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Rediscovering Impacts of Collaboration through Workflow Analysis to Value Service Development
Chengxiang Ren,Yucong Duan,Zhangbing Zhou,Guohua Fu,Zhen Guo,Honghao Gao 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.11
Information and service economies are developing rapidly in recent years with the development of information technology and new applications. Software and service development are becoming more and more complex relating to different participants, organizations and alliances. This phenomenon has caused urgent requirements on economic and technical fusion through coordination and collaboration among stakeholders. This paper addresses the gap between business layer and technical development in roles and activities. We then propose a combination of economic value and profits and technical service development processes. We also consider value exchange under profit sharing contracts in participants' workflows. A simulation and an experiment are performed to show related impacts.
( Dawei Sun ),( Hongbin Yan ),( Shang Gao ),( Zhangbing Zhou ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.7
In big data era, fresh data grows rapidly every day. More than 30,000 gigabytes of data are created every second and the rate is accelerating. Many organizations rely heavily on real time streaming, while big data stream computing helps them spot opportunities and risks from real time big data. Storm, one of the most common online stream computing platforms, has been used for big data stream computing, with response time ranging from milliseconds to sub-seconds. The performance of Storm plays a crucial role in different application scenarios, however, few studies were conducted to evaluate the performance of Storm. In this paper, we investigate the performance of Storm under different application scenarios. Our experimental results show that throughput and latency of Storm are greatly affected by the number of instances of each vertex in task topology, and the number of available resources in data center. The fault-tolerant mechanism of Storm works well in most big data stream computing environments. As a result, it is suggested that a dynamic topology, an elastic scheduling framework, and a memory based fault-tolerant mechanism are necessary for providing high throughput and low latency services on Storm platform.