際際滷shows by User: MelanieGrupe / http://www.slideshare.net/images/logo.gif 際際滷shows by User: MelanieGrupe / Wed, 02 Sep 2020 12:27:46 GMT 際際滷Share feed for 際際滷shows by User: MelanieGrupe The Fast Track to Fair Lab Data /slideshow/the-fast-track-to-fair-lab-data/238371976 osthus-fasttracktofairlabdata-final-200902122746
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.]]>
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.]]> Wed, 02 Sep 2020 12:27:46 GMT /slideshow/the-fast-track-to-fair-lab-data/238371976 MelanieGrupe@slideshare.net(MelanieGrupe) The Fast Track to Fair Lab Data MelanieGrupe 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/osthus-fasttracktofairlabdata-final-200902122746-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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.
The Fast Track to Fair Lab Data from OSTHUS
]]> 150 0 https://cdn.slidesharecdn.com/ss_thumbnails/osthus-fasttracktofairlabdata-final-200902122746-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Challenges & Opportunities of Implementation FAIR in Life Sciences /slideshow/challenges-opportunities-of-implementation-fair-in-life-sciences/101095106 fairdatawsutrecht002-180607083152
Speak in common terms identify Business Outcomes (value) as well as technology Dont 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 wont 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?) ]]>
Speak in common terms identify Business Outcomes (value) as well as technology Dont 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 wont 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?) ]]> Thu, 07 Jun 2018 08:31:52 GMT /slideshow/challenges-opportunities-of-implementation-fair-in-life-sciences/101095106 MelanieGrupe@slideshare.net(MelanieGrupe) Challenges & Opportunities of Implementation FAIR in Life Sciences MelanieGrupe Speak in common terms identify Business Outcomes (value) as well as technology Dont 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 wont 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?) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fairdatawsutrecht002-180607083152-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Speak in common terms identify Business Outcomes (value) as well as technology Dont 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 wont 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&amp;A, etc.) Innovation (what is the value to being a thought leader?)
Challenges & Opportunities of Implementation FAIR in Life Sciences from OSTHUS
]]> 278 5 https://cdn.slidesharecdn.com/ss_thumbnails/fairdatawsutrecht002-180607083152-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data lifecycle mgt across the enterprise /slideshow/data-lifecycle-mgt-across-the-enterprise/91937614 datalifecyclemgtacrosstheenterprise-180326104655
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 todays 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.]]>
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 todays 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.]]> Mon, 26 Mar 2018 10:46:55 GMT /slideshow/data-lifecycle-mgt-across-the-enterprise/91937614 MelanieGrupe@slideshare.net(MelanieGrupe) Data lifecycle mgt across the enterprise MelanieGrupe 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 todays 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datalifecyclemgtacrosstheenterprise-180326104655-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 todays 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.
Data lifecycle mgt across the enterprise from OSTHUS
]]> 360 1 https://cdn.slidesharecdn.com/ss_thumbnails/datalifecyclemgtacrosstheenterprise-180326104655-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 From allotrope to reference master data management /slideshow/from-allotrope-to-reference-master-data-management/91937213 fromallotropetoreferencemasterdatamanagementwstalk-180326104146
We will present the updated Allotrope framework and cover .adf files and how they are used. Well demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). Well 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 well show scalability of this approach using data science techniques can be employed beyond just the metadata we refer to this as Big Analysis. ]]>
We will present the updated Allotrope framework and cover .adf files and how they are used. Well demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). Well 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 well show scalability of this approach using data science techniques can be employed beyond just the metadata we refer to this as Big Analysis. ]]> Mon, 26 Mar 2018 10:41:45 GMT /slideshow/from-allotrope-to-reference-master-data-management/91937213 MelanieGrupe@slideshare.net(MelanieGrupe) From allotrope to reference master data management MelanieGrupe We will present the updated Allotrope framework and cover .adf files and how they are used. Well demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). Well 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 well show scalability of this approach using data science techniques can be employed beyond just the metadata we refer to this as Big Analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fromallotropetoreferencemasterdatamanagementwstalk-180326104146-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We will present the updated Allotrope framework and cover .adf files and how they are used. Well demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). Well 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 well show scalability of this approach using data science techniques can be employed beyond just the metadata we refer to this as Big Analysis.
