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.
For at least a decade, WWW, large enterprises, and desktop users suffer from an inability to efficiently access and manage their data. To help automate different aspects of this challenging problem many solutions have been proposed in both academic and industrial research. One of them -UFO Repository, introduced in [5] is currently gaining momentum by advocating an object-oriented approach to help manage information overflow.An overview and evaluation of the UFO approach was published and reviewed in [5,1]. This paper is more focused on the algorithms for UFO creation and knowledge accumulation. We describe, evaluate these algorithms, and demonstrate that UFO learning performance is surprisingly fast and accurate across several domains even for a small amount of initial training data.
Currently,
WWW
, large enterprises, and desktop users suffer from an inability to efficiently access and manage
differently structured
data. The same data objects (e.g. Product) stored by different databases, repositories, distributed web storage systems,
etc
are named, referenced, and combined internally into schemas or data structures differently. This leads to
structural mismatch
of data that often consists of the same semantic objects (e.g. EBay and Yahoo! online auction offers).
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