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The MASSIF platform : a modular and semantic platform for the development of flexible IoT services

机译：massIF平台：用于开发灵活的物联网服务的模块化和语义平台

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In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.
机译：在物联网（IoT）中，数据生成实体会感知其环境，并将这些观察结果传输到数据处理平台以进行进一步分析。通过组合这些感测的数据或通过处理组合的数据，应用程序可以具有上下文感知的概念。合并数据的过程可以包括合并动态感测数据以及将感测数据与背景和历史数据融合。语义可以帮助完成此任务，因为它们已证明可用于数据集成，知识交换和推理中。语义服务对集成的感测数据进行推理，并与背景知识（例如配置文件数据）结合在一起，可以提取有用的信息并支持智能决策。然而，仍然难以实现对所感测的数据和背景知识的结合的高级推理。此外，语义服务之间的协作允许达成复杂的决策。可以适应当前环境的这种协作工作流的动态组成尚未引起足够的重视。在本文中，我们介绍了MASSIF，这是一个用于物联网数据语义标注和推理的数据驱动平台。它允许集成多个模块化推理服务，这些服务可以灵活的方式协作以促进复杂的决策过程。通过让服务指定他们要使用的数据来启用数据驱动的工作流。经过彻底处理，这些服务可以决定与其他消费者共享其决定。通过定义这些服务要使用的数据，它们可以对数据的子集进行操作，从而提高推理效率。此外，这些服务中的每一个都可以在其自己的上下文模型中将使用的数据与背景知识集成在一起，以进行快速的智能决策。为了展示平台的优势，我们详细介绍了两个用例并进行了全面评估。

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