The Experts below are selected from a list of 312 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
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.
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.
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
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.
Dimitrije Jankov - 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.
Luis Perez - 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.
