ºÝºÝߣshows by User: blurock / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: blurock / Fri, 12 Jul 2024 12:27:41 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: blurock Ontology for the semantic enhancement, database definition and management and revision control /slideshow/ontology-for-the-semantic-enhancement-database-definition-and-management-and-revision-control-5db7/270208850 blurockpresentation-ic3k2021-extended-240712122741-906569aa
This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database.]]>
This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database.]]> Fri, 12 Jul 2024 12:27:41 GMT /slideshow/ontology-for-the-semantic-enhancement-database-definition-and-management-and-revision-control-5db7/270208850 blurock@slideshare.net(blurock) Ontology for the semantic enhancement, database definition and management and revision control blurock This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-ic3k2021-extended-240712122741-906569aa-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database.
Ontology for the semantic enhancement, database definition and management and revision control from Edward Blurock
]]> 50 0 https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-ic3k2021-extended-240712122741-906569aa-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 Ontology for the Semantic Enhancement, Database Definition and Management and Revision Control /slideshow/ontology-for-the-semantic-enhancement-database-definition-and-management-and-revision-control/270208682 blurockposter-ic3k2021-240712121903-e5b23230
Poster: Case Study: CHEMCONNECT: Web application in the chemical and scientific instrumentation domain using Google Cloud Firebase (Firestore NoSQL database and blob storage) ]]>
Poster: Case Study: CHEMCONNECT: Web application in the chemical and scientific instrumentation domain using Google Cloud Firebase (Firestore NoSQL database and blob storage) ]]> Fri, 12 Jul 2024 12:19:03 GMT /slideshow/ontology-for-the-semantic-enhancement-database-definition-and-management-and-revision-control/270208682 blurock@slideshare.net(blurock) Ontology for the Semantic Enhancement, Database Definition and Management and Revision Control blurock Poster: Case Study: CHEMCONNECT: Web application in the chemical and scientific instrumentation domain using Google Cloud Firebase (Firestore NoSQL database and blob storage) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blurockposter-ic3k2021-240712121903-e5b23230-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Poster: Case Study: CHEMCONNECT: Web application in the chemical and scientific instrumentation domain using Google Cloud Firebase (Firestore NoSQL database and blob storage)
Ontology for the Semantic Enhancement, Database Definition and Management and Revision Control from Edward Blurock
]]> 6 0 https://cdn.slidesharecdn.com/ss_thumbnails/blurockposter-ic3k2021-240712121903-e5b23230-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 Use of Ontologies in Chemical Kinetic Database CHEMCONNECT /slideshow/use-of-ontologies-in-chemical-kinetic-database-chemconnect/270208438 blurockpresentation-240712120350-153a2a29
This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database.]]>
This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database.]]> Fri, 12 Jul 2024 12:03:50 GMT /slideshow/use-of-ontologies-in-chemical-kinetic-database-chemconnect/270208438 blurock@slideshare.net(blurock) Use of Ontologies in Chemical Kinetic Database CHEMCONNECT blurock This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-240712120350-153a2a29-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This paper describes the use of ontologies interacting with a noSQL database (Google Cloud Firestore) in multiple capacities in the database system CHEMCONNECT. The motivation is to implement the ¡®Data on the Web Best Practices¡± as recommended by the W3C (https://www.w3.org/TR/2017/REC-dwbp-20170131/, 2017) in an application within the physical chemistry and instrumentation. First, the ontology provides semantic enhancement to each database object through meta-data, standard vocabularies and data object relationships. There is a one-to-one correspondence between the database objects and the ontology objects. Another use of the ontology is to provide a data-driven model for the creation, provenance and versioning of database objects. One aspect of this is the use of domain specific templates to guide the construction of the database objects. The definition of each database object is in a hierarchy of catalog objects, record objects and components (using the DCAT ontology model). Within each of these object definitions is a link describing how a create a set of automatically generated RDF objects within the CHEMCONNECT database. The RDFs facilitate searching the database. To facilitate versioning, data source tracking and data quality control, operations on the database are organized as transactions. In CHEMCONNECT a transaction has a one to one correspondence with the underlying JAVA operation in the implementation. Within the transaction definition, the set of prerequisites and the output of the operation is defined. The use of transactions helps organize and give semantic enhancement to the set of individual operations within the implementation. The work in this paper is on-going and as the first use-case is concentrating experimental and theoretical information in the chemical domain. The implementation is written in JAVA and is using Google Cloud firestore as the database.
