Toward a Message-Oriented Application Model and its Middleware Support in Ubiquitous Environments

Liao, Chun-Feng; Jong, Ya-Wen; Fu, Li‐Chen

doi:10.1109/mue.2008.24

Cited by 14 publications

(5 citation statements)

References 15 publications

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“…Asynchronous messaging relies on the message-oriented middleware (MOM). The MOM creates a "software bus" for integrating heterogeneous applications [12]. Messaging is distributed across applications running on different servers; its reliability and robustness is crucial to MOM.…”

Section: Pos(isgc 2012)008mentioning

confidence: 99%

A Novel Data Analysis Service Oriented Architecture Using R Statistics Engine

Lee¹

2012

Proceedings of the International Symposium on Grids and Clouds (ISGC) 2012 — PoS(ISGC 2012)

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Making quality business decisions relies on the efficiency of data analysis processes. There are many existing Business Intelligence (BI) software products providing rich features to simplify the data analysis processes, but serving statistical purpose as effective and flexible as GNU-R (R) is rare. R is a highly extensible language and runtime environment providing a variety of statistical and graphical features. In enterprise environments, software records business activities in various forms such as files, database, and streaming data. Currently analysts conduct the data analysis in an offline mode using statistical software; it means that the analysts are manually (1) extracting the desired data; (2) storing extracted data into files; (3) manipulating the software; (4) drawing analytical results, and (5) making the inferences. Automating the statistical procedures-by directly extracting the data-will expedite the decision-making sooner and less costly. It is a common practice that enterprises build applications under Service-Oriented Architecture (SOA) to achieve their operation excellence. This paper proposes a serviceoriented statistics framework integrating R to improve the quality of decision-making by automatically extracting the business live data directly. It also presents how such a system was developed from a software engineering perspective to benefit business decision making.

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Section: Pos(isgc 2012)008mentioning

confidence: 99%

A Novel Data Analysis Service Oriented Architecture Using R Statistics Engine

Lee¹

2012

Proceedings of the International Symposium on Grids and Clouds (ISGC) 2012 — PoS(ISGC 2012)

View full text Add to dashboard Cite

show abstract

“…Asynchronous messaging relies on message-oriented middleware (MOM). The MOM creates a "software bus" for integrating heterogeneous applications [12]. Messages are distributed across applications running on different servers; its reliability and robustness is crucial to MOM.…”

Section: The Architecture Of Statistical Service Engine Solutionmentioning

confidence: 99%

A Service Oriented Analytics Framework for Multi-Level Marketing Business

Lee¹

2012

JSEA

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Today’s enterprises have accumulated vast amount of data and keep exploding by business activities. These datasets may contain potential undiscovered business strategies as a key basis of competition; underpin new waves of productivity growth, innovation, and consumer surplus. Data analysis is crucial in making managerial decisions. Although there are many Business Intelligence (BI) software of commercial and open source, but serving statistical purpose as exuberant as GNU-R (R) is rare. R is a highly extensible language and environment for providing a variety of statistical and graphical features. In enterprise environment, the source data are stored in various forms such as files, database, and streaming data. Currently analysts conduct data analysis in offline mode using statistical software. The offline mode means analysts 1) extract the desired data; 2) store extracted data into files; 3) manipulate software; 4) draw analytical results; 5) generate the inferences. Automating the statistical procedures by directly pulling source data will make critical decision sooner and less costly. It is a common practice that enterprise adopts Service-Oriented Architecture (SOA) to achieve its operation excellence. Since the business applications populate the source data during the operation processes, pulling the source data directly under SOA is the most effective way of data analysis. This paper demonstrates how service-oriented statistics engine was developed and how such a system benefits the business decision-making

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“…In this paper, OSGi is advanced as a good solution for standardizing life cycle management of sensor‐related components. A similar approach is presented in , where a message‐oriented application model is designed for data dissemination in a ubiquitous system and OSGi is used for executing some of its applications and actuators.…”

Section: Related Workmentioning

confidence: 99%

Distributed OSGi built over message‐oriented middleware

2011

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This paper describes key aspects of remote service invocation in federations of OSGi containers. It refers to the OSGi Remote Service Admin specification and describes its efficient implementation over messageoriented middleware. Scalability problems of several different approaches to implementation are identified, and a solution in a form of innovative Remote Service Admin model extension is proposed. The extension, named On-demand Remote Service Admin, is analyzed and validated in the context of a motivating scenario. Validation includes performance and scalability evaluation, which confirms that all assumed requirements have been satisfied by the constructed prototype. Finally, the presented research is compared with related works.

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Toward a Message-Oriented Application Model and its Middleware Support in Ubiquitous Environments

Abstract: Abstract

Cited by 14 publications

References 15 publications

A Novel Data Analysis Service Oriented Architecture Using R Statistics Engine

A Novel Data Analysis Service Oriented Architecture Using R Statistics Engine

A Service Oriented Analytics Framework for Multi-Level Marketing Business

Distributed OSGi built over message‐oriented middleware

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