing – this talk will explore this issue more thoroughly
• Of the 4 V’s of Big Data, Data Variety and Data Veracity (uncertainty) are of increasing importance. These can cause barriers to successful integration strategies , which, in turn, can lead to poorly performing analytics.
• The problems of Variety and Veracity can be tackled using a new form of Data Science which combines formal ontologies with statistical heuristics. This talk will explore some key features of these approaches and how they can be developed together in symbiosis – leading to complex models that allow for improved analytics – or as we call it Big Analysis.
• The end result is improved capture of data types/sources, from laboratory instrument data, to clinical data, to regulatory rules & submissions, all the way to business drivers for the enterprise. In the end providing advanced analytics capabilities that can be built as modules and expand across an enterprise.
IQPC’s 5th Forum on Laboratory Informatics will provide strategies for overcoming challenges, including:
- In-depth regulatory compliance guidance
- Extensive ELN deployment and roll out projects, focusing on ROI maximization and impact on business performance
- Informatics systems in the biobanking environment
- Proactive approaches to address challenges of integration and interfacing
- Integrating and embracing knowledge management and social media tools
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...OSTHUS
During SmartLab Exchange 2015, Allotrope Foundation and OSTHUS presented the latest update on the Allotrope Framework. To learn more, please view the slides below.
Presented by:
Dana Vanderwall, BMS Research IT & Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS
Semantics for integrated laboratory analytical processes - The Allotrope Pers...OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats,
which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, the Allotrope Foundation develops a
comprehensive and innovative Framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its lifecycle. The
talk describes how laboratory data and their semantic metadata descriptions are brought together to ease the management of vast amount of data that underpin almost
every aspect of drug discovery and development.
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...OSTHUS
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, Allotrope Foundation is developing a comprehensive and innovative framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its life cycle. In this talk we describe how laboratory data and semantic metadata descriptions are brought together to ease the management of a vast amount of data that underpins almost every aspect of drug discovery and development.
Allotrope foundation vanderwall_and_little_bio_it_world_2016OSTHUS
Allotrope Foundation is building a framework (a software toolkit) to embed a set of federated, public, non-proprietary standards for analytical data in software utilized throughout the entire analytical chemistry data lifecycle, and serves as a basis for providing controlled vocabularies and taxonomies for a variety of pharmaceutical and biotech R&D applications. This framework provides extended capabilities to build in business rules and other analytics on top of the standardized vocabularies allowing companies enhanced abilities to classify and manage their data. Legacy systems can be maintained more easily and new technologies including cloud databases, Big Data Analytics, or reasoning engines can be employed to allow researchers unprecedented access to important contextualized data, because the foundational class structure is common and highly extensible to new and expanding domains. We will briefly describe some of the current data integration and management challenges facing the industry, e.g., utilization of legacy data warehouses, the creation of new data lakes, integration of existing semantic models, cloud-scale applications and how the Allotrope Framework provides a semantic basis for improved metadata and master data management through the use of modularized semantic models that capture the most pertinent entities, attributes and relationships needed to capture the plethora of laboratory data. We will provide an update on the rapid progress of development and the release of the Allotrope Framework 1.0, including: the Allotrope Data Format (for data and semantically-described metadata), Allotrope Taxonomies, and the first release of APIs (application programming interfaces), and how Allotrope Member companies have begun to integrate these into their internal environments. We will then discuss some of the potential extensions of this framework, which in the future, could enable state-of-the-art data integration and analytics capabilities for various applications.
Reinventing Laboratory Data To Be Bigger, Smarter & FasterOSTHUS
• Big Data technologies, especially Data Lakes are spreading across many industries at the moment with the hopes that they will provide unprecedented capabilities for data integration and data analytics
• In spite of the popularity and promise of these technology approaches, many early adopters are not seeking immediate solutions to their complex problems. Answers are not simply appearing – this talk will explore this issue more thoroughly
• Of the 4 V’s of Big Data, Data Variety and Data Veracity (uncertainty) are of increasing importance. These can cause barriers to successful integration strategies , which, in turn, can lead to poorly performing analytics.