Use of Ontologies in Chemical Kinetic Database CHEMCONNECT from Edward Blurock
]]> 21 0 https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-240712120350-153a2a29-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 KEOD23-JThermodynamcsCloud /slideshow/keod23jthermodynamcscloud/265829597 keod23-jthermodynamcscloudv2-240126143324-eeed99e8
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.]]>
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.]]> Fri, 26 Jan 2024 14:33:23 GMT /slideshow/keod23jthermodynamcscloud/265829597 blurock@slideshare.net(blurock) KEOD23-JThermodynamcsCloud blurock JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/keod23-jthermodynamcscloudv2-240126143324-eeed99e8-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.
KEOD23-JThermodynamcsCloud from Edward Blurock
]]> 8 0 https://cdn.slidesharecdn.com/ss_thumbnails/keod23-jthermodynamcscloudv2-240126143324-eeed99e8-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 BlurockPresentation-KEOD2023 /slideshow/blurockpresentationkeod2023/265829480 blurockpresentation-keod2023full-240126143026-9b608875
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.]]>
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.]]> Fri, 26 Jan 2024 14:30:25 GMT /slideshow/blurockpresentationkeod2023/265829480 blurock@slideshare.net(blurock) BlurockPresentation-KEOD2023 blurock JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-keod2023full-240126143026-9b608875-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.
BlurockPresentation-KEOD2023 from Edward Blurock
]]> 8 0 https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-keod2023full-240126143026-9b608875-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 KEOD-2023-Poster.pptx /slideshow/keod2023posterpptx/265823620 keod-2023-poster-240126111147-fbd700e2
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.]]>
JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.]]> Fri, 26 Jan 2024 11:11:46 GMT /slideshow/keod2023posterpptx/265823620 blurock@slideshare.net(blurock) KEOD-2023-Poster.pptx blurock JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/keod-2023-poster-240126111147-fbd700e2-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JThermodynamicsCloud is software service for the chemical, or more specifically, the combustion research domain. JThermodynamicsCloud service can be said to be an model driven application, where the ontology is a platform independent model of the data and operational structures. The ontology, as used by the service, has three distinct purposes: documentation, data structure definition and operational definitions. One goal of the ontology is to place as much of the design and domain specific structures in the ontology rather than in the application code. The application code interprets the ontology in the backend. The primary purpose of the JThermodynamicsCloud is to perform thermdynamic calculations and manage the data needed to make those calculations. The calculation itself is highly dependent on the varied types of molecular data found in the database The complete service is a system with three interacting components, a user interface using Angular, a (RESTful) backend written in JAVA (with the JENA API interpreting the ontology) and the Google Firestore noSQL document database and Firebase storage. The service uses these three components to make calculations for thermodynamic quantities based on molecular species structure. These different platforms are united through the ontology.
KEOD-2023-Poster.pptx from Edward Blurock
]]> 7 0 https://cdn.slidesharecdn.com/ss_thumbnails/keod-2023-poster-240126111147-fbd700e2-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 ChemConnect: Poster for European Combustion Meeting 2017 /slideshow/chemconnect-poster-for-european-combustion-meeting-2017/75048722 chemconnectposter-170415142833
This is a poster presented at the European Combustion Meeting, April 2017. It explains the Reference Description Language (RDF) setup of the database and the direction and development of the ChemConnect database project as an efficient means of data retrieval and data exhange and how the project is moving towards being an Electronic Laboratory Notebook (ELN).]]>
This is a poster presented at the European Combustion Meeting, April 2017. It explains the Reference Description Language (RDF) setup of the database and the direction and development of the ChemConnect database project as an efficient means of data retrieval and data exhange and how the project is moving towards being an Electronic Laboratory Notebook (ELN).]]> Sat, 15 Apr 2017 14:28:33 GMT /slideshow/chemconnect-poster-for-european-combustion-meeting-2017/75048722 blurock@slideshare.net(blurock) ChemConnect: Poster for European Combustion Meeting 2017 blurock This is a poster presented at the European Combustion Meeting, April 2017. It explains the Reference Description Language (RDF) setup of the database and the direction and development of the ChemConnect database project as an efficient means of data retrieval and data exhange and how the project is moving towards being an Electronic Laboratory Notebook (ELN). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/chemconnectposter-170415142833-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is a poster presented at the European Combustion Meeting, April 2017. It explains the Reference Description Language (RDF) setup of the database and the direction and development of the ChemConnect database project as an efficient means of data retrieval and data exhange and how the project is moving towards being an Electronic Laboratory Notebook (ELN).
