With the advancement of social sensing technologies, digital maps have recently witnessed a tremendous evolution with the aim of integrating enriched semantic layers from heterogeneous and diverse data sources. Current generations of digital maps are often crowd-sourced, allow interactive route planning, and may contain live updates, such as traffic congestion states. Within this context, we believe that the next generation of maps will introduce the concept of extracting Events of Interest (EoI) from crowdsourced data, and displaying them at different spatial scales based on their significance. This paper introduces Hadath¹, a scalable and efficient system that extracts social events from unstructured data streams, e.g. Twitter. Hadath applies natural language processing and multi-dimensional clustering techniques to extract relevant events of interest at different map scales, and to infer the spatio-temporal scope of detected events. Hadath also implements a hierarchical in-memory spatio-temporal indexing scheme to allow efficient and scalable access to raw data, as well as to extracted clusters of events. Initially, data packets are processed to discover events at a local scale, then, the proper spatio-temporal scope and the significance of detected events at a global scale is determined. As a result, live events can be displayed at different spatio-temporal resolutions, thus allowing a smooth and unique browsing experience. Finally, to validate our proposed system, we conducted experiments on real-time and historical social media streams.

中文翻译：

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构建具有社交功能的事件丰富的地图

随着社会传感技术的进步，数字地图最近见证了巨大的发展，其目的是整合来自异构和多样化数据源的丰富语义层。当前一代的数字地图通常是众包的，可以进行交互式路线规划，并且可能包含实时更新，例如交通拥堵状态。在此背景下，我们相信下一代地图将引入从众包数据中提取关注事件（EoI）的概念，并根据其重要性在不同的空间比例下显示它们。本文介绍了哈达斯¹，一种可扩展且高效的系统，可从非结构化数据流（例如Twitter）中提取社交事件。Hadath应用自然语言处理和多维聚类技术来提取不同地图比例下的相关关注事件，并推断检测到事件的时空范围。Hadath还实现了分层的内存时空索引机制，以允许高效且可伸缩地访问原始数据以及提取的事件簇。最初，对数据包进行处理以发现局部范围的事件，然后确定适当的时空范围和全局范围内检测到的事件的重要性。结果，可以以不同的时空分辨率显示实时事件，从而提供流畅而独特的浏览体验。最后，