• The problems of Variety and Veracity can be tackled using a new form of Data Science which combines formal ontologies with statistical heuristics. This talk will explore some key features of these approaches and how they can be developed together in symbiosis – leading to complex models that allow for improved analytics – or as we call it Big Analysis.
• The end result is improved capture of data types/sources, from laboratory instrument data, to clinical data, to regulatory rules & submissions, all the way to business drivers for the enterprise. In the end providing advanced analytics capabilities that can be built as modules and expand across an enterprise.
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...OSTHUS
The Allotrope Foundation is a consortium of major pharmaceutical companies and a partner network whose goal is to address challenges in the pharmaceutical industry by providing a set of public, non-proprietary standards for using and integrating analytical laboratory data. Current challenges in data management within the pharmaceutical industry often center around inconsistent or incomplete data and metadata and proprietary data formats. Because of a lack of standardization, several operations (e.g. integration of instruments/applications, transfer of methods or results, archiving for regulatory purposes) require unnecessary efforts. Further, higher level aggregation of data, e.g. regulatory filings, that are derived from multiple sources of laboratory data are costly to create. These unnecessary costs impact operations within a company’s laboratories, between partnering companies, and between a company and contract research organizations (CROs). Finally, the accelerating transition of laboratories from hybrid (paper + electronic) to purely electronic data streams, coupled with an ever-increasing regulatory scrutiny of electronic data management practices, further require a comprehensive solution. This talk will discuss how The Allotrope Foundation is providing a new framework for data standards through collaboration between numerous stakeholders.
Automated and Explainable Deep Learning for Clinical Language Understanding a...Databricks
Unstructured free-text medical notes are the only source for many critical facts in healthcare. As a result, accurate natural language processing is a critical component of many healthcare AI applications like clinical decision support, clinical pathway recommendation, cohort selection, patient risk or abnormality detection.
The OntoChem IT Solutions GmbH ...
... was founded in 2015 as a purely IT-oriented offshoot of the OntoChem GmbH. Even before we had many years of experience and it has always been our mission to provide added value to our customers by helping them to navigate today’s complex information world by developing cognitive computing solutions, indexing intranet and internet data and applying semantic search solutions for pharmaceutical, material sciences and technology driven businesses.
We strive to support our customers with the most useful tools for knowledge discovery possible, encompassing up-to-date data sources, optimized ontologies and high-throughput semantic document processing and annotation techniques.
We create new knowledge from structured and unstructured data by extracting relationships thereby exploiting the full potential of full-text documents & databases while also scanning social media, news flows and analyzing web-pages.
We aim at an unprecedented, machine understanding of text and subsequent knowledge extraction and inference. The application of our methods towards chemical compounds and their properties supports our customers in generating intellectual property and their use as novel therapeutics, agrochemical products, nutraceuticals, cosmetics and in the field of novel materials.
It's our mission to provide added value to customers by:
developing and applying cognitive computing solutions
creating intranet and internet data indexing and semantic search solutions
Big Data analytics for technology driven businesses
supporting product development and surveillance.
We deliver useful tools for knowledge discovery for:
creating background knowledge ontologies
high-throughput semantic document processing and annotation
knowledge mining by extracting relationships
exploiting the full potential of full-text documents & databases while also scanning social media, news flows and analyzing web-pages.
ICIC 2013 Conference Proceedings Sebastian RadestockDr. Haxel Consult
Making hidden data discoverable: How to build effective drug discovery engines?
Sebastian Radestock (Elsevier, Germany)
In a complex IT environment comprising dozens if not hundreds of databases and likely as many user interfaces it becomes difficult if not impossible to find all the relevant information needed to make informed decisions. Historical data get lost, not normalized data cannot be compared and maintenance becomes a nightmare. We will discuss a new approach to address this issue by showing various examples and use cases on how in-house data and public data can be integrated in various ways to address the unique and individual needs of companies to keep the competitive edge.
