Paper detail

Facilitating Rapid Prototyping in the OODIDA Data Analytics Platform via Active-Code Replacement

OODIDA (On-board/Off-board Distributed Data Analytics) is a platform for distributed real-time analytics, targeting fleets of reference vehicles in the automotive industry. Its users are data analysts. The bulk of the data analytics tasks are performed by clients (on-board), while a central cloud server performs supplementary tasks (off-board). OODIDA can be automatically packaged and deployed, which necessitates restarting parts of the system, or all of it. As this is potentially disruptive, we added the ability to execute user-defined Python modules on clients as well as the server. These modules can be replaced without restarting any part of the system; they can even be replaced between iterations of an ongoing assignment. This feature is referred to as active-code replacement. It facilitates use cases such as iterative A/B testing of machine learning algorithms or modifying experimental algorithms on-the-fly. Consistency of results is achieved by majority vote, which prevents tainted state. Active-code replacement can be done in less than a second in an idealized setting whereas a standard deployment takes many orders of magnitude more time. The main contribution of this paper is the description of a relatively straightforward approach to active-code replacement that is very user-friendly. It enables a data analyst to quickly execute custom code on the cloud server as well as on client devices. Sensible safeguards and design decisions ensure that this feature can be used by non-specialists who are not familiar with the implementation of OODIDA in general or this feature in particular. As a consequence of adding the active-code replacement feature, OODIDA is now very well-suited for rapid prototyping.

preprint2020arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.