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首页> 外文期刊>Journal of Intelligent Information Systems >REMI: A framework of reusable elements for mining heterogeneous data with missing information
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REMI: A framework of reusable elements for mining heterogeneous data with missing information

机译：REMI：可重用元素的框架，用于在缺少信息的情况下挖掘异构数据

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摘要

Applications targeting smart cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of smart city ecosystems. A major obstacle involves the differences in the levels of available information. In this work, we present REMI, which is a mining framework that handles varying degrees of information availability by providing a meta-solution to missing data. The framework core concept is the REMI layered stack architecture, offering two complementary approaches to dealing with missing information, namely data enrichment (DARE) and graceful degradation (GRADE). DARE aims at inference of missing information levels, while GRADE attempts to mine the patterns using only the existing data.We show that REMI provides multiple ways for re-usability, while being fault tolerant and enabling incremental development. One may apply the architecture to different problem instantiations within the same domain, or deploy it across various domains. Furthermore, we introduce the other three components of the REMI framework backing the layered stack. To support decision making in this framework, we show a mapping of REMI into an optimization problem (OTP) that balances the trade-off between three costs: inaccuracies in inference of missing data (DARE), errors when using less information (GRADE), and gathering of additional data. Further, we provide an experimental evaluation of REMI using real-world transportation data coming from two European smart cities, namely Dublin and Warsaw.
机译：针对智能城市的应用程序可以解决常见的挑战，但是由于智能城市生态系统的异构性，解决方案很难在一个城市之间移植。主要障碍涉及可用信息水平的差异。在这项工作中，我们介绍了REMI，它是一个挖掘框架，通过为丢失的数据提供元解决方案来处理不同程度的信息可用性。框架的核心概念是REMI分层堆栈体系结构，它提供了两种互补的方法来处理丢失的信息，即数据丰富（DARE）和优雅降级（GRADE）。 DARE旨在推断缺失的信息级别，而GRADE尝试仅使用现有数据来挖掘模式。我们证明REMI提供了多种可重用性，同时具有容错能力并能够进行增量开发。可以将体系结构应用于同一域中的不同问题实例，或者将其部署在各个域中。此外，我们介绍了支持分层堆栈的REMI框架的其他三个组件。为了支持该框架中的决策，我们展示了REMI到优化问题（OTP）的映射，该优化问题平衡了三种成本之间的权衡：丢失数据的推断不准确（DARE），使用较少信息时的错误（GRADE），并收集其他数据。此外，我们使用来自两个欧洲智慧城市（都柏林和华沙）的真实交通数据，对REMI进行了实验评估。

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