ChemConnect: Poster for European Combustion Meeting 2017 from Edward Blurock
]]> 441 5 https://cdn.slidesharecdn.com/ss_thumbnails/chemconnectposter-170415142833-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 ChemConnect: SMARTCATS presentation /slideshow/chemconnect-smartcats-presentation/75048675 blurock-chemconnect-170415142452
This is a general overview of ChemConnect given to the EU Action SMARTCATS CM1404, CHEMISTRY OF SMART ENERGY CARRIERS AND TECHNOLOGIES.]]>
This is a general overview of ChemConnect given to the EU Action SMARTCATS CM1404, CHEMISTRY OF SMART ENERGY CARRIERS AND TECHNOLOGIES.]]> Sat, 15 Apr 2017 14:24:52 GMT /slideshow/chemconnect-smartcats-presentation/75048675 blurock@slideshare.net(blurock) ChemConnect: SMARTCATS presentation blurock This is a general overview of ChemConnect given to the EU Action SMARTCATS CM1404, CHEMISTRY OF SMART ENERGY CARRIERS AND TECHNOLOGIES. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blurock-chemconnect-170415142452-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is a general overview of ChemConnect given to the EU Action SMARTCATS CM1404, CHEMISTRY OF SMART ENERGY CARRIERS AND TECHNOLOGIES.
ChemConnect: SMARTCATS presentation from Edward Blurock
]]> 381 6 https://cdn.slidesharecdn.com/ss_thumbnails/blurock-chemconnect-170415142452-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 EU COST Action CM1404: WG€ - Efficient Data Exchange /slideshow/eu-cost-action-cm1404-wg-efficient-data-exchange/75048381 wg4summarymaster-170415141039
This talk discusses the topic of data exchange within the combustion community. This is a summary of a task force on data exchange within the WG4 working group, Standard definition for data collection and mining toward a virtual chemistry of Smart Energy Carriers, within the SMARTCATS EU COST Action CM1404]]>
This talk discusses the topic of data exchange within the combustion community. This is a summary of a task force on data exchange within the WG4 working group, Standard definition for data collection and mining toward a virtual chemistry of Smart Energy Carriers, within the SMARTCATS EU COST Action CM1404]]> Sat, 15 Apr 2017 14:10:39 GMT /slideshow/eu-cost-action-cm1404-wg-efficient-data-exchange/75048381 blurock@slideshare.net(blurock) EU COST Action CM1404: WG€ - Efficient Data Exchange blurock This talk discusses the topic of data exchange within the combustion community. This is a summary of a task force on data exchange within the WG4 working group, Standard definition for data collection and mining toward a virtual chemistry of Smart Energy Carriers, within the SMARTCATS EU COST Action CM1404 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wg4summarymaster-170415141039-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This talk discusses the topic of data exchange within the combustion community. This is a summary of a task force on data exchange within the WG4 working group, Standard definition for data collection and mining toward a virtual chemistry of Smart Energy Carriers, within the SMARTCATS EU COST Action CM1404
EU COST Action CM1404: WG¸Z¥ã - Efficient Data Exchange from Edward Blurock
]]> 243 5 https://cdn.slidesharecdn.com/ss_thumbnails/wg4summarymaster-170415141039-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 ChemConnect: Viewing the datasets in the repository /slideshow/chemconnect-viewing-the-datasets-in-the-repository/75047662 viewingtherepository-170415131941
This video demonstrates how one can view the original data sets, in their original text form, in the ChemConnect repository.]]>
This video demonstrates how one can view the original data sets, in their original text form, in the ChemConnect repository.]]> Sat, 15 Apr 2017 13:19:41 GMT /slideshow/chemconnect-viewing-the-datasets-in-the-repository/75047662 blurock@slideshare.net(blurock) ChemConnect: Viewing the datasets in the repository blurock This video demonstrates how one can view the original data sets, in their original text form, in the ChemConnect repository. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/viewingtherepository-170415131941-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This video demonstrates how one can view the original data sets, in their original text form, in the ChemConnect repository.
