The efficient and real-time exploitation of large streaming data sources and stored data poses many questions regarding the underlying platform, including: 1) Performance - how can the potential performance of the platform be exploited effectively by arbitrary applications; 2) Guarantees - how can the platform support guarantees regarding processing streaming data sources and accessing stored data; and 3) Scalability - how can scalable platforms and applications be built. The fundamental challenge addressed by the project is to enable application development using an industrial strength programming language that enables the necessary performance and performance guarantees required for real-time exploitation of large streaming data sources and stored data. The project's vision is to create a Java Platform that can support a range of high-performance Intelligent Information Management application domains that seek real-time processing of streaming data, or real-time access to stored data. This will be achieved by developing Java and UML modelling technologies to provide: 1) Architectural Patterns - using predefined libraries and annotation technology to extend Java with new directives for exploiting streaming I/O and parallelism on high performance platforms; 2) Virtual Machine Extensions - using class libraries to extend the JVM for scalable platforms; 3) Java Acceleration - performance optimisation is achieved using Java JIT to Hardware (FPGA), especially to enable real-time processing of fast streaming data; 4) Performance Guarantees - will be provided for common application real-time requirements; and 5) Modelling - of persistence and real-time within UML / MARTE to enable effective development, code generation and capture of real-time system properties. The project will use financial and web streaming case studies from industrial partners to provide industrial data and data volumes, and to evaluate the developed technologies. 318763

Description in Czech
Projekt se zabývá technologiemi pro vývoj aplikací pracujícími s rozsáhlými proudy dat v reálném čase. Cílem je použít jazyk Java, resp. vytvořit platformu postavenou na technologiích využívající tento jazyk, a demonstrovat výhody řešení na aplikacích v oblasti "big data" v reálném čase.

Keywords
Performance guarantees, realtime, Big Data, streaming data, stored data, parallelisation, Java

English

Smrž Pavel, doc. RNDr., Ph.D. - principal person responsible
Dytrych Jaroslav, Ing., Ph.D. - fellow researcher
Fučík Otto, doc. Dr. Ing. - fellow researcher
Kouřil Jan, Ing. - fellow researcher
Musil Petr, Ing., Ph.D. - fellow researcher
Otrusina Lubomír, Ing. - fellow researcher
Zachariáš Michal, Ing., Ph.D. - fellow researcher

Department of Computer Graphics and Multimedia
- responsible department (2.7.2013 - 30.11.2015)
Department of Computer Graphics and Multimedia
- beneficiary (2.7.2013 - 30.11.2015)

RYCHLÝ, M.: SchedulingAdvisor; Scheduling Advisor for Performance Tuning of Juniper Applications. https://github.com/juniper-project/sched-advisor. URL: https://github.com/juniper-project/sched-advisor. (software)
Detail

RYCHLÝ, M.; ŠKODA, P.; SMRŽ, P. Heterogeneity-Aware Scheduler for Stream Processing Frameworks. International Journal of Big Data Intelligence, 2015, vol. 2, no. 2, p. 70-80. ISSN: 2053-1397.
Detail

RYCHLÝ, M.; ŠKODA, P.; SMRŽ, P. Scheduling Decisions in Stream Processing on Heterogeneous Clusters. In 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems. Birmingham: IEEE Computer Society, 2014. p. 614-619. ISBN: 978-1-4799-4325-8.
Detail

ŠKODA, P.: HeterogeneityScheduler; Heterogeneity-Aware Scheduler for Stream Processing Frameworks. https://github.com/radkovo/webstorm. URL: https://github.com/radkovo/webstorm. (software)
Detail

KOUŘIL, J.: TwitterStorm; Apache Storm topology for processing tweets. https://github.com/ikouril/twitterstorm. URL: https://github.com/ikouril/twitterstorm. (software)
Detail

Responsibility: Smrž Pavel, doc. RNDr., Ph.D.