Abstract
In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for this purpose are very heterogeneous and complex. As a result, tasks for analyzing data, such as collecting, searching, understanding and processing data, become very time-consuming. This makes it difficult to realize visions, such as the Internet of Production, which pursues the goal of guaranteeing the availability of real-time information at any time and place in an industrial setting. To reduce the time to analytics in such scenarios, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline provides an abstraction to all tasks related to data acquisition. The main goal is, therefore, the controllable access to data and meta information contained in machines and other systems on the shop floor. Additionally, it provides the possibility to forward the collected data to a configurable endpoint, such as a data lake. ESKAPE acts as one of those endpoints enabling semantic data integration and processing. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world industrial use case.
Highlights
Modern information technology has led the way to an era of ubiquitous information availability in our private lives
To reduce the time to analytics for data sources, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline that enables controllable access to data and meta information contained in machines and other systems on the shop floor and forwards the data to a configurable endpoint, such as a data lake
The semantic data platform Evolving Semantic Knowledge Aggregation and Processing Engine (ESKAPE) is capable of integrating data sets of different formats (e.g., CSV, XML, JavaScript Object Notation (JSON)), we demonstrate the data integration based on an abstract data set supplied in JSON
Summary
Modern information technology has led the way to an era of ubiquitous information availability in our private lives. Large amounts of data are generated every second to enable the subsequent collection, storage, usage and analysis of this data for various applications. The enterprises that focus on analyzing this data and create products out of it follow a green-field approach that enables them to set up infrastructures that are exactly designed for this purpose. The application of these methods in already existing environments proves to be challenging. Technologies 2018, 6, 86 have been set up over the last few decades, tailored to the needs of specific tasks. Enterprises run whole landscapes of very heterogeneous and complex infrastructures. Trying to apply these new innovative data driven methods often is limited by the design of the systems, but must be enabled exactly for this purpose. One area in which such environments can be found is the industrial sector
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