ChemConnect: Viewing the datasets in the repository from Edward Blurock
]]> 71 3 https://cdn.slidesharecdn.com/ss_thumbnails/viewingtherepository-170415131941-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 ChemConnect: Characterizing CombusAon KineAc Data with ontologies and meta-?©\data /slideshow/chemconnect-characterizing-combusaon-kineac-data-with-ontologies-and-metadata/63613882 blurock-cloudchemconnect-160630153440
ChemConnect is a database that interconnects fine-grained information extracted from chemical kinetic and thermodynamic sources such as CHEMKIN mechanism files, NASA polynomial files, and even the information behind automatic generation files. The key to the interconnection is the Resource Description Framework (RDF) from Semantic Web technologies. The RDF is a triplet where an object item (first) is associated through a descriptor (second) to a subject item. In this way the information of the object is connected (through the descriptor) to the subject. In ChemConnect the object is word (text) and the subject can be text or a database item. The search mechanism within ChemConnect uses the object and subject text as search strings. The presentation also contains an brief introduction to cloud computing. This was presented at the COST Action 1404 SMARTCATS workshop on Databases and Systems Use Cases (http//http://www.smartcats.eu/wg4ws1dp/) ]]>
ChemConnect is a database that interconnects fine-grained information extracted from chemical kinetic and thermodynamic sources such as CHEMKIN mechanism files, NASA polynomial files, and even the information behind automatic generation files. The key to the interconnection is the Resource Description Framework (RDF) from Semantic Web technologies. The RDF is a triplet where an object item (first) is associated through a descriptor (second) to a subject item. In this way the information of the object is connected (through the descriptor) to the subject. In ChemConnect the object is word (text) and the subject can be text or a database item. The search mechanism within ChemConnect uses the object and subject text as search strings. The presentation also contains an brief introduction to cloud computing. This was presented at the COST Action 1404 SMARTCATS workshop on Databases and Systems Use Cases (http//http://www.smartcats.eu/wg4ws1dp/) ]]> Thu, 30 Jun 2016 15:34:40 GMT /slideshow/chemconnect-characterizing-combusaon-kineac-data-with-ontologies-and-metadata/63613882 blurock@slideshare.net(blurock) ChemConnect: Characterizing CombusAon KineAc Data with ontologies and meta-?©\data blurock ChemConnect is a database that interconnects fine-grained information extracted from chemical kinetic and thermodynamic sources such as CHEMKIN mechanism files, NASA polynomial files, and even the information behind automatic generation files. The key to the interconnection is the Resource Description Framework (RDF) from Semantic Web technologies. The RDF is a triplet where an object item (first) is associated through a descriptor (second) to a subject item. In this way the information of the object is connected (through the descriptor) to the subject. In ChemConnect the object is word (text) and the subject can be text or a database item. The search mechanism within ChemConnect uses the object and subject text as search strings. The presentation also contains an brief introduction to cloud computing. This was presented at the COST Action 1404 SMARTCATS workshop on Databases and Systems Use Cases (http//http://www.smartcats.eu/wg4ws1dp/) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/blurock-cloudchemconnect-160630153440-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ChemConnect is a database that interconnects fine-grained information extracted from chemical kinetic and thermodynamic sources such as CHEMKIN mechanism files, NASA polynomial files, and even the information behind automatic generation files. The key to the interconnection is the Resource Description Framework (RDF) from Semantic Web technologies. The RDF is a triplet where an object item (first) is associated through a descriptor (second) to a subject item. In this way the information of the object is connected (through the descriptor) to the subject. In ChemConnect the object is word (text) and the subject can be text or a database item. The search mechanism within ChemConnect uses the object and subject text as search strings. The presentation also contains an brief introduction to cloud computing. This was presented at the COST Action 1404 SMARTCATS workshop on Databases and Systems Use Cases (http//http://www.smartcats.eu/wg4ws1dp/)
ChemConnect: Characterizing CombusAon KineAc Data with ontologies and meta-¥Ä¥å¸Mнata from Edward Blurock
]]> 243 6 https://cdn.slidesharecdn.com/ss_thumbnails/blurock-cloudchemconnect-160630153440-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 Poster: Characterizing Ignition behavior through morphing to generic curves /slideshow/characterizing-ignion-behavior-through-morphing-to-generic-curves/52803181 edwardblurockposterecm2015-150915135203-lva1-app6891
The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times.]]>
The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times.]]> Tue, 15 Sep 2015 13:52:02 GMT /slideshow/characterizing-ignion-behavior-through-morphing-to-generic-curves/52803181 blurock@slideshare.net(blurock) Poster: Characterizing Ignition behavior through morphing to generic curves blurock The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/edwardblurockposterecm2015-150915135203-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times.
