DAME: Searching Large Data Sets Within a Grid-Enabled Engineering Application

Austin, Jim; Davis, Richard A.; Fletcher, Martyn; Jackson, Thomas W.; Jessop, Mark; Liang, Bojian; Pasley, A.

doi:10.1109/jproc.2004.842746

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…The DAME [6] has proposed a grid-enabled advanced neuralnetwork-based model for searching distributed large data sets within the distributed aircraft maintenance environment. This framework was to enhance diagonosis and prognosis of airoengine problems via using remote Grid services, tools and human experts.…”

Section: Related Workmentioning

confidence: 99%

A distributed architecture for data mining and integration

Atkinson

Hemert

Han

et al. 2009

Proceedings of the Second International Workshop on Data-Aware Distributed Computing

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This paper presents the rationale for a new architecture to support a significant increase in the scale of data integration and data mining. It proposes the composition into one framework of (1) data mining and (2) data access and integration. We name the combined activity "DMI". It supports enactment of DMI processes across heterogeneous and distributed data resources and data mining services. It posits that a useful division can be made between the facilities established to support the definition of DMI processes and the computational infrastructure provided to enact DMI processes. Communication between those two divisions is restricted to requests submitted to gateway services in a canonical DMI language. Larger-scale processes are enabled by incremental refinement of DMI-process definitions often by recomposition of lower-level definitions. Autonomous evolution of data resources and services is supported by types and descriptions which will support detection of inconsistencies and semi-automatic insertion of adaptations. These architectural ideas are being evaluated in a feasibility study that involves an application scenario and representatives of the community.

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Section: Related Workmentioning

confidence: 99%

A distributed architecture for data mining and integration

Atkinson

Hemert

Han

et al. 2009

Proceedings of the Second International Workshop on Data-Aware Distributed Computing

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“…Another example is a form of neural network called a Correlation Matrix Memory (CMM) Austin and Lees (1998) used as part of the advanced uncertainty reasoning architecture (AURA) (Austin, 1996). AURA has been used successfully in a variety of fields (Hodge and Austin, 2003;Austin et al, 2005) but requires binary input vectors. A natural way of applying CMM and AURA to time series would be to use it in conjunction with RLE clipped data, with, for example, the k-NN binary classifier described in Austin and Zhou (1998).…”

Section: Using Clipped Series In Tsdmmentioning

confidence: 99%

A Bit Level Representation for Time Series Data Mining with Shape Based Similarity

Bagnall

Ratanamahatana

Keogh

et al. 2006

Data Min Knowl Disc

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Clipping is the process of transforming a real valued series into a sequence of bits representing whether each data is above or below the average. In this paper, we argue that clipping is a useful and flexible transformation for the exploratory analysis of large time dependent data sets. We demonstrate how time series stored as bits can be very efficiently compressed and manipulated and that, under some assumptions, the discriminatory power with clipped series is asymptotically equivalent to that achieved with the raw data. Unlike other transformations, clipped series can be compared directly to the raw data series. We show that this means we can form a tight lower bounding metric for Euclidean and Dynamic Time Warping distance and hence efficiently query by content. Clipped data can be used in conjunction with a host of algorithms and statistical tests that naturally follow from the binary nature of the data. A series of experiments illustrate how clipped series can be used in increasingly complex ways to achieve better results than other popular representations. The usefulness of the proposed representation is demonstrated by the fact that the results with clipped data are consistently better than those achieved with a Wavelet or Discrete Fourier Transformation at the same compression ratio for both clustering and query by content. The flexibility of the representation is shown by the fact that we can take advantage of a variable Run Length Encoding of clipped series to define an approximation of the Kolmogorov complexity and hence perform Kolmogorov based clustering

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“…The architecture of the search system [7] is shown in Figure 1 and comprises the following components:…”

Section: The Distributed Grid Based Search Toolmentioning

confidence: 99%