Algorithms and Data-Structures for Compressed Computation

Dear Colleagues,

As the production of massive data has outpaced Moore’s law in many scientific areas, the very notion of algorithm is shaping up to keep the pace. Today’s algorithms must not only be able to deal with large amounts of data, they also must be capable to use resources close to their theoretical limits. Against this landscape, compression has become a fundamental tool to make the storage of this kind of data possible in the first place.

While motivated by the fact that massive data are often very compressible, this approach poses interesting algorithmic challenges: Can we compute directly on compressed data without first decompressing it? Can we propose measures and frameworks to compare different approaches? Can different techniques for compressed computation be unified?

Thanks to more than two decades of successful research on the subject, today we know that many algorithmic marvels are indeed possible. In fact, compression and computation are two sides of the same coin, as they exploit the same underlying structure both for storing and operating on data.

Despite numerous successes in the field, however, several challenges remain open, including (but not limited to) extending techniques to more/less structured and massive data. The goal of this Special Issue is precisely to advance research in the field of compressed computation. We welcome the submission of high-quality papers dealing with compressed data structures, data compression, string/tree/graph processing, and combinatorics.

Dr. Alberto Policriti
Dr. Nicola Prezza
Guest Editors

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