Poster: Characterizing Ignition behavior through morphing to generic curves from Edward Blurock
]]> 212 8 https://cdn.slidesharecdn.com/ss_thumbnails/edwardblurockposterecm2015-150915135203-lva1-app6891-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 Poster: Very Open Data Project /slideshow/poster-very-open-data-project/52803073 dataposter-blurock-150915134953-lva1-app6891
The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community. The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ¡®electronic lab notebook¡¯ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.]]>
The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community. The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ¡®electronic lab notebook¡¯ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.]]> Tue, 15 Sep 2015 13:49:53 GMT /slideshow/poster-very-open-data-project/52803073 blurock@slideshare.net(blurock) Poster: Very Open Data Project blurock The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community. The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ¡®electronic lab notebook¡¯ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataposter-blurock-150915134953-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community. The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ¡®electronic lab notebook¡¯ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.
Poster: Very Open Data Project from Edward Blurock
]]> 606 12 https://cdn.slidesharecdn.com/ss_thumbnails/dataposter-blurock-150915134953-lva1-app6891-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 Poster: Adaptive On-?©\the-?©\fly Regression Tabula@on: Beyond ISAT /slideshow/adapve-onthefly-regression-tabulaon-beyond-isat/52802924 tabulation-blurock-150915134712-lva1-app6891
This describes a tabulation method based on computing, retaining and accessing a large, on the order of millions, number of individual kinetic time step calculations and approximations. It is essentially an extension of Pope¡¯s In Situ Adaptive Tabulation (ISAT) method. The primary differences lie in that not all configurations need be stored in memory and that a polynomial approximation is only calculated when enough points have accumulated within a localized area to be able to calculate the polynomial approximation. The latter increases efficiency because no extra points are evaluated to form an approximation (as is done in ISAT). The speed up is expected to be that of ISAT. ]]>
This describes a tabulation method based on computing, retaining and accessing a large, on the order of millions, number of individual kinetic time step calculations and approximations. It is essentially an extension of Pope¡¯s In Situ Adaptive Tabulation (ISAT) method. The primary differences lie in that not all configurations need be stored in memory and that a polynomial approximation is only calculated when enough points have accumulated within a localized area to be able to calculate the polynomial approximation. The latter increases efficiency because no extra points are evaluated to form an approximation (as is done in ISAT). The speed up is expected to be that of ISAT. ]]> Tue, 15 Sep 2015 13:47:12 GMT /slideshow/adapve-onthefly-regression-tabulaon-beyond-isat/52802924 blurock@slideshare.net(blurock) Poster: Adaptive On-?©\the-?©\fly Regression Tabula@on: Beyond ISAT blurock This describes a tabulation method based on computing, retaining and accessing a large, on the order of millions, number of individual kinetic time step calculations and approximations. It is essentially an extension of Pope¡¯s In Situ Adaptive Tabulation (ISAT) method. The primary differences lie in that not all configurations need be stored in memory and that a polynomial approximation is only calculated when enough points have accumulated within a localized area to be able to calculate the polynomial approximation. The latter increases efficiency because no extra points are evaluated to form an approximation (as is done in ISAT). The speed up is expected to be that of ISAT. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tabulation-blurock-150915134712-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This describes a tabulation method based on computing, retaining and accessing a large, on the order of millions, number of individual kinetic time step calculations and approximations. It is essentially an extension of Pope¡¯s In Situ Adaptive Tabulation (ISAT) method. The primary differences lie in that not all configurations need be stored in memory and that a polynomial approximation is only calculated when enough points have accumulated within a localized area to be able to calculate the polynomial approximation. The latter increases efficiency because no extra points are evaluated to form an approximation (as is done in ISAT). The speed up is expected to be that of ISAT.
