Special Issue "Data Processing for Machine Learning"
Deadline for manuscript submissions: 30 September 2022 | Viewed by 199
Interests: data processing for machine learning (ML); optimization of ML pipelines; ML model management and materialization; learned and workload-aware indexing; graph processing
Machine learning (ML) depends on data processing operations, such as data acquisition and cleaning, preprocessing and feature extraction, and selection of relevant subsets of data for the ML task at hand. Consequently, efficient data processing is essential for efficient ML applications. This is especially the case for applications that involve the continuous deployment of ML models (e.g., for real-time, on-demand predictions) as well as continuous data updates (e.g., in streaming settings).
Motivated by the above, we launch the Special Issue on Data Processing for Machine Learning and cordially invite your contributions.
Specific topics of interest include, but are not limited to:
- data acquisition and feature extraction for ML; monitoring data and feature quality; data and feature versioning;
- data drifts and model updates; model materialization, logging, and versioning;
- data sampling, summarization, and core sets for ML;
- in-database ML, ML over distributed data, ML over data streams;
- inference query processing and optimization;
- systems and programming languages for joint data and model management; data processing pipelines;
- ML over private data, ML compliance with data-protection regulations.
We welcome different types of articles, including articles that propose new algorithms for a general class of problems; or present experimental evaluations of existing algorithms; or explain how challenges related to data processing are addressed in the context of real ML systems and applications.
Articles that are accepted for publication in this Special Issue are required to (i) address data processing challenges in the context of ML, and (ii) make substantially novel contributions, with clearly stated and well-supported claims.
We are looking forward to your contributions.
On behalf of the editorial board,
Dr. Michael Mathioudakis
Prof. Dr. Jukka Nurminen
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- data processing
- machine learning
- end-to-end machine learning