The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
Chris Jermaine - One of the best experts on this subject based on the ideXlab platform.
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scalable linear algebra on a Relational database system
Communications of The ACM, 2020Co-Authors: Shangyu Luo, Michael Gubanov, Zekai J Gao, Luis Perez, Dimitrije Jankov, Chris JermaineAbstract:As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. We argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization---which is vital to scaling linear algebra computations---and it is well known how to make Relational systems scalable. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can become a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology.
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scalable linear algebra on a Relational database system
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Michael Gubanov, Luis L. Perez, Chris JermaineAbstract:As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization—which is vital to scaling linear algebra computations—and it is well-known how to make Relational systems scale. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can be a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology. Our results also suggest that if scalable linear algebra is to be added to a modern dataflow platform such as Spark, they should be added on top of the system's more structured (Relational) data abstractions, rather than being constructed directly on top of the system's raw dataflow operators.
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scalable linear algebra on a Relational database system
International Conference on Management of Data, 2018Co-Authors: Michael Gubanov, Luis L. Perez, Chris JermaineAbstract:Scalable linear algebra is important for analytics and machine learning (including deep learning). In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization-which is vital to scaling linear algebra computations-and it is well-known how to make Relational systems scale. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can be a competitive platform for scalable linear algebra. Our results suggest that brand new systems supporting scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology.
Michael Gubanov - One of the best experts on this subject based on the ideXlab platform.
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scalable linear algebra on a Relational database system
Communications of The ACM, 2020Co-Authors: Shangyu Luo, Michael Gubanov, Zekai J Gao, Luis Perez, Dimitrije Jankov, Chris JermaineAbstract:As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. We argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization---which is vital to scaling linear algebra computations---and it is well known how to make Relational systems scalable. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can become a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology.
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scalable linear algebra on a Relational database system
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Michael Gubanov, Luis L. Perez, Chris JermaineAbstract:As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization—which is vital to scaling linear algebra computations—and it is well-known how to make Relational systems scale. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can be a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology. Our results also suggest that if scalable linear algebra is to be added to a modern dataflow platform such as Spark, they should be added on top of the system's more structured (Relational) data abstractions, rather than being constructed directly on top of the system's raw dataflow operators.
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scalable linear algebra on a Relational database system
International Conference on Management of Data, 2018Co-Authors: Michael Gubanov, Luis L. Perez, Chris JermaineAbstract:Scalable linear algebra is important for analytics and machine learning (including deep learning). In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization-which is vital to scaling linear algebra computations-and it is well-known how to make Relational systems scale. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can be a competitive platform for scalable linear algebra. Our results suggest that brand new systems supporting scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology.
Peter J. Stuckey - One of the best experts on this subject based on the ideXlab platform.
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The aditi deductive database system
The VLDB Journal, 1994Co-Authors: Jayen Vaghanl, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. Stuckey, Tim S. Leask, James HarlandAbstract:Deductive databases generalize Relational databases by providing support for recursive views and non-atomic data. Aditi is a deductive system based on the client-server model; it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses Relational Technology for efficiency in the management of disk-based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than Relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.
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design overview of the aditi deductive database system
International Conference on Data Engineering, 1991Co-Authors: Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. StuckeyAbstract:An overview of the structure of Aditi, a disk-based deductive database system under continuous development at the University of Melbourne, is presented. The aim of the project is to find out what implementation methods and optimization techniques would make deductive databases competitive with current commercial Relational databases. The structure of the Aditi prototype is based on a variant of the client-server model. The front end of Aditi interacts with the user exclusively in a logical language that has more expressive power than Relational query languages. The back end uses Relational Technology for efficiency in the management of disk-based data and uses some optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The system has been functional for almost two years now, and has already proven its worth as a research tool. >
Kotagiri Ramamohanarao - One of the best experts on this subject based on the ideXlab platform.
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The aditi deductive database system
The VLDB Journal, 1994Co-Authors: Jayen Vaghanl, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. Stuckey, Tim S. Leask, James HarlandAbstract:Deductive databases generalize Relational databases by providing support for recursive views and non-atomic data. Aditi is a deductive system based on the client-server model; it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses Relational Technology for efficiency in the management of disk-based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than Relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.
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an implementation overview of the aditi deductive database system
International Conference on Deductive and Object-Oriented Databases, 1993Co-Authors: Kotagiri RamamohanaraoAbstract:Deductive databases generalize Relational databases by providing support for mecursive views and non-atomic data. Aditi is a deductive database system based on the client-server model: it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses Relational Technology for efficiency in the management of disk based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than Relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.
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design overview of the aditi deductive database system
International Conference on Data Engineering, 1991Co-Authors: Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. StuckeyAbstract:An overview of the structure of Aditi, a disk-based deductive database system under continuous development at the University of Melbourne, is presented. The aim of the project is to find out what implementation methods and optimization techniques would make deductive databases competitive with current commercial Relational databases. The structure of the Aditi prototype is based on a variant of the client-server model. The front end of Aditi interacts with the user exclusively in a logical language that has more expressive power than Relational query languages. The back end uses Relational Technology for efficiency in the management of disk-based data and uses some optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The system has been functional for almost two years now, and has already proven its worth as a research tool. >
Shangyu Luo - One of the best experts on this subject based on the ideXlab platform.
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scalable linear algebra on a Relational database system
Communications of The ACM, 2020Co-Authors: Shangyu Luo, Michael Gubanov, Zekai J Gao, Luis Perez, Dimitrije Jankov, Chris JermaineAbstract:As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. We argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most Relational systems already have support for cost-based optimization---which is vital to scaling linear algebra computations---and it is well known how to make Relational systems scalable. We show that by making just a few changes to a parallel/distributed Relational database system, such a system can become a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing Relational Technology.