Fairification experience clarifying the semantics of data matricesPistoia Alliance
This webinar presents the Statistics Ontology, STATO which is a semantic framework to support the creation of standardized analysis reports to help with review of results in the form of data matrices. STATO includes a hierarchy of classes and a vocabulary for annotating statistical methods used in life, natural and biomedical sciences investigations, text mining and statistical analyses.
Case Study Life Sciences Data: Central for Integrative Systems Biology and Bi...sesrdm
Presentation by Dr Sarah Butcher, Imperial College London at Science and Engineering South (SES) Event - Helping Researchers Manage their Data - Friday 9th May 2014 held at Imperial College London
Application of recently developed FAIR metrics to the ELIXIR Core Data ResourcesPistoia Alliance
The FAIR (Findable, Accessible, Interoperable and Reusable) principles aim to maximize the discovery and reuse of digital resources. Using recently developed software and metrics to assess FAIRness and supported through an ELIXIR Implementation Study, Michel worked with a subset of ELIXIR Core Data Resources to apply these technologies. In this webinar, he will discuss their approach, findings, and lessons learned towards the understanding and promotion of the FAIR principles.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
Labmatrix is a software application that manages the operational aspects of collaborative clinical and translational research programs, including patient recruiting, consenting, sample management (biobanking), experimental characterization of the samples and tracking of patient clinical profiles.
Why ICT Fails in Healthcare: Software Maintenance and MaintainabilityKoray Atalag
This presentation was for a SERG seminar at the University of Auckland Department of Computer Science. I present why software maintenance is a barrier for adoption of IT in healthcare and the maintainability aspects based on ISO/IEC 9126 software quality standard quality model. I then present the preliminary results of my research here.
Open interoperability standards, tools and services at EMBL-EBIPistoia Alliance
In this webinar Dr Henriette Harmse from EMBL-EBI presents how they are using their ontology services at EMBL-EBI to scale up the annotation of data and deliver added value through ontologies and semantics to their users.
Clinical modelling with openEHR ArchetypesKoray Atalag
This is the prezo I used in CellML workshop in Waiheke Island, Auckland, New Zealand on 14 April 2015. The aim was to introduce information modelling with openEHR and how to achieve semantic interoperability by using shared ontologies and clinical terminology.
Dissemination Patterns of Technical Knowledge in the IR Industry: Scientometric Analysis of Citations in IR-related Patents
Ricardo Eito-Brun (Universidad Carlos III de Madrid, Spain)
The purpose of this paper is to identify the most influential institutions and journals on information retrieval and text mining through the analysis of the citations in the patents issued in the period between 1990 and 2013.
Bibliographic citations received by different academic journals in a representative set of patents related to the text mining area are analyzed applying sound and consolidated statistical techniques. Besides identifying the most influential academic journals, conferences and institutions in the period under study, the conclusions of this research are also useful to identify the most relevant and productive organizations (those with a higher number of patents) and those organizations whose patents have received a major number of citations.
The analysis also permits to obtain a general view of the disseminations patterns in the consumption of the products of academic and technical research. The period under study offers interesting conclusions regarding the impact of the Web on the Intellectual Property Rights (IPR) strategies of companies building Information Retrieval and Text Mining software solutions, and how the information retrieval and access industry has evolved in the recent years.
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...OSTHUS
The Allotrope Foundation is a consortium of major pharmaceutical companies and a partner network whose goal is to address challenges in the pharmaceutical industry by providing a set of public, non-proprietary standards for using and integrating analytical laboratory data. Current challenges in data management within the pharmaceutical industry often center around inconsistent or incomplete data and metadata and proprietary data formats. Because of a lack of standardization, several operations (e.g. integration of instruments/applications, transfer of methods or results, archiving for regulatory purposes) require unnecessary efforts. Further, higher level aggregation of data, e.g. regulatory filings, that are derived from multiple sources of laboratory data are costly to create. These unnecessary costs impact operations within a company’s laboratories, between partnering companies, and between a company and contract research organizations (CROs). Finally, the accelerating transition of laboratories from hybrid (paper + electronic) to purely electronic data streams, coupled with an ever-increasing regulatory scrutiny of electronic data management practices, further require a comprehensive solution. This talk will discuss how The Allotrope Foundation is providing a new framework for data standards through collaboration between numerous stakeholders.
