The Experts below are selected from a list of 29706 Experts worldwide ranked by ideXlab platform

Chris Jermaine - One of the best experts on this subject based on the ideXlab platform.

  • scalable linear algebra on a Relational Database System
    IEEE Transactions on Knowledge and Data Engineering, 2019
    Co-Authors: Michael Gubanov, Luis L. Perez, Chris Jermaine
    Abstract:

    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.

  • scalable linear algebra on a Relational Database System
    International Conference on Management of Data, 2018
    Co-Authors: Michael Gubanov, Luis L. Perez, Chris Jermaine
    Abstract:

    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.

Chun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Storing and querying ordered XML using a Relational Database System
    2003
    Co-Authors: Igor Tatarinov, Stratis D. Viglas, Kevin Beyer, Eugene Shekita, Jayavel Shanmugasundaram, Chun Zhang
    Abstract:

    XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using Relational Database Systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered Relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a Relational Database System. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the Relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

  • SIGMOD Conference - Storing and querying ordered XML using a Relational Database System
    Proceedings of the 2002 ACM SIGMOD international conference on Management of data - SIGMOD '02, 2002
    Co-Authors: Igor Tatarinov, Stratis D. Viglas, Eugene Shekita, Jayavel Shanmugasundaram, Kevin Scott Beyer, Chun Zhang
    Abstract:

    XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using Relational Database Systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered Relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a Relational Database System. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the Relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

  • the design and performance evaluation of alternative xml storage strategies
    International Conference on Management of Data, 2002
    Co-Authors: Feng Tian, David J. Dewitt, Jianjun Chen, Chun Zhang
    Abstract:

    This paper studies five strategies for storing XML documents including one that leaves documents in the file System, three that use a Relational Database System, and one that uses an object manager. We implement and evaluate each approach using a number of XQuery queries. A number of interesting insights are gained from these experiments and a summary of the advantages and disadvantages of the approaches is presented.

Igor Tatarinov - One of the best experts on this subject based on the ideXlab platform.

  • Storing and querying ordered XML using a Relational Database System
    2003
    Co-Authors: Igor Tatarinov, Stratis D. Viglas, Kevin Beyer, Eugene Shekita, Jayavel Shanmugasundaram, Chun Zhang
    Abstract:

    XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using Relational Database Systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered Relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a Relational Database System. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the Relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

  • SIGMOD Conference - Storing and querying ordered XML using a Relational Database System
    Proceedings of the 2002 ACM SIGMOD international conference on Management of data - SIGMOD '02, 2002
    Co-Authors: Igor Tatarinov, Stratis D. Viglas, Eugene Shekita, Jayavel Shanmugasundaram, Kevin Scott Beyer, Chun Zhang
    Abstract:

    XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using Relational Database Systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered Relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a Relational Database System. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the Relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

  • a general technique for querying xml documents using a Relational Database System
    International Conference on Management of Data, 2001
    Co-Authors: Jayavel Shanmugasundaram, Eugene Shekita, Jerry Kiernan, Rajasekar Krishnamurthy, Efstratios Viglas, Jeffrey F Naughton, Igor Tatarinov
    Abstract:

    There has been recent interest in using Relational Database Systems to store and query XML documents. Each of the techniques proposed in this context works by (a) creating tables for the purpose of storing XML documents (also called Relational schema generation), (b) storing XML documents by shredding them into rows in the created tables, and (c) converting queries over XML documents into SQL queries over the created tables. Since Relational schema generation is a physical Database design issue -- dependent on factors such as the nature of the data, the query workload and availability of schemas -- there have been many techniques proposed for this purpose. Currently, each Relational schema generation technique requires its own query processor to efficiently convert queries over XML documents into SQL queries over the created tables. In this paper, we present an efficient technique whereby the same query-processor can be used for all such Relational schema generation techniques. This greatly simplifies the task of Relational schema generation by eliminating the need to write a special-purpose query processor for each new solution to the problem. In addition, our proposed technique enables users to query seamlessly across Relational data and XML documents. This provides users with unified access to both Relational and XML data without them having to deal with separate Databases.

Michael Gubanov - One of the best experts on this subject based on the ideXlab platform.

  • scalable linear algebra on a Relational Database System
    IEEE Transactions on Knowledge and Data Engineering, 2019
    Co-Authors: Michael Gubanov, Luis L. Perez, Chris Jermaine
    Abstract:

    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.

  • scalable linear algebra on a Relational Database System
    International Conference on Management of Data, 2018
    Co-Authors: Michael Gubanov, Luis L. Perez, Chris Jermaine
    Abstract:

    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.

Jayavel Shanmugasundaram - One of the best experts on this subject based on the ideXlab platform.

  • Storing and querying ordered XML using a Relational Database System
    2003
    Co-Authors: Igor Tatarinov, Stratis D. Viglas, Kevin Beyer, Eugene Shekita, Jayavel Shanmugasundaram, Chun Zhang
    Abstract:

    XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using Relational Database Systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered Relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a Relational Database System. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the Relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

  • SIGMOD Conference - Storing and querying ordered XML using a Relational Database System
    Proceedings of the 2002 ACM SIGMOD international conference on Management of data - SIGMOD '02, 2002
    Co-Authors: Igor Tatarinov, Stratis D. Viglas, Eugene Shekita, Jayavel Shanmugasundaram, Kevin Scott Beyer, Chun Zhang
    Abstract:

    XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using Relational Database Systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered Relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a Relational Database System. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the Relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

  • a general technique for querying xml documents using a Relational Database System
    International Conference on Management of Data, 2001
    Co-Authors: Jayavel Shanmugasundaram, Eugene Shekita, Jerry Kiernan, Rajasekar Krishnamurthy, Efstratios Viglas, Jeffrey F Naughton, Igor Tatarinov
    Abstract:

    There has been recent interest in using Relational Database Systems to store and query XML documents. Each of the techniques proposed in this context works by (a) creating tables for the purpose of storing XML documents (also called Relational schema generation), (b) storing XML documents by shredding them into rows in the created tables, and (c) converting queries over XML documents into SQL queries over the created tables. Since Relational schema generation is a physical Database design issue -- dependent on factors such as the nature of the data, the query workload and availability of schemas -- there have been many techniques proposed for this purpose. Currently, each Relational schema generation technique requires its own query processor to efficiently convert queries over XML documents into SQL queries over the created tables. In this paper, we present an efficient technique whereby the same query-processor can be used for all such Relational schema generation techniques. This greatly simplifies the task of Relational schema generation by eliminating the need to write a special-purpose query processor for each new solution to the problem. In addition, our proposed technique enables users to query seamlessly across Relational data and XML documents. This provides users with unified access to both Relational and XML data without them having to deal with separate Databases.