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This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.
Large scale feature searches of accumulated collections of medical imagery are required for multiple purposes, including clinical studies, administrative planning, epidemiology, teaching, quality improvement, and research. To perform a feature search of large collections of medical imagery, one can either search text descriptors of the imagery in the collection (usually the interpretation), or (if the imagery is in digital format) the imagery itself. At our institution, text interpretations of medical imagery are all available in our VA Hospital Information System. These are downloaded daily into an off-line computer. The text descriptors of most medical imagery are usually formatted as free text, and so require a user friendly database search tool to make searches quick and easy for any user to design and execute. We are tailoring such a database search tool (Liveview), developed by one of the authors (Karshat). To further facilitate search construction, we are constructing (from our accumulated interpretation data) a dictionary of medical and radiological terms and synonyms. If the imagery database is digital, the imagery which the search discovers is easily retrieved from the computer archive. We describe our database search user interface, with examples, and compare the efficacy of computer assisted imagery searches from a clinical text database with manual searches. Our initial work on direct feature searches of digital medical imagery is outlined.
Current methods of searching for and retrieving data from satellite land remote sensing archives do not allow for interactive information extraction. Instead, Earth science data users are required to download files over low-bandwidth networks to local workstations and process data before science questions can be addressed. New methods of extracting information from data archives need to become more interactive to meet user demands for deriving increasingly complex information from rapidly expanding archives. Moving the tools required for processing data to computer systems of data providers, and away from systems of the data consumer, can improve turnaround times for data processing workflows. The implementation of middleware services was used to provide interactive access to archive data. The goal of this middleware services development is to enable Earth science data users to access remote sensing archives for immediate answers to science questions instead of links to large volumes of data to download and process. Exposing data and metadata to web-based services enables machine-driven queries and data interaction. Also, product quality information can be integrated to enable additional filtering and sub-setting. Only the reduced content required to complete an analysis is then transferred to the user.
The flexibility and interoperability of OGC Web Processing Services are combined with an extensive range of data processing operations supported by the Climate Data Operators (CDO) library to facilitate processing of the CMIP5 climate data archive. The challenges posed by this peta-scale archive allow us to test and develop systems which will help us to deal with approaching exa-scale challenges. The CEDA WPS package allows users to manipulate data in the archive and export the results without first downloading the data -- in some cases this can drastically reduce the data volumes which need to be transferred and greatly reduce the time needed for the scientists to get their results. Reductions in data transfer are achieved at the expense of an additional computational load imposed on the archive (or near-archive) infrastructure. This is managed with a load balancing system. Short jobs may be run in near real-time, longer jobs will be queued. When jobs are queued the user is provided with a web dashboard displaying job status. A clean split between the data manipulation software and the request management software is achieved by exploiting the extensive CDO library. This library has a long history of development to support the needs of the climate science community. Use of the library ensures that operations run on data by the system can be reproduced by users using the same operators installed on their own computers. Examples using the system deployed for the CMIP5 archive will be shown and issues which need to be addressed as archive volumes expand into the exa-scale will be discussed. 2b1af7f3a8