Poster: Adaptive On-¥Ä¥å¸MÄIhe-¥Ä¥å¸MÔ\ly Regression Tabula@on: Beyond ISAT from Edward Blurock
]]> 225 9 https://cdn.slidesharecdn.com/ss_thumbnails/tabulation-blurock-150915134712-lva1-app6891-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 Characterization Ignition Behavior through Morphing to Generic Ignition Curves /slideshow/characterization-ignition-behavior-through-morphing-to-generic-ignition-curves/52795930 edwardblurock-presentationv1-150915111451-lva1-app6892
Presented at the International Conference of Chemical Kinetics, Ghent, Belgium, July, 2015 The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes, a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times. With this additional synchronization, a single generic curve, derived from the average of the morphed curves, can be derived. This generic curve represents a kinetic modelers intuitive notion of the mechanism of the process.]]>
Presented at the International Conference of Chemical Kinetics, Ghent, Belgium, July, 2015 The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes, a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times. With this additional synchronization, a single generic curve, derived from the average of the morphed curves, can be derived. This generic curve represents a kinetic modelers intuitive notion of the mechanism of the process.]]> Tue, 15 Sep 2015 11:14:51 GMT /slideshow/characterization-ignition-behavior-through-morphing-to-generic-ignition-curves/52795930 blurock@slideshare.net(blurock) Characterization Ignition Behavior through Morphing to Generic Ignition Curves blurock Presented at the International Conference of Chemical Kinetics, Ghent, Belgium, July, 2015 The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes, a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times. With this additional synchronization, a single generic curve, derived from the average of the morphed curves, can be derived. This generic curve represents a kinetic modelers intuitive notion of the mechanism of the process. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/edwardblurock-presentationv1-150915111451-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at the International Conference of Chemical Kinetics, Ghent, Belgium, July, 2015 The qualitative notion that ignition processes have similar behavior, even over an extensive range of starting conditions, is quantitatively demonstrated through the production of a single ¡¯generic¡¯ ignition curve. The key to the production of the generic curve is the recognition that the basic shapes of the species and temperature profiles occurring in the ignition process differ only in their ¡¯timing¡¯. By ¡¯morphing¡¯ the time scale, the profile shapes can be made to align. From the aligned profile shapes, a generic or ¡¯average¡¯ profile can be derived. Synchronizing chemical events modifies the ignition progress times. In addition to fixing the ignition time to have the progress value of one, intermediate ignition events (such as selected profile maxima or inflection points) that occur before ignition are also aligned to have specific ¡¯normalized¡¯ times. With this additional synchronization, a single generic curve, derived from the average of the morphed curves, can be derived. This generic curve represents a kinetic modelers intuitive notion of the mechanism of the process.
