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Prof. Dr. M. Jarke
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RWTH Aachen
Informatik 5
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Automatic Provenance Generation for Serverless Computing

Thesis type
  • Bachelor
Status Running
Supervisor(s)
Advisor(s)

In the context of this work, the student should extend the Function-as-a-Service (FaaS) paradigm of serverless computing with concepts for the automatic tracking of provenance information or data lineage (information about data origins, uses, actors and involved processes over time, similar to a GIT revision history for data) and implement a corresponding framework using a readily available FaaS platform, such as Kubeless, OpenFaaS or the Fn Project, as well as the existing factlib.js library for semi-automatic provenance tracking, that was developed as a result of previous work.

In the Internet of Things, and especially the Internet of Production, massive amounts of semi-structured data, such as sensor readings, process control data, log data etc., are generated at an ever increasing pace. While this data is abundant, extracting information from these source is typically hard due to the lack of semantic interpretability of the data, further rendering continuous information sharing across organizational boundaries practically infeasible. Especially the lack of versioning information, data origins and influences limits interorganizational collaboration.

Recently, resource oriented architectures have seen grater adoption in manufacturing environments (c.f. e.g. https://books.google.de/books?id=F119DwAAQBAJ). While such approaches appear promising for the future of an Internet of Production, reusability and interoperability of the data produced in the context of such systems is limited by the lack of a common computing model.

In the context of this work, the student should extend the Function-as-a-Service (FaaS) paradigm of serverless computing with concepts for the automatic tracking of provenance information or data lineage (information about data origins, uses, actors and involved processes over time, similar to a GIT revision history for data) and implement a corresponding framework using a readily available FaaS platform, such as Kubeless, OpenFaaS or the Fn Project, as well as the existing factlib.js library for semi-automatic provenance tracking, that was developed as a result of previous work.

If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to gleim@dbis.rwth-aachen.de

Prerequisites

- JavaScript / TypeScript
- Knowledge of functional programming principles is a plus

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