From allotrope to reference master data management from OSTHUS
]]> 442 4 https://cdn.slidesharecdn.com/ss_thumbnails/fromallotropetoreferencemasterdatamanagementwstalk-180326104146-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data becomes Big Analysis /slideshow/big-data-becomes-big-analysis/82201154 2017-11-15bigdatabecomesbiganalysisboston2017-171117080552
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 ]]>
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 ]]> Fri, 17 Nov 2017 08:05:52 GMT /slideshow/big-data-becomes-big-analysis/82201154 MelanieGrupe@slideshare.net(MelanieGrupe) Big Data becomes Big Analysis MelanieGrupe 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 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017-11-15bigdatabecomesbiganalysisboston2017-171117080552-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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
Big Data becomes Big Analysis from OSTHUS
]]> 237 2 https://cdn.slidesharecdn.com/ss_thumbnails/2017-11-15bigdatabecomesbiganalysisboston2017-171117080552-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Early AI Adoption Via Advanced Analytics /slideshow/early-ai-adoption-via-advanced-analytics/80979718 aiconferencemiami10-18-17002-171019113142
Eric Little, CDO at OSTHUS about "Early AI Adoption Via Advanced Analytics"]]>
Eric Little, CDO at OSTHUS about "Early AI Adoption Via Advanced Analytics"]]> Thu, 19 Oct 2017 11:31:42 GMT /slideshow/early-ai-adoption-via-advanced-analytics/80979718 MelanieGrupe@slideshare.net(MelanieGrupe) Early AI Adoption Via Advanced Analytics MelanieGrupe Eric Little, CDO at OSTHUS about "Early AI Adoption Via Advanced Analytics" <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiconferencemiami10-18-17002-171019113142-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Eric Little, CDO at OSTHUS about &quot;Early AI Adoption Via Advanced Analytics&quot;
Early AI Adoption Via Advanced Analytics from OSTHUS
]]> 199 2 https://cdn.slidesharecdn.com/ss_thumbnails/aiconferencemiami10-18-17002-171019113142-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Why Data is Becoming the Most Valuable Asset Companies Posses /slideshow/big-analysis-why-data-is-becoming-the-most-valuable-asset-companies-posses/76557962 innovalabtalkmay242017-170601114943
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 ones 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.]]>
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 ones 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.]]> Thu, 01 Jun 2017 11:49:43 GMT /slideshow/big-analysis-why-data-is-becoming-the-most-valuable-asset-companies-posses/76557962 MelanieGrupe@slideshare.net(MelanieGrupe) Why Data is Becoming the Most Valuable Asset Companies Posses MelanieGrupe 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 ones 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/innovalabtalkmay242017-170601114943-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 ones 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.
Why Data is Becoming the Most Valuable Asset Companies Posses from OSTHUS
]]> 608 4 https://cdn.slidesharecdn.com/ss_thumbnails/innovalabtalkmay242017-170601114943-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Reinventing Laboratory Data To Be Bigger, Smarter & Faster /slideshow/reinventing-laboratory-data-to-be-bigger-smarter-faster/71966596 osthussmartlabberlin2017-170209155235
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 Vs 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. ]]>
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 Vs 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. ]]> Thu, 09 Feb 2017 15:52:35 GMT /slideshow/reinventing-laboratory-data-to-be-bigger-smarter-faster/71966596 MelanieGrupe@slideshare.net(MelanieGrupe) Reinventing Laboratory Data To Be Bigger, Smarter & Faster MelanieGrupe 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 Vs 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/osthussmartlabberlin2017-170209155235-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 Vs 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 &amp; 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.