Characterization Ignition Behavior through Morphing to Generic Ignition Curves from Edward Blurock
]]> 287 4 https://cdn.slidesharecdn.com/ss_thumbnails/edwardblurock-presentationv1-150915111451-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 Paradigms /slideshow/paradigms-52795273/52795273 paradigms-150915105612-lva1-app6891
Course: Cooperative Information Systems: A brief outline of programming paradigms]]>
Course: Cooperative Information Systems: A brief outline of programming paradigms]]> Tue, 15 Sep 2015 10:56:12 GMT /slideshow/paradigms-52795273/52795273 blurock@slideshare.net(blurock) Paradigms blurock Course: Cooperative Information Systems: A brief outline of programming paradigms <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/paradigms-150915105612-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Course: Cooperative Information Systems: A brief outline of programming paradigms
Paradigms from Edward Blurock
]]> 2282 8 https://cdn.slidesharecdn.com/ss_thumbnails/paradigms-150915105612-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 Computability, turing machines and lambda calculus /slideshow/functional-background/52794942 functionalbackground-150915104733-lva1-app6891
Course: Programming Languages and Paradigms: A brief introduction to computability, turing machines and lambda calculus]]>
Course: Programming Languages and Paradigms: A brief introduction to computability, turing machines and lambda calculus]]> Tue, 15 Sep 2015 10:47:33 GMT /slideshow/functional-background/52794942 blurock@slideshare.net(blurock) Computability, turing machines and lambda calculus blurock Course: Programming Languages and Paradigms: A brief introduction to computability, turing machines and lambda calculus <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/functionalbackground-150915104733-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Course: Programming Languages and Paradigms: A brief introduction to computability, turing machines and lambda calculus
Computability, turing machines and lambda calculus from Edward Blurock
]]> 1863 8 https://cdn.slidesharecdn.com/ss_thumbnails/functionalbackground-150915104733-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 Imperative programming /slideshow/imperative-programming/52794703 imperativeprogramming-150915104120-lva1-app6891
Course: Programming Languages and Paradigms: A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ... ]]>
Course: Programming Languages and Paradigms: A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ... ]]> Tue, 15 Sep 2015 10:41:20 GMT /slideshow/imperative-programming/52794703 blurock@slideshare.net(blurock) Imperative programming blurock Course: Programming Languages and Paradigms: A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ... <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/imperativeprogramming-150915104120-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Course: Programming Languages and Paradigms: A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ...
Imperative programming from Edward Blurock
]]> 4219 13 https://cdn.slidesharecdn.com/ss_thumbnails/imperativeprogramming-150915104120-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 Programming Languages /slideshow/programming-languages-52794043/52794043 languages-150915102455-lva1-app6891
Course: Programming Languages and Paradigms: This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics]]>
Course: Programming Languages and Paradigms: This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics]]> Tue, 15 Sep 2015 10:24:55 GMT /slideshow/programming-languages-52794043/52794043 blurock@slideshare.net(blurock) Programming Languages blurock Course: Programming Languages and Paradigms: This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/languages-150915102455-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Course: Programming Languages and Paradigms: This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics
Programming Languages from Edward Blurock
]]> 1053 7 https://cdn.slidesharecdn.com/ss_thumbnails/languages-150915102455-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 Relational algebra /slideshow/relational-algebra-52793944/52793944 relationalalgebra-150915102139-lva1-app6892
Course: Database systems This is a simple introduction to relational algebra for computer science undergraduates. ]]>
Course: Database systems This is a simple introduction to relational algebra for computer science undergraduates. ]]> Tue, 15 Sep 2015 10:21:38 GMT /slideshow/relational-algebra-52793944/52793944 blurock@slideshare.net(blurock) Relational algebra blurock Course: Database systems This is a simple introduction to relational algebra for computer science undergraduates. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/relationalalgebra-150915102139-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Course: Database systems This is a simple introduction to relational algebra for computer science undergraduates.
Relational algebra from Edward Blurock
]]> 3755 7 https://cdn.slidesharecdn.com/ss_thumbnails/relationalalgebra-150915102139-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 https://cdn.slidesharecdn.com/profile-photo-blurock-48x48.jpg?cb=1720782982 Specialties: Use of artificial intelligence in Chemical Information and Combustion Research http//www.esblurock.info https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-ic3k2021-extended-240712122741-906569aa-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ontology-for-the-semantic-enhancement-database-definition-and-management-and-revision-control-5db7/270208850 Ontology for the seman... https://cdn.slidesharecdn.com/ss_thumbnails/blurockposter-ic3k2021-240712121903-e5b23230-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ontology-for-the-semantic-enhancement-database-definition-and-management-and-revision-control/270208682 Ontology for the Seman... https://cdn.slidesharecdn.com/ss_thumbnails/blurockpresentation-240712120350-153a2a29-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/use-of-ontologies-in-chemical-kinetic-database-chemconnect/270208438 Use of Ontologies in C...