The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Randall Weis - One of the best experts on this subject based on the ideXlab platform.
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The Origins of Asserted Versioning: Computer Science Research
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses computer science contributions to temporal data management, and the relevance of some of these concepts to Asserted Versioning. It begins with an overview of the three sources of Asserted Versioning: computer science work on temporal data; best practices in the IT profession related to versioning; and original work by the authors themselves. Over the last three decades, the computer science community has done extensive work on temporal data, and especially on bitemporal data. During that same period of time, the IT community has developed various forms of versioning, all of which are methods of managing one of the two kinds of unitemporal data. Asserted Versioning may be thought of as a method of managing both uni and bitemporal data which, unlike the standard model of temporal data management, recognizes that rows in bitemporal tables represent versions of things and that, consequently, these rows do not stand alone as semantic objects. Asserted Versioning may also be thought of as a form of versioning, a technique for managing historical data that has evolved in the IT industry over the last quarter-century. But unlike existing best practice variations on that theme, Asserted Versioning supports the full semantics of versions. In addition, Asserted Versioning also integrates the management of versions with the management of assertions and with the management of bitemporal data. Besides embracing contributions from computer science research and from business IT best practices, Asserted Versioning introduces three new concepts to the field of temporal data management: episode , the internalization of Pipeline Datasets , and encapsulation .
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Deferred Assertions and Other Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses the topic of Pipeline Datasets, in general, and of one kind of Pipeline dataset—deferred assertions—in particular. It begins by noting that deferred assertions represent past, present, and future versions in future assertion time, but that past, present, and future versions also exist in past and present assertion time. This gives nine categories of temporal data, one of which is currently asserted current versions of things, which known as conventional data, physically located in production tables. The other eight categories correspond to Pipeline Datasets, being data that has those production tables as either their destinations or their origins. Deferred assertions are the result of applying deferred transactions to the database. Instead of holding on to maintenance transactions until it is the right time to apply them, Asserted Versioning applies them right away, but does not immediately assert them. These deferred assertions may be updated or deleted by themselves. Just as deferred assertions replace collections of transactions that have not yet been applied to the database, bitemporal data in any of the other seven categories replaces other physically external Datasets. Asserted version tables contain data in all these temporal categories and, in doing so, internalize what would otherwise be physically distinct Datasets.
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Re-Presenting Internalized Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:Pipeline Datasets are files, tables or other physical Datasets in which the managed object itself represents a type and contains multiple managed objects each of which represents an instance of that type, and which in turn themselves contain instances of other types. This chapter focuses on the entire family of Pipeline Datasets. Eight logical categories of Pipeline Datasets can be distinguished, based on where in a combination of past, present or future assertion and effective time their data is located. These include Posted History, Posted Updates, Posted Projections, Current History, Current Data, Current Projections, Pending History, Pending Updates, and Pending Projections. The chapter discusses each of them and shows how queries and views can reassemble, as queryable objects, exactly the data that had existed in those Datasets. This demonstrates that while eliminating the management costs associated with this data, one can still make this data available in whatever combinations it is needed.
Allison Williford - One of the best experts on this subject based on the ideXlab platform.
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A standardized head-fixation system for performing large-scale, in vivo physiological recordings in mice.
Journal of neuroscience methods, 2020Co-Authors: Peter A. Groblewski, David Sullivan, Jérôme Lecoq, S.e.j. De Vries, Shiella Caldejon, Quinn L’heureux, T. Keenan, Kate Roll, C Slaughterback, Allison WillifordAbstract:Abstract Background The Allen Institute recently built a set of high-throughput experimental Pipelines to collect comprehensive in vivo surveys of physiological activity in the visual cortex of awake, head-fixed mice. Developing these large-scale, industrial-like Pipelines posed many scientific, operational, and engineering challenges. New method Our strategies for creating a cross-platform reference space to which all Pipeline Datasets were mapped required development of 1) a robust headframe, 2) a reproducible clamping system, and 3) data-collection systems that are built, and maintained, around precise alignment with a reference artifact. Results When paired with our Pipeline clamping system, our headframe exceeded deflection and reproducibility requirements. By leveraging our headframe and clamping system we were able to create a cross-platform reference space to which multi-modal imaging Datasets could be mapped. Comparison with existing methods Together, the Allen Brain Observatory headframe, surgical tooling, clamping system, and system registration strategy create a unique system for collecting large amounts of standardized in vivo Datasets over long periods of time. Moreover, the integrated approach to cross-platform registration allows for multi-modal Datasets to be collected within a shared reference space. Conclusions Here we report the engineering strategies that we implemented when creating the Allen Brain Observatory physiology Pipelines. All of the documentation related to headframe, surgical tooling, and clamp design has been made freely available and can be readily manufactured or procured. The engineering strategy, or components of the strategy, described in this report can be tailored and applied by external researchers to improve data standardization and stability.
Tom Johnston - One of the best experts on this subject based on the ideXlab platform.
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The Origins of Asserted Versioning: Computer Science Research
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses computer science contributions to temporal data management, and the relevance of some of these concepts to Asserted Versioning. It begins with an overview of the three sources of Asserted Versioning: computer science work on temporal data; best practices in the IT profession related to versioning; and original work by the authors themselves. Over the last three decades, the computer science community has done extensive work on temporal data, and especially on bitemporal data. During that same period of time, the IT community has developed various forms of versioning, all of which are methods of managing one of the two kinds of unitemporal data. Asserted Versioning may be thought of as a method of managing both uni and bitemporal data which, unlike the standard model of temporal data management, recognizes that rows in bitemporal tables represent versions of things and that, consequently, these rows do not stand alone as semantic objects. Asserted Versioning may also be thought of as a form of versioning, a technique for managing historical data that has evolved in the IT industry over the last quarter-century. But unlike existing best practice variations on that theme, Asserted Versioning supports the full semantics of versions. In addition, Asserted Versioning also integrates the management of versions with the management of assertions and with the management of bitemporal data. Besides embracing contributions from computer science research and from business IT best practices, Asserted Versioning introduces three new concepts to the field of temporal data management: episode , the internalization of Pipeline Datasets , and encapsulation .