Automated and Explainable Deep Learning for Clinical Language Understanding a...Databricks
Unstructured free-text medical notes are the only source for many critical facts in healthcare. As a result, accurate natural language processing is a critical component of many healthcare AI applications like clinical decision support, clinical pathway recommendation, cohort selection, patient risk or abnormality detection.
The OntoChem IT Solutions GmbH ...
... was founded in 2015 as a purely IT-oriented offshoot of the OntoChem GmbH. Even before we had many years of experience and it has always been our mission to provide added value to our customers by helping them to navigate today’s complex information world by developing cognitive computing solutions, indexing intranet and internet data and applying semantic search solutions for pharmaceutical, material sciences and technology driven businesses.
We strive to support our customers with the most useful tools for knowledge discovery possible, encompassing up-to-date data sources, optimized ontologies and high-throughput semantic document processing and annotation techniques.
We create new knowledge from structured and unstructured data by extracting relationships thereby exploiting the full potential of full-text documents & databases while also scanning social media, news flows and analyzing web-pages.
We aim at an unprecedented, machine understanding of text and subsequent knowledge extraction and inference. The application of our methods towards chemical compounds and their properties supports our customers in generating intellectual property and their use as novel therapeutics, agrochemical products, nutraceuticals, cosmetics and in the field of novel materials.
It's our mission to provide added value to customers by:
developing and applying cognitive computing solutions
creating intranet and internet data indexing and semantic search solutions
Big Data analytics for technology driven businesses
supporting product development and surveillance.
We deliver useful tools for knowledge discovery for:
creating background knowledge ontologies
high-throughput semantic document processing and annotation
knowledge mining by extracting relationships
exploiting the full potential of full-text documents & databases while also scanning social media, news flows and analyzing web-pages.
ICIC 2013 Conference Proceedings Sebastian RadestockDr. Haxel Consult
Making hidden data discoverable: How to build effective drug discovery engines?
Sebastian Radestock (Elsevier, Germany)
In a complex IT environment comprising dozens if not hundreds of databases and likely as many user interfaces it becomes difficult if not impossible to find all the relevant information needed to make informed decisions. Historical data get lost, not normalized data cannot be compared and maintenance becomes a nightmare. We will discuss a new approach to address this issue by showing various examples and use cases on how in-house data and public data can be integrated in various ways to address the unique and individual needs of companies to keep the competitive edge.
Fairification experience clarifying the semantics of data matricesPistoia Alliance
This webinar presents the Statistics Ontology, STATO which is a semantic framework to support the creation of standardized analysis reports to help with review of results in the form of data matrices. STATO includes a hierarchy of classes and a vocabulary for annotating statistical methods used in life, natural and biomedical sciences investigations, text mining and statistical analyses.
Case Study Life Sciences Data: Central for Integrative Systems Biology and Bi...sesrdm
Presentation by Dr Sarah Butcher, Imperial College London at Science and Engineering South (SES) Event - Helping Researchers Manage their Data - Friday 9th May 2014 held at Imperial College London
Application of recently developed FAIR metrics to the ELIXIR Core Data ResourcesPistoia Alliance
The FAIR (Findable, Accessible, Interoperable and Reusable) principles aim to maximize the discovery and reuse of digital resources. Using recently developed software and metrics to assess FAIRness and supported through an ELIXIR Implementation Study, Michel worked with a subset of ELIXIR Core Data Resources to apply these technologies. In this webinar, he will discuss their approach, findings, and lessons learned towards the understanding and promotion of the FAIR principles.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Modern medicine needs methods to enable access to data,
captured during health care, for research, surveillance,
decision support and other reuse purposes. Initiatives like the
National Patient Centered Clinical Research Network in the
US and the Electronic Health Records for Clinical Research
in the EU are facilitating the reuse of Electronic Health
Record (EHR) data for clinical research. One of the barriers
for data reuse is the integration and interoperability of
different Healthcare Information Systems (HIS). The reason is
the differences among the HIS information and terminology
models. The use of EHR standards like openEHR can alleviate
these barriers providing a standard, unambiguous,
semantically enriched representation of clinical data to
enable semantic interoperability and data integration. Few
works have been published describing how to drive
proprietary data stored in EHRs into standard openEHR
repositories. This tutorial provides an overview of the key
concepts, tools and techniques necessary to implement an
openEHR-based Data Warehouse (DW) environment to reuse
clinical data. We aim to provide insights into data extraction
from proprietary sources, transformation into openEHR
compliant instances to populate a standard repository and
enable access to it using standard query languages and
services
Labmatrix is a software application that manages the operational aspects of collaborative clinical and translational research programs, including patient recruiting, consenting, sample management (biobanking), experimental characterization of the samples and tracking of patient clinical profiles.
