The discovery of new functional oxides using combinatorial techniques and advanced data mining algorithms

Electroceramic materials research is a wide ranging field driven by device applications. For many years, the demand for new materials was addressed largely through serial processing and analysis of samples often similar in composition to those already characterised. The Functional Oxide Discovery project (FOXD) is a combinatorial materials discovery project combining high-throughput synthesis and characterisation with advanced data mining to develop novel materials. Dielectric ceramics are of interest for use in telecommunications equipment; oxygen ion conductors are examined for use in fuel cell cathodes. Both applications are subject to ever increasing industry demands and materials designs capable of meeting the stringent requirements are urgently required. The London University Search Instrument (LUSI) is a combinatorial robot employed for materials synthesis. Ceramic samples are produced automatically using an ink-jet printer which mixes and prints inks onto alumina slides. The slides are transferred to a furnace for sintering and transported to other locations for analysis. Production and analysis data are stored in the project database. The database forms a valuable resource detailing the progress of the project and forming a basis for data mining. Materials design is a two stage process. The first stage, forward prediction, is accomplished using an artificial neural network, a Baconian, inductive technique. In a second stage, the artificial neural network is inverted using a genetic algorithm. The artificial neural network prediction, stoichiometry and prediction reliability form objectives for the genetic algorithm which results in a selection of materials designs. The full potential of this approach is realised through the manufacture and characterisation of the materials. The resulting data improves the prediction algorithms, permitting iterative improvement to the designs and the discovery of completely new materials

Group Activity Recognition Using Wearable Sensing Devices

Understanding behavior of groups in real time can help prevent tragedy in crowd emergencies. Wearable devices allow sensing of human behavior, but the infrastructure required to communicate data is often the first casualty in emergency situations. Peer-to-peer (P2P) methods for recognizing group behavior are necessary, but the behavior of the group cannot be observed at any single location. The contribution is the methods required for recognition of group behavior using only wearable devices

A grid-based approach for enterprise-scale data mining, Future Generation Computer Systesm 23

Abstract — We describe a grid-based approach for enterprisescale data mining that leverages database technology for I/O parallelism, and on-demand compute servers for compute parallelism in the statistical computations. By enterprise-scale, we mean the highly-automated use of data mining in vertical business applications, where the data is stored on one or more relational database systems, and where a distributed architecture comprising of high-performance compute servers or a network of low-cost, commodity processors is used to improve application performance and provide the application deployment flexibility for overall workload management. The approach relies on an algorithmic decomposition of the data mining kernel on the data and compute grids, which makes it possible to exploit the parallelism on the respective grids in a simple way, while minimizing the data transfer between them. The overall approach is compatible with existing database standards for data mining task specification and results reporting, and hence external applications using these standardsbased interfaces do not have to be modified in order to realize the benefits of this grid-based approach. Index Terms—Data mining, Grid computing, Predictive modeling, Parallel databases. Data-mining technologies that automate the generation and application of statistical models from data are of interest in a variety of applications cutting across industry sectors. These applications include, for example, customer relationship management (Retail, Banking and Finance, Telecom), fraud detection (Banking and Finance, Telecom), lead generatio

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Abstract — We describe a grid-based approach for enterprisescale data mining that leverages database technology for I/O parallelism, and on-demand compute servers for compute parallelism in the statistical computations. By enterprise-scale, we mean the highly-automated use of data mining in vertical business applications, where the data is stored on one or more relational database systems, and where a distributed architecture comprising of high-performance compute servers or a network of low-cost, commodity processors is used to improve application performance and provide the application deployment flexibility for overall workload management. The approach relies on an algorithmic decomposition of the data mining kernel on the data and compute grids, which makes it possible to exploit the parallelism on the respective grids in a simple way, while minimizing the data transfer between them