Reinventing Laboratory Data To Be Bigger, Smarter & Faster from OSTHUS
]]> 232 7 https://cdn.slidesharecdn.com/ss_thumbnails/osthussmartlabberlin2017-170209155235-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry: How Semantic Technology is Helping Drive New Standards for Data Management /slideshow/revolutionizing-laboratory-instrument-data-for-the-pharmaceutical-industry-how-semantic-technology-is-helping-drive-new-standards-for-data-management/71958444 smartdatatalkbayer-osthus-170209123922
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 companys 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. ]]>
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 companys 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. ]]> Thu, 09 Feb 2017 12:39:22 GMT /slideshow/revolutionizing-laboratory-instrument-data-for-the-pharmaceutical-industry-how-semantic-technology-is-helping-drive-new-standards-for-data-management/71958444 MelanieGrupe@slideshare.net(MelanieGrupe) Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry: How Semantic Technology is Helping Drive New Standards for Data Management MelanieGrupe 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 companys 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smartdatatalkbayer-osthus-170209123922-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 companys 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.
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry: How Semantic Technology is Helping Drive New Standards for Data Management from OSTHUS
]]> 1367 12 https://cdn.slidesharecdn.com/ss_thumbnails/smartdatatalkbayer-osthus-170209123922-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Demystifying Semantics:Practical Utilization of Semantic Technologies for Real World Applications /slideshow/demystifying-semanticspractical-utilization-of-semantic-technologies-for-real-world-applications/71133267 2016-01-17osthusknowledgesharingwebinar-170118082923
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.) ]]>
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.) ]]> Wed, 18 Jan 2017 08:29:23 GMT /slideshow/demystifying-semanticspractical-utilization-of-semantic-technologies-for-real-world-applications/71133267 MelanieGrupe@slideshare.net(MelanieGrupe) Demystifying Semantics:Practical Utilization of Semantic Technologies for Real World Applications MelanieGrupe 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.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-01-17osthusknowledgesharingwebinar-170118082923-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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.)
Demystifying Semantics:Practical Utilization of Semantic Technologies for Real World Applications from OSTHUS
]]> 265 3 https://cdn.slidesharecdn.com/ss_thumbnails/2016-01-17osthusknowledgesharingwebinar-170118082923-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Why paperless lab is just the first step towards a smart lab /slideshow/why-paperless-lab-is-just-the-first-step-towards-a-smart-lab/61144792 2016-04-17hopaperless-160420131013
Life sciences 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 ELNs, 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.]]>
Life sciences 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 ELNs, 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.]]> Wed, 20 Apr 2016 13:10:13 GMT /slideshow/why-paperless-lab-is-just-the-first-step-towards-a-smart-lab/61144792 MelanieGrupe@slideshare.net(MelanieGrupe) Why paperless lab is just the first step towards a smart lab MelanieGrupe Life sciences 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 ELNs, 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-04-17hopaperless-160420131013-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Life sciences 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 ELNs, 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.
Why paperless lab is just the first step towards a smart lab from OSTHUS
]]> 602 10 https://cdn.slidesharecdn.com/ss_thumbnails/2016-04-17hopaperless-160420131013-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Allotrope foundation vanderwall_and_little_bio_it_world_2016 /slideshow/allotrope-foundation-vanderwallandlittlebioitworld2016-60596175/60596175 allotropefoundationvanderwallandlittlebioitworld2016-160407072421
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.]]>
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.]]> Thu, 07 Apr 2016 07:24:21 GMT /slideshow/allotrope-foundation-vanderwallandlittlebioitworld2016-60596175/60596175 MelanieGrupe@slideshare.net(MelanieGrupe) Allotrope foundation vanderwall_and_little_bio_it_world_2016 MelanieGrupe 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/allotropefoundationvanderwallandlittlebioitworld2016-160407072421-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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&amp;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.