Why ICT Fails in Healthcare: Software Maintenance and MaintainabilityKoray Atalag
This presentation was for a SERG seminar at the University of Auckland Department of Computer Science. I present why software maintenance is a barrier for adoption of IT in healthcare and the maintainability aspects based on ISO/IEC 9126 software quality standard quality model. I then present the preliminary results of my research here.
Open interoperability standards, tools and services at EMBL-EBIPistoia Alliance
In this webinar Dr Henriette Harmse from EMBL-EBI presents how they are using their ontology services at EMBL-EBI to scale up the annotation of data and deliver added value through ontologies and semantics to their users.
Clinical modelling with openEHR ArchetypesKoray Atalag
This is the prezo I used in CellML workshop in Waiheke Island, Auckland, New Zealand on 14 April 2015. The aim was to introduce information modelling with openEHR and how to achieve semantic interoperability by using shared ontologies and clinical terminology.
Dissemination Patterns of Technical Knowledge in the IR Industry: Scientometric Analysis of Citations in IR-related Patents
Ricardo Eito-Brun (Universidad Carlos III de Madrid, Spain)
The purpose of this paper is to identify the most influential institutions and journals on information retrieval and text mining through the analysis of the citations in the patents issued in the period between 1990 and 2013.
Bibliographic citations received by different academic journals in a representative set of patents related to the text mining area are analyzed applying sound and consolidated statistical techniques. Besides identifying the most influential academic journals, conferences and institutions in the period under study, the conclusions of this research are also useful to identify the most relevant and productive organizations (those with a higher number of patents) and those organizations whose patents have received a major number of citations.
The analysis also permits to obtain a general view of the disseminations patterns in the consumption of the products of academic and technical research. The period under study offers interesting conclusions regarding the impact of the Web on the Intellectual Property Rights (IPR) strategies of companies building Information Retrieval and Text Mining software solutions, and how the information retrieval and access industry has evolved in the recent years.
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysNEWYORKSYS-IT SOLUTIONS
NEWYORKSYSTRAINING are destined to offer quality IT online training and comprehensive IT consulting services with complete business service delivery orientation.
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/Bvmvc9
Data prep and data blending are terms that have come to prominence over the last year or two. On the surface, they appear to offer functionality similar to data virtualization…but there are important differences!
In this session, you will learn:
• How data virtualization complements or contrasts technologies such as data prep and data blending
• Pros and cons of functionality provided by data prep, data catalog and data blending tools
• When and how to use these different technologies to be most effective
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
ACL Software is a powerful product yet many users are concerned it is difficult to start and therefore, may never effectively maximize the product. If you fall into this category or just want to learn from one of the top industry experts in ACL Software (over 20 years experience), this course will provide the key learning blocks to get started quickly auditing three top audit areas for data analytics.
Using a live/video training library approach, we help companies of all sizes use audit and assurance software to improve business intelligence, increase efficiencies, identify fraud, test controls, and bottom line savings.
AuditNet and Cash Recovery Partners Webinar recording available at auditsoftwarevideos.com and AuditNet.tv (registration required) Recording free to view.
Sample Data Files for All Courses are available for $49
To purchase access to all sample data files, Excel macros and ACL scripts associated with the free training visit AuditSoftwareVideos.
