TitleEvaluation of Multimedia Applications in a Cluster-Oriented Environment
Journal titleMetrology and Measurement Systems
Keywordsquality measurements ; software quality ; quality model and measures ; parallel computing ; distributed middleware ; multimedia applications
Divisions of PASNauki Techniczne
PublisherPolish Academy of Sciences Committee on Metrology and Scientific Instrumentation
TypeArtykuły / Articles
ReferencesKrafzig D. (2004), Enterprise SOA: Service-Oriented Architecture Best Practices. ; Krawczyk H. (2010), Kaskada - multimedia processing platform architecture, null, 26. ; Czarnul P. (2006), Software Engineering Techniques: Design for Quality, 179, doi.org/10.1007/978-0-387-39388-9_18 ; Krawczyk H. (1998), Analysis and testing of distributed software applications. ; Czarnul P. (2010), Modeling, run-time optimization and execution of distributed workflow applications in the JEE-based BeesyCluster environment, Journal of Supercomputing, 1, doi.org/10.1007/s11227-010-0499-7 ; Czarnul P. (2011), BeesyBees: A mobile agent-based middleware for a reliable and secure execution of service-based workflow applications in BeesyCluster, Multiagent and Grid Systems Journal, 7, 6, 219, doi.org/10.3233/MGS-2011-0178 ; Czarnul P. (2010), Modelling, Optimization and Execution of Workflow Applications with Data Distribution, Service Selection and Budget Constraints in BeesyCluster, null, 5, 629. ; Wieczorek M. (2009), Towards a general model of the multi-criteria workflow scheduling on the grid, Future Generation Computer Systems, 25, 237, doi.org/10.1016/j.future.2008.09.002 ; Yu J. (2005), A taxonomy of workflow management systems for grid computing, Journal of Grid Computing, 3, 171, doi.org/10.1007/s10723-005-9010-8 ; Yu J. (2008), Metaheuristics for Scheduling in Distributed Computing Environments, 146, 173, doi.org/10.1007/978-3-540-69277-5_7 ; Chin S. (2010), Adaptive service scheduling for workflow applications in service-oriented grid, Journal of Supercomputing, 52, 253, doi.org/10.1007/s11227-009-0290-9 ; Garg S. (2010), Time and cost trade-off management for scheduling parallel applications on utility grids, Future Generation Computer Systems, 26, 1344, doi.org/10.1016/j.future.2009.07.003 ; <a target="_blank" href='http://i.top500.org/system/9260'>http://i.top500.org/system/9260</a> ; Blokus A. (2011), The design of an intelligent medical space supporting automated patient interviewing, null, 16. ; Iakovidis D. (2010), Reduction of capsule endoscopy reading times by unsupervised image mining, Computerized Medical Imaging and Graphics. Biomedical Image Technologies and Methods - BIBE, 34, 6, 471, doi.org/10.1016/j.compmedimag.2009.11.005 ; Li B. (2009), Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine, null, 498. ; Kodogiannis V. (2007), An adaptive neurofuzzy approach for the diagnosis in wireless capsule endoscopy imaging, International Journal of Information Technology, 13, 1, 46. ; Magoulas G. (2004), Neural network-based colonoscopic diagnosis using on-line learning and differential evolution, Applied Soft Computing, 4, 4, 369, doi.org/10.1016/j.asoc.2004.01.005 ; Magoulas G. (2006), Neuronal networks and textural descriptors for automated tissue classification in endoscopy, Oncology Reports, 15, 997.