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Deferred Assertions and Other Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses the topic of Pipeline Datasets, in general, and of one kind of Pipeline dataset—deferred assertions—in particular. It begins by noting that deferred assertions represent past, present, and future versions in future assertion time, but that past, present, and future versions also exist in past and present assertion time. This gives nine categories of temporal data, one of which is currently asserted current versions of things, which known as conventional data, physically located in production tables. The other eight categories correspond to Pipeline Datasets, being data that has those production tables as either their destinations or their origins. Deferred assertions are the result of applying deferred transactions to the database. Instead of holding on to maintenance transactions until it is the right time to apply them, Asserted Versioning applies them right away, but does not immediately assert them. These deferred assertions may be updated or deleted by themselves. Just as deferred assertions replace collections of transactions that have not yet been applied to the database, bitemporal data in any of the other seven categories replaces other physically external Datasets. Asserted version tables contain data in all these temporal categories and, in doing so, internalize what would otherwise be physically distinct Datasets.
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Re-Presenting Internalized Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:Pipeline Datasets are files, tables or other physical Datasets in which the managed object itself represents a type and contains multiple managed objects each of which represents an instance of that type, and which in turn themselves contain instances of other types. This chapter focuses on the entire family of Pipeline Datasets. Eight logical categories of Pipeline Datasets can be distinguished, based on where in a combination of past, present or future assertion and effective time their data is located. These include Posted History, Posted Updates, Posted Projections, Current History, Current Data, Current Projections, Pending History, Pending Updates, and Pending Projections. The chapter discusses each of them and shows how queries and views can reassemble, as queryable objects, exactly the data that had existed in those Datasets. This demonstrates that while eliminating the management costs associated with this data, one can still make this data available in whatever combinations it is needed.
Peter A. Groblewski - One of the best experts on this subject based on the ideXlab platform.
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A standardized head-fixation system for performing large-scale, in vivo physiological recordings in mice.
Journal of neuroscience methods, 2020Co-Authors: Peter A. Groblewski, David Sullivan, Jérôme Lecoq, S.e.j. De Vries, Shiella Caldejon, Quinn L’heureux, T. Keenan, Kate Roll, C Slaughterback, Allison WillifordAbstract:Abstract Background The Allen Institute recently built a set of high-throughput experimental Pipelines to collect comprehensive in vivo surveys of physiological activity in the visual cortex of awake, head-fixed mice. Developing these large-scale, industrial-like Pipelines posed many scientific, operational, and engineering challenges. New method Our strategies for creating a cross-platform reference space to which all Pipeline Datasets were mapped required development of 1) a robust headframe, 2) a reproducible clamping system, and 3) data-collection systems that are built, and maintained, around precise alignment with a reference artifact. Results When paired with our Pipeline clamping system, our headframe exceeded deflection and reproducibility requirements. By leveraging our headframe and clamping system we were able to create a cross-platform reference space to which multi-modal imaging Datasets could be mapped. Comparison with existing methods Together, the Allen Brain Observatory headframe, surgical tooling, clamping system, and system registration strategy create a unique system for collecting large amounts of standardized in vivo Datasets over long periods of time. Moreover, the integrated approach to cross-platform registration allows for multi-modal Datasets to be collected within a shared reference space. Conclusions Here we report the engineering strategies that we implemented when creating the Allen Brain Observatory physiology Pipelines. All of the documentation related to headframe, surgical tooling, and clamp design has been made freely available and can be readily manufactured or procured. The engineering strategy, or components of the strategy, described in this report can be tailored and applied by external researchers to improve data standardization and stability.
Jérôme Lecoq - One of the best experts on this subject based on the ideXlab platform.
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A standardized head-fixation system for performing large-scale, in vivo physiological recordings in mice.
Journal of neuroscience methods, 2020Co-Authors: Peter A. Groblewski, David Sullivan, Jérôme Lecoq, S.e.j. De Vries, Shiella Caldejon, Quinn L’heureux, T. Keenan, Kate Roll, C Slaughterback, Allison WillifordAbstract:Abstract Background The Allen Institute recently built a set of high-throughput experimental Pipelines to collect comprehensive in vivo surveys of physiological activity in the visual cortex of awake, head-fixed mice. Developing these large-scale, industrial-like Pipelines posed many scientific, operational, and engineering challenges. New method Our strategies for creating a cross-platform reference space to which all Pipeline Datasets were mapped required development of 1) a robust headframe, 2) a reproducible clamping system, and 3) data-collection systems that are built, and maintained, around precise alignment with a reference artifact. Results When paired with our Pipeline clamping system, our headframe exceeded deflection and reproducibility requirements. By leveraging our headframe and clamping system we were able to create a cross-platform reference space to which multi-modal imaging Datasets could be mapped. Comparison with existing methods Together, the Allen Brain Observatory headframe, surgical tooling, clamping system, and system registration strategy create a unique system for collecting large amounts of standardized in vivo Datasets over long periods of time. Moreover, the integrated approach to cross-platform registration allows for multi-modal Datasets to be collected within a shared reference space. Conclusions Here we report the engineering strategies that we implemented when creating the Allen Brain Observatory physiology Pipelines. All of the documentation related to headframe, surgical tooling, and clamp design has been made freely available and can be readily manufactured or procured. The engineering strategy, or components of the strategy, described in this report can be tailored and applied by external researchers to improve data standardization and stability.