Denodo’s Data Catalog: Bridging the Gap between Data and Business (APAC)Denodo
Watch full webinar here: https://bit.ly/3nxGFam
Self service is a major goal of modern data strategists. Denodo’s data catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It’s the perfect companion for a virtual layer to fully empower those self service initiatives with minimal IT intervention. It provides business users with the tool to generate their own insights with proper security, governance and guardrails.
In this session you will learn about:
- The role of a virtual semantic layer in self service initiatives
- What are the key capabilities of Denodo’s new Data Catalog
- Best practices and advanced tips for a successful deployment
- How customers are using the Denodo’s Data Catalog to enable self-service initiatives
This powerpoint slide deck is the presentation given at the Microsoft center in Waltham, MA titled Leading Practices and Insights for Managing Data Integration Initiatives.
Topics covered include:
Key Drivers
Approaches and Strategy
Tools and Products
Useful Case Studies
Success Factors
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
Integrating SIS’s with Salesforce: An Accidental Integrator’s GuideSalesforce.org
Join our next Success webinar, Integrating Student Information Systems with Salesforce: Strategies and Best Practices, to explore the many ways system integration benefits your school. Whether you want an aggregated view of your students, the ability to trigger actions based on status changes, or the automation of manual work, you will learn the three simple steps to successful integration. By highlighting how higher education institutions have integrated with the most popular Student Information Systems, Grant Miller, director of Alliances and Jill Kenney, Director of Sales Engineering at the Salesforce Foundation, will explain the layers of integration and discuss considerations like synchronous-versus-asynchronous and buy-versus-build options.
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA
Curtis ODell, Global Director Data Integrity at Tricentis
Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management tools—one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented.
Key Learning Objective
1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance
2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage
3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this.
4. How this approach has impact in your vertical
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
Informatica is the global giant in data warehousing solutions, which offers data integration tools for enterprise. Informatica is a proven solution for enterprise and have set standards for highly scalable and high-performance solutions.
Similar to OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015 (20)
Current labs can greatly benefit from a digital transformation.
FAIR data principles are crucial in this process.
Laying a solid data governance foundation is an invaluable long-term move.
Challenges & Opportunities of Implementation FAIR in Life SciencesOSTHUS
Speak in common terms – identify Business Outcomes (value) as well as technology
Don’t say “semantics”, “FAIR”, “ontologies”, etc. – talk about outcomes and results
Drive projects through results – QUICK WINS
Identify the right data – build off of that (evolution not revolution)
Think about legacy systems, provenance, governance, stewardship, etc. – have answers to the nay-sayers.
Be honest what this will do and what it won’t
ROI – have this in mind (Business Value not Tech Value)
Cost savings (reduced hours, faster search, accurate reporting, better visibility, etc.)
Risk Mitigation (improved regulatory, corporate knowledge vs. indivual, M&A, etc.)
Innovation (what is the value to being a thought leader?)
This talk will provide a means to discuss the capture, integration and dissemination of data across large enterprises. We will show how data variety is continuing to grow, meaning new data sources are steadily becoming available for use in analysis. Data veracity is also of importance since a large amount of data is fuzzy (uncertain) in nature. The ability to integrate these various data sources and provide improved capabilities to understand and use it is of increasing importance in today’s pharma climate. We call this Reference Master Data Management (RMDM).
This talk will span an arc of data lifecycle management, beginning with instrument data, moving across to clinical studies, production, regulatory affairs and finally e-archiving (see Fig. 1). I will show how these systems can use a common semantics for modeling of important metadata, which can apply the FAIR principles of Findability, Accessibility, Interoperability and Reusability to a common “semantic hub” that can connect data sources of different varieties across the enterprise. ADF files, for example, use their Data Description layer to provide semantic metadata about file contents. Similarly, semantics can be used to describe clinical trials data, regulatory data, etc., through to archiving, for improved storage and search over long periods of time.
