DisDaS: Distributed Data Streams in Dynamic Environments

我们目前观察到对大型设备系统的兴趣迅速增长，每个设备系统都永久地观察数据，这些数据通常是在真实的时间内被聚合为有用的信息。这种系统的例子是 (1)全球分布式用户的智能手机收集的信息可以（并且在某些情况下已经）用于聚合信息。除了由提供商完成的监视之外，还可以请求基于位置的信息，如附近的餐馆、关于前方交通的信息、附近的朋友和许多其他种类的信息。(2)汽车生成传感器数据（例如，关于它们的位置、它们的速度、它们的环境以及附近的其他汽车），所述传感器数据用于实现用于在交叉路口驾驶或用于在高速公路上通过的自组织管理。(3)互联网服务提供商的网络的节点观察链路的本地使用，并一起工作以保持网络处于健康状态。 (4)传感器或机器人被部署到某个领域，目的是从观察到的数据中收集有用的信息。聚合的示例是测量温度的（加权）平均值、最小值、最大值。在这个项目中，我们计划为分布式算法的设计和分析奠定基础，这些算法连续计算由众多设备观察到的数据流的聚合信息。这些设备可以是移动的，即能够在平面或空间中移动，并且包含（无线）通信设备和用于观察其环境的传感器。主要的挑战是如何科普设备产生的大量数据。通常，数据流太大并且到达太快而不能完全存储，或者通过网络发送到中央服务器，或者真实的处理。因此，我们必须找到方法来提取有用的信息流使用有限的资源，如内存，通信量和计算时间。我们计划在动态环境中开发连续的分布式算法，同时考虑到设备的移动性和所观察到的事件。这反映了使用智能手机观察环境的移动人员的场景。设备的动态模型和数据流的生成将一方面受到我们的理论模型的激励。此外，我们期待与大数据优先程序算法（SPP 1736）中的项目就此类模型进行有趣的合作，这些项目涉及社交网络。

We currently observe rapidly growing interest in large systems of devices, each of which permanently observes data that has - often in real time - to be aggregated to useful information. Examples for such systems are (1) Information gathered by the smartphones of world-wide distributed users can (and in some cases is already) used for aggregating information. Besides the monitoring done by the providers, also position based information like nearby restaurants, information about traffic ahead, nearby friends and many further kinds of information can be requested. (2) Cars generate sensor data (about, for example, their position, their speed, their environment, and other cars nearby) that is used in order to realise a self-organised management for driving in intersections or for passing on freeways. (3) Nodes of a network of an Internet Service Provider observe local usage of links, and work together in order to keep the the network in a healthy state. (4) Sensors or robots are deployed to some field, with the aim of aggregating useful information from observed data. Examples for aggregation are (weighted) average, minimum, maximum of measured temperature. In this project we plan to lay the foundations for the design and analysis of distributed algorithms that continuously compute aggregated information of streams of data which are observed by a multitude of devices. These devices may be mobile, i.e. capable of moving in the plane or in space, and contain both (wireless) communication devices and sensors for observing their environment. The major challenge is to cope with the huge amount of data generated by the devices. Typically, the data streams are too big and arrive too fast to be completely stored, or sent to a central server through a network, or processed in real time. Thus we have to find ways to extract useful information from the streams using restricted resources like memory, communication volume and computation time. We plan to develop continuous distributed algorithms in dynamic environments, taking both mobility of the devices and of the observed events into account. This reflects the scenario of moving people with smartphones who observe their environments. The models for the dynamics of the devices and the generation of the data streams will on one side be motivated by our theoretical models. Moreover, we expect interesting cooperation about such models with projects within the Priority Programme Algorithms for Big Data (SPP 1736), that deal with social networks.