Data analytics have the potential to increase the value of data emitted from smart devices in usercentric Internet of Things environments, such as smart home, drastically. In order to allow businesses and end-consumers alike to tap into this potential, appropriate analytics architectures must be present. Current solutions in this field do not tackle all of the diverse challenges and requirements, which were identified in previous research. Specifically, personalized, extensible analytics solutions, which still offer the means to address big data problems are scarce. In this paper, we therefore present an architectural solution, which was specifically designed to address the named challenges. Furthermore, we offer insights into the prototypical implementation of the proposed concept as well as an evaluation of its performance against traditional big data architectures.
Data analytics is an important component for the benefit and growth of the Internet of Things (IoT). The utilization of data generated by a variety of heterogeneous smart devices offers the possibility of gaining meaningful insights into various aspects of the daily lives of end consumers, the environment and weather, but also into value-added processes of business and industry. The potential benefits derived from analyzing IoT data can be further enhanced by advancing developments in streaming and machine learning technologies. A critical factor in the application of these technologies are the underlying analytics architectures. These must overcome a variety of different challenges that are influenced by technical, but also legal or personal constraints and differ in importance and impact depending on the IoT application domain in which such an architecture is to be deployed. Solutions presented by previous research address only a handful of these challenges. An important capability to address the variety of challenges that arise from this situation is the ability to support the hybrid deployment of analytics pipelines at different network layers. Consequently, in this work, we propose an architectural solution that enables hybrid analytics pipeline deployments, addresses the challenges described in previous scientific literature and can be deployed in various IoT application domains. Finally, we experimentally evaluate the proposed solution.
The operation of open-cast lignite mines is a large intervention in nature, making the areas uninhabitable even after closing the mines without renaturation processes. Renaturation of these large areas requires a regional planning process which is tied to many conditions and restrictions, such as environmental protection laws. The related information is available only as unstructured text in a variety of documents. Associated temporal aspects and the geographical borders to these textual information have to be linked manually so far. This process is highly time-consuming, error-prone, and tedious. Therefore, the knowledge of experts is often used, but this does not necessarily include all the relevant information. In this paper, we present a system to support the experts in decision-making of urban planning, renaturation, and redevelopment projects. The system allows to plan new projects, while considering spatial and temporal restrictions extracted from text documents. With this, our presented system can also be used to verify compliance with certain legal regulations, such as nature conservation laws.
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