Conference Paper

The Signal Data Explorer: A High Performance Grid based Signal Search Tool for use in Distributed Diagnostic Applications.

Dept. of Comput. Sci., York Univ., UK
DOI: 10.1109/CCGRID.2006.102 Conference: Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2006), 16-19 May 2006, Singapore
Source: DBLP

ABSTRACT We describe a high performance grid based signal search tool for distributed diagnostic applications developed in conjunction with Rolls-Royce plc for civil aero engine condition monitoring applications. With the introduction of advanced monitoring technology into engineering systems, healthcare, etc., the associated diagnostic processes are increasingly required to handle and consider vast amounts of data. An exemplar of such a diagnosis process was developed during the DAME project, which built a proof of concept demonstrator to assist in the enhanced diagnosis and prognosis of aero-engine conditions. In particular it has shown the utility of an interactive viewing and high performance distributed search tool (the signal data explorer) in the aeroengine diagnostic process. The viewing and search techniques are equally applicable to other domains. The signal data explorer and search services have been demonstrated on the Worldwide Universities Network to search distributed databases of electrocardiograph data.

0 Bookmarks
 · 
81 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we introduce a theoretical basis for a Hadoop-based framework for parallel and distributed feature selection. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of four feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop MapReduce. Hadoop allows parallel and distributed processing so each feature selector can be processed in parallel and multiple feature selectors can be processed together in parallel allowing multiple feature selectors to be compared. We identify commonalities among the four features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all four feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop.
    Technical Report YCS-2013-485, Department of Computer Science, University of York. 09/2013;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This chapter presents the Relaxation by Elimination methods (RBE) for searching large collections of graph data that has been implemented on a distributed platform and is in daily use for searching a database of molecules. The core of the approach uses an ‘inverted’ relaxation labelling method that finds a good match of the input data with stored examples. The method is shown to scale linearly with the number of graphs, and to scale linearly under given circumstances to the number of nodes in the graph. Key to the idea is that the system cuts the search time by removing a set of sub-optimal matches leaving those that could match. The system uses arrays of biologically plausible neural networks, Correlation Matrix Memories (CMMs) to store the constraints between the nodes of the graphs being searched. This is coupled to a novel search method. The system is highly parallel. Recently we have developed a parallel Grid enabled computer system (Cortex II) which utilises Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs) and have implemented the method on this system. A service for matching small molecules to a molecule database, in which the molecules are represented as attributed graphs, is currently running online. The methods have also been applied to searching trademark databases allowing people to find trademarks that are geometrically similar. The chapter describes the method in detail and its implementation and application. It also brings together work that has appeared separately and presents a new mathematical formulation of the mapping of RBE onto correlation matrix methods.
    12/2009: pages 111-138;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The organisation of much industrial and scientific work involves the geographically distributed utilisation of multiple tools, services and (increasingly) distributed data. A generic Distributed Tool, Service and Data Architecture is described together with its application to the aero-engine domain through the BROADEN project. A central issue in the work is the ability to provide a flexible platform where data intensive services may be added with little overhead from existing tool and service vendors. The paper explains the issues surrounding this and explains how the project is investigating the PMC method (developed in DAME) and the use of Enterprise Service Bus to over come the problems. The mapping of the generic architecture to the BROADEN application (visualisation tools and distributed data and services) is described together with future work.
    01/2006;

Full-text (3 Sources)

Download
27 Downloads
Available from
May 27, 2014