Algorithms in Data Reduction
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".
Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 470
Special Issue Editors
Interests: fault tolerance; checkpoint/restart; silent data corruption; lossy compression; scientific computing; high-performance computing
Special Issue Information
Dear Colleagues,
Large-scale applications, IoT devices, and scientific instruments are capable of generating massive amounts of data. As the size of datasets continues to grow, their volume can significantly hamper their ability to be transmitted, stored, visualized, processed, and analyzed. To combat these bottlenecks, new data reduction techniques are needed. Data reduction can either be lossless or lossy and can be expressed in many forms, from compression and sampling, to machine-learning techniques. Further, when lossy data reduction is used, how such data loss affects the quantities of interest, how much uncertainty is introduced, and how much uncertainty can be allowed for effective domain science all become important topics.
To account for the expected order-of-magnitude increases in dataset size over the next few years, new algorithms and analysis techniques must be developed. We invite authors to submit original, high-quality research that advances the field of data reduction. We are interested in all aspects of algorithms that improve data reduction and those that access the quality of lossy reduction methods. Topics of interest include, but are not limited to, the following:
- Novel data sampling algorithms;
- Novel lossy or lossless data compression algorithms;
- AI/ML to improve data reduction;
- Specialized data reduction workflows;
- Lossy compression evaluation tools;
- Accelerated data reduction algorithms;
- Data analysis and visualization on lossy reduced data;
- Region of interest identification;
- Uncertainty quantification for lossy reduction methods;
- Data reduction runtime systems;
- Metrics to evaluate quality of lossy methods;
- Hardware and data reduction co-design.
Prof. Dr. Jon Calhoun
Dr. Ayan Biswas
Guest Editors
Manuscript Submission Information
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Keywords
- big data
- lossless compression
- lossy compression
- data reduction
- sampling
- feature-based reduction
- reconstruction
- machine learning
- saliency detection
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