Nowadays, we are witnessing a rapid increase of spatio-temporal data that permeates different aspects of our everyday life such as mobile geolocation services and geo-located weather sensors. This big amount of data needs innovative analytics techniques to ease correlation and comparison operations. Visual Analytics is often advocated as a doable solution thanks to its ability to enable users to directly obtain insights that support the understanding of the data. However, the grand challenge is to offer to visual analytics software an integrated view on top of multi-source, geo-located, time-varying data. The abstractions described in the FraPPE ontology address this challenge by exploiting classical image processing concepts (i.e. Pixel and Frame), a consolidated geographical data model (i.e. GeoSparql) and a time/event vocabulary (i.e. Time and Event ontologies). FraPPE was originally developed to represent telecommunication and social media data in an unified way and it is evaluated modeling the dataset made available by ACM DEBS 2015 Grand Challenge.
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机译：如今，我们看到时空数据迅速增加，这些时空数据渗透到我们日常生活的各个方面，例如移动地理位置服务和地理位置天气传感器。大量数据需要创新的分析技术来简化关联和比较操作。由于Visual Analytics具有使用户直接获取支持数据理解的见解的能力，因此经常被提倡为可行的解决方案。但是，最大的挑战是为可视化分析软件提供基于多源，地理位置，时变数据的集成视图。 FraPPE本体中描述的抽象通过利用经典的图像处理概念（即像素和帧），合并的地理数据模型（即GeoSparql）和时间/事件词汇表（即时间和事件本体）来解决这一挑战。 FraPPE最初是为了以统一的方式表示电信和社交媒体数据而开发的，并且对ACM DEBS 2015 Grand Challenge提供的数据集进行了建模评估。
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