From allotrope to reference master data management OSTHUS
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
• Improve Data Management with Semantic Data Integration
• Discuss the issues of data variety and data uncertainty
• Moving from Big Data to Big Analysis
• How to apply Analysis to Big Data (Big Analysis)
• Benefits of Advanced Analytics in Life Science
Why Data is Becoming the Most Valuable Asset Companies PossesOSTHUS
The world is changing at a rapid pace. New varieties of data continue to spring up and be made available for integration and knowledge improvement across many domains. Knowledge engineering, using advanced techniques in data science, is therefore moving to the forefront of technology and IT concerns at many companies. We see this in the expansion of cloud technologies, semantic technologies, data analytics, and the construction of Data Lakes. Understanding one’s data and being able to derive complex patterns of interest from across a multitude of different data sources (public and private) should be of paramount concern for companies in the pharmaceutical, crop science and life science industries. Companies who embrace knowledge engineering practices will possess a distinct advantage in the coming years due to their ability to integrate and use data to their advantage. This talk will discuss recent trends in data science and will highlight some of the main points to consider for taking advantage of these new technologies and approaches. We will also cover certain lessons learned from real-world industry use cases to highlight how people are using these technologies for improved business benefits.
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...OSTHUS
In our webinar on Jan 17th, 2017, Eric and Heiner gave attendees insights on the following:
1. What semantics are (model/data separation, graphs, apply better meaning to data, etc.)
2. Why you should consider using these technologies (real world examples of benefits our customers are seeing)
3. How to pick the right tech for your needs (provide a description of the types of graph/RDF stores out there – we have a matrix based on features – and show how various SPARQL queries work against legacy data.)
Why paperless lab is just the first step towards a smart labOSTHUS
Life science’s main asset is its data. Data forms the basis of scientific decision making and its availability via electronic systems is a prerequisite for collaborative work and successful innovation. While more data is published as linked (open) data, huge amounts of data remain unused in internal data silos, such as various ELN’s, because of substantial integration efforts and data quality issues. Since the overwhelming amount of data is unstructured, information extraction and corresponding classification and semantic labeling of content is required. To generate value from your ELN data, a solid informatics strategy is needed to ensure data quality and streamline analytics. Semantic technologies are key enabler to overcome existing limitations.
Current challenges facing the implementation of NoSQL-type databases involve how to use advanced rule-based analytics on large tables and key value stores, where metadata is often sparse. Graph databases or triple stores are great for utilizing one’s metadata, but are often computationally inefficient compared to NoSQL stores. To combat this problem, Modus Operandi will showcase a Predicate Store inside of its MOVIA product that can run advanced, first-order level, logical rule sets and queries against large tables or column stores directly to provide a scalable, rapid and advanced data analytics for cloud applications. This provides graph complexity in terms of content with the performance and scalability of NoSQL data approaches. The system also allows for both statistical algorithms as well as logic-based rule sets to be run concurrently, meaning that a host of parallel analytics can be run at once, providing deep analysis over a multitude of important pattern types.
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.
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.”
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.
The affect of service quality and online reviews on customer loyalty in the E...
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
1. Building your laboratory
informatics strategy
The benefit of reference architectures &
data standardization
Wolfgang Colsman, OSTHUS
Dana Vanderwall, Bristol-Myers Squibb
2. Slide 2
Abstract
Building your laboratory informatics strategy:
The benefit of reference architectures & data standardization
Modern laboratory processes have to deal with a multitude of
data sources originating from different instruments, systems,
sites and external resources. As a consequence data analytics is
severely limited by incomplete or inconsistent metadata and
different data formats. This complexity leads to inefficient
processes and high costs due to insufficient data integration and
accessibility.
Showing different use cases, we will present a successful data
and systems integration approach using reference architectures
and data standardization, resulting in increased cost efficiency
and improved decision making.