Allotrope foundation vanderwall_and_little_bio_it_world_2016 from OSTHUS
]]> 708 5 https://cdn.slidesharecdn.com/ss_thumbnails/allotropefoundationvanderwallandlittlebioitworld2016-160407072421-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Smart Data for Smart Labs /slideshow/smart-data-for-smart-labs/58095946 2016-02-03osthusslides-drericlittle-160210112849
OSTHUS VP Data Science Dr. Eric Little talked about Smart Data for Smart Labs at this years SmartLab EU in Munich. ]]>
OSTHUS VP Data Science Dr. Eric Little talked about Smart Data for Smart Labs at this years SmartLab EU in Munich. ]]> Wed, 10 Feb 2016 11:28:49 GMT /slideshow/smart-data-for-smart-labs/58095946 MelanieGrupe@slideshare.net(MelanieGrupe) Smart Data for Smart Labs MelanieGrupe OSTHUS VP Data Science Dr. Eric Little talked about Smart Data for Smart Labs at this years SmartLab EU in Munich. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-02-03osthusslides-drericlittle-160210112849-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> OSTHUS VP Data Science Dr. Eric Little talked about Smart Data for Smart Labs at this years SmartLab EU in Munich.
Smart Data for Smart Labs from OSTHUS
]]> 616 5 https://cdn.slidesharecdn.com/ss_thumbnails/2016-02-03osthusslides-drericlittle-160210112849-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Semantics for Integrated Analytical Laboratory Processes the Allotrope Perspective /slideshow/semantics-for-integrated-analytical-laboratory-processes-the-allotrope-perspective/53003922 allotropeperspectivesemanticsfinal-150921074459-lva1-app6892
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. ]]>
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. ]]> Mon, 21 Sep 2015 07:44:59 GMT /slideshow/semantics-for-integrated-analytical-laboratory-processes-the-allotrope-perspective/53003922 MelanieGrupe@slideshare.net(MelanieGrupe) Semantics for Integrated Analytical Laboratory Processes the Allotrope Perspective MelanieGrupe 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/allotropeperspectivesemanticsfinal-150921074459-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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.
Semantics for Integrated Analytical Laboratory Processes the Allotrope Perspective from OSTHUS
]]> 1021 7 https://cdn.slidesharecdn.com/ss_thumbnails/allotropeperspectivesemanticsfinal-150921074459-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Reasoning over big data /slideshow/reasoning-over-big-data/52164044 reasoningoverbigdata-150828074226-lva1-app6891
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 ones 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.]]>
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 ones 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.]]> Fri, 28 Aug 2015 07:42:26 GMT /slideshow/reasoning-over-big-data/52164044 MelanieGrupe@slideshare.net(MelanieGrupe) Reasoning over big data MelanieGrupe 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 ones 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reasoningoverbigdata-150828074226-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 ones 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.
Reasoning over big data from OSTHUS
]]> 817 8 https://cdn.slidesharecdn.com/ss_thumbnails/reasoningoverbigdata-150828074226-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Semantics for integrated laboratory analytical processes - The Allotrope Perspective /slideshow/semantics-for-integrated-laboratory-analytical-processes-the-allotrope-perspective/50587814 semanticsforintegratedlaboratoryanalyticalprocesses-theallotropeperspective-150716091657-lva1-app6891
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.]]>
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.]]> Thu, 16 Jul 2015 09:16:57 GMT /slideshow/semantics-for-integrated-laboratory-analytical-processes-the-allotrope-perspective/50587814 MelanieGrupe@slideshare.net(MelanieGrupe) Semantics for integrated laboratory analytical processes - The Allotrope Perspective MelanieGrupe 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/semanticsforintegratedlaboratoryanalyticalprocesses-theallotropeperspective-150716091657-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 laboratory analytical processes - The Allotrope Perspective from OSTHUS
]]> 908 5 https://cdn.slidesharecdn.com/ss_thumbnails/semanticsforintegratedlaboratoryanalyticalprocesses-theallotropeperspective-150716091657-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Best Practice Reference Architecture for Data Curation /slideshow/2015-0421-me-bio-it-final/47290217 2015-04-21mebioitfinal-150422091444-conversion-gate01
OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015.]]>
OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015.]]> Wed, 22 Apr 2015 09:14:44 GMT /slideshow/2015-0421-me-bio-it-final/47290217 MelanieGrupe@slideshare.net(MelanieGrupe) Best Practice Reference Architecture for Data Curation MelanieGrupe OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2015-04-21mebioitfinal-150422091444-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> OSTHUS representative Michael Engels presented &quot;Best Practice Reference Architecture for Data Curation&quot; at BioIT World 2015.
