dimensional data


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dimensional data

see dimensional data.
References in periodicals archive ?
Due to high computation complexity of similarity search on high dimensional data and rapid stream arrival rate, continuously retrieving the similar content on high dimensional data streams is very challenging.
First, we utilize a compact representation for the high dimensional data sequence, namely CV.
Firstly, the work in this paper is the first work that concerns how to process continuous sequence similarity query over high dimensional data stream.
In section 5, we discuss how to online transform high dimensional data stream into CVs and how to organize these CVs.
A m dimensional data stream S is an ordered and infinite data set {[s.
Our goal in this paper is to continuously find the nearest neighbor for a high dimensional data stream S from a static sequence set FS.
We let e_Min be 0 and e_Max be diagonal of high dimensional data space D, which can ensure that the expected value of a falls into the interval [e_Min, e_Max].
In this section, we report the results of an extensive performance study conducted to evaluate the proposed methods on high dimensional data stream.
We randomly generate a 16 dimensional data set as streaming data set S.
This new sensor technology will be scaleable for use in the measurement of complex contours in the aerospace and automotive industries as well and will be powerful enough to look at whole car bodies and aerospace fuselage sections and provide large amounts of accurate dimensional data.
WhereScape is the market's leading independent source of data warehouse lifecycle management tools for the design, construction and operation of dimensional data warehouses, data marts, business intelligence environments and decision support systems.
Privately held with offices in the US, UK and Asia Pacific, WhereScape was founded by dimensional data warehousing practitioners with a single end in view: the creation of methodologically-sound tools to drive the rapid creation, maintenance and enhancement of robust, real world data warehouses - tools formed and shaped by the experience of literally thousands of experienced dimensional data warehousing practitioners, rather than by academic notions, abstract models and untested hypotheses.

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