5. Slide 5
Instrument
Instrument
Why:
Example 1
LIMS
Data Mart
Instrument
JMP
CRO
CSV Paper
Scripts
Data Cleansing and
Controlled Vocabularies
build into code
PDF
Method Transfer,
Manual Transcription
Data Incomplete,
Formatting, …
Manual Transcription,
Missing Context
7. Slide 7
Best of breed did not work in the past because of the lack of
standardization, but do we agree:
1. Everybody should do what he can do best?
2. Anybody should be able to talk to everybody?
Why:
Thesis
9. Slide 9
What should a Reference Architecture look like:
Lab Integration Requirements
ELN
CRO
CMOMDM
IMS
LES
HR
CDS
Data
Archive
Data Mining
Data Mart
SDMS
DMS
LIMSInstrument
Data
Warehouse
Registration
Controlled
Vocabularies
Data
Analytics
Predictive
Modeling
ERP
MES
Departments
Sites
10. Slide 10
Data Acquisition
Data Analytics Data Management
Master DataLab Workflow
Collaboration
What should a Reference Architecture look like:
Lab Integration Requirements
ELN MDMCDS
DMS
Instrument
CRO
Data
Warehouse
Data Mart
Data Mining
CMO
LIMS
LES
SDMS
Data
Archive
HR
IMS
Controlled
Vocabularies
Data
Analytics
Predictive
Modeling
Manufacturing
ERP
MES
LIMS
Departments
Sites
Registration
11. Slide 11
Data Acquisition:
CDS / Instrument
Data Analytics:
Data Warehouse / Data Mart / Data Mining / Data Analytics / Predictive Modeling
Data Management:
DMS / SDMS / Data Archive
MasterData:
MDM/HR/IMS/Registration/Contr.Vocab.
Lab Workflow & Manufacturing:
ELN / LIMS / LES / MES / ERP
What should a Reference Architecture look like:
Lab Integration Requirements
Collaboration:
CRO / CMO
12. Slide 12
What should a Reference Architecture look like:
Two Worlds of Workflow
Lab Workflow
Experiment Report
Data Analytics
Data Knowledge
Where is my Data?
13. Slide 13
Data Analytics
Data Knowledge
Where is my Data?
What should a Reference Architecture look like:
Pain Points
Lab Workflow
Experiment Report
Document Preparation
•Finding data
•Copy/paste
•Transcribe/convert
•Combine multiple sources
Data Management & Archiving
•Searching/Finding data
•Data format conversion
•Data migration
•Maintenance and/or unavailability
of legacy systems
Errors
•Manual text entry or transcription
•Manual calculations
•Wrong or missing metadata
•Need to reprocess data
Data Exchange
•Disparate data file formats
•Manual transcriptions
•Added cost & complexity to
CROs, CMOs, partnerships
Regulatory Compliance
•Instrument & software validation
•SOPs
•System documentation
•Supporting questions/investigations
(CAPA)
Extracting Knowledge &
Value from Data
•Speed to answer/decision
•Data silos
•Constrained innovation
•Limited data mining & analytics
14. Slide 14
What should a Reference Architecture look like:
Root Cause
Lab Workflow
Experiment Report
Data Analytics
Data Knowledge
• Patchwork of software, helper applications, persistent gaps
• Lack of standard data file formats
• Lack of standard software interfaces (APIs)
• Lack of standard for metadata (the who, what, where, when, why, how)
15. Slide 15
What should a Reference Architecture look like:
Solution
Lab Workflow
Experiment Report
Data Analytics
Data Knowledge
• Open Document Standards
• Reusable Software Components and APIs
• Metadata Repository
16. Slide 16
Lab Workflow
Data Analytics
Reference Architecture & Data Standards
Data Management
Dashboards Metadata Browser Data Viewer
Plan
Analysis
Prepare
Samples
Submit
Samples
Acquire
Data
Process
Data
Store
Data
Analyze
Data
Reports
Results
Taxonomies Methods Instruments Samples Experiments Results Data
Allotrope Data Format
Allotrope Metadata
Taxonomies
Allotrope Class
Libraries and APIs
Forecasting
& Capacity Planning
Request Management
& Tracking
Collaboration
& Distribution
17. Slide 17
APN webinars and information exchange with specific APN
members:
12 March 2015 APN Partner Led Committee Workshop, New
Orleans, LA
13 March 2015 APN General Meeting & Workshop, New
Orleans, LA
24 April 2015 Cross Industry Workshop, Cambridge, MA
15 Sept 2015 APN Workshop, Chicago, IL
16 Sept 2015 Cross Industry Workshop, Chicago, IL
Next Steps