Best Practice Reference Architecture for Data Curation from OSTHUS
]]> 1462 4 https://cdn.slidesharecdn.com/ss_thumbnails/2015-04-21mebioitfinal-150422091444-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotrope Framework /slideshow/allotrope-foundation-osthus-smart-lab-exchange-2015/45479235 allotropefoundation-osthus-smartlabexchange2015-150305104431-conversion-gate01
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]]>
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]]> Thu, 05 Mar 2015 10:44:31 GMT /slideshow/allotrope-foundation-osthus-smart-lab-exchange-2015/45479235 MelanieGrupe@slideshare.net(MelanieGrupe) Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotrope Framework MelanieGrupe 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 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/allotropefoundation-osthus-smartlabexchange2015-150305104431-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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 &amp; Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotrope Framework from OSTHUS
]]> 1165 8 https://cdn.slidesharecdn.com/ss_thumbnails/allotropefoundation-osthus-smartlabexchange2015-150305104431-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015 /slideshow/osthus-allotropesmart-lab-2015-laboratory-informatics-strategy/45477210 osthus-allotrope-smartlab2015-laboratoryinformaticsstrategy-150305100313-conversion-gate01
Building your laboratory informatics strategy: The benefit of reference architectures & data standardization. Presented by: Wolfgang Colsman, OSTHUS Dana Vanderwall, Bristol-Myers Squibb]]>
Building your laboratory informatics strategy: The benefit of reference architectures & data standardization. Presented by: Wolfgang Colsman, OSTHUS Dana Vanderwall, Bristol-Myers Squibb]]> Thu, 05 Mar 2015 10:03:13 GMT /slideshow/osthus-allotropesmart-lab-2015-laboratory-informatics-strategy/45477210 MelanieGrupe@slideshare.net(MelanieGrupe) OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015 MelanieGrupe Building your laboratory informatics strategy: The benefit of reference architectures & data standardization. Presented by: Wolfgang Colsman, OSTHUS Dana Vanderwall, Bristol-Myers Squibb <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/osthus-allotrope-smartlab2015-laboratoryinformaticsstrategy-150305100313-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Building your laboratory informatics strategy: The benefit of reference architectures &amp; data standardization. Presented by: Wolfgang Colsman, OSTHUS Dana Vanderwall, Bristol-Myers Squibb
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015 from OSTHUS
]]> 1381 8 https://cdn.slidesharecdn.com/ss_thumbnails/osthus-allotrope-smartlab2015-laboratoryinformaticsstrategy-150305100313-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Quality- How to clean up your legacy data /slideshow/data-quality-45468698/45468698 dataquality-150305065411-conversion-gate01
Common data issues in legacy data]]>
Common data issues in legacy data]]> Thu, 05 Mar 2015 06:54:11 GMT /slideshow/data-quality-45468698/45468698 MelanieGrupe@slideshare.net(MelanieGrupe) Data Quality- How to clean up your legacy data MelanieGrupe Common data issues in legacy data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataquality-150305065411-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Common data issues in legacy data
Data Quality- How to clean up your legacy data from OSTHUS
]]> 283 17 https://cdn.slidesharecdn.com/ss_thumbnails/dataquality-150305065411-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-MelanieGrupe-48x48.jpg?cb=1599049623 www.osthus.com https://cdn.slidesharecdn.com/ss_thumbnails/osthus-fasttracktofairlabdata-final-200902122746-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-fast-track-to-fair-lab-data/238371976 The Fast Track to Fair... https://cdn.slidesharecdn.com/ss_thumbnails/fairdatawsutrecht002-180607083152-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/challenges-opportunities-of-implementation-fair-in-life-sciences/101095106 Challenges &amp; Opportuni... https://cdn.slidesharecdn.com/ss_thumbnails/datalifecyclemgtacrosstheenterprise-180326104655-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-lifecycle-mgt-across-the-enterprise/91937614 Data lifecycle mgt acr...