Abstract
Nowadays, enterprises generate massive amounts of heterogeneous structured and unstructured data within their factories and attempt to store them inside data lakes. However, potential users, such as data scientists, encounter problems when they have to find, analyze and especially understand the data. Possible existing solutions use ontologies as data governance technique for establishing a common understanding of data sources. While ontologies build a solid basis for representing knowledge, their construction is a very complex task which requires the knowledge of multiple domain experts. However, in fast and continuously evolving enterprises a static ontology will be quickly outdated.
To cope with this problem, we developed the information processing platform ESKAPE. With the help of ESKAPE, data publishers annotate their added data sources with semantic models providing additional knowledge which enables later users to process, query and subscribe to heterogeneous data as information products. Instead of solely creating semantic models based on a pre-defined ontology, ESKAPE maintains a knowledge graph which learns from the knowledge provided within the semantic models by data publishers. Based on the semantic models and the evolving knowledge graph, ESKAPE supports enterprises’ data scientists in finding, analyzing and understanding data.
To evaluate ESKAPE’s usability, we conducted an open competitive hackathon where users had to develop mobile applications. The received feedback shows that ESKAPE already reduced the workload of the participants for getting the appropriate required data and enhanced the usability of dealing with the available data.
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Advanced Message Queuing Protocol: An open standard application layer protocol.
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Message Queue Telemetry Transport: A lightweight publish-subscribe messaging protocol.
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Pomp, A., Paulus, A., Jeschke, S., Meisen, T. (2018). Enabling Semantics in Enterprises. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_20
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