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Using a Domain-specific Modeling Language for Analyzing Harmonizing and Interfering Public and Private Sector Goals A Scenario in the Context of Open Data for Weather Forecasting

机译:使用域特定的建模语言,用于分析协调和干扰公共和私营部门目标的统一和私营部门目标在天气预报的开放数据中的一个场景

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The opening of data by public organizations can result in innovations and new business models in the private sector. Yet, the public and private sectors may have different and sometimes interfering objectives. In this paper, we analyze the goals of an open data business model for weather forecasting using the multi-perspective goal modelling language GoalML. The public and private sectors partly share similar goals, but creating public value was found to be interfering (to some extent) with the private sector objective of making profit. One of the values of GoalML is that it clearly shows harmonizing and interfering goals. The interfering goals are one of the explanations for a slow adoption of open data. Mechanisms need to be developed to deal with them.
机译:公共组织的数据开通可能导致私营部门的创新和新商业模式。然而,公共和私营部门可能有不同,有时干扰目标。在本文中,我们使用多视角目标建模语言GoalML分析了天气预报的开放数据业务模型的目标。公共和私营部门部分分享了类似的目标,但发现了公共价值在某种程度上会受到私营部门的盈利目标。 Goalml的一个值是它清楚地显示了协调和干扰目标。干扰目标是缓慢采用开放数据的解释之一。需要制定机制来处理它们。

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