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Functional programming for dynamic and large data with self-adjusting computation
2014
SIGPLAN notices
Combining type theory, language design, and empirical work, we present techniques for computing with large and dynamically changing datasets. Based on lambda calculus, our techniques are suitable for expressing a diverse set of algorithms on large datasets and, via self-adjusting computation, enable computations to respond automatically to changes in their data. Compared to prior work, this work overcomes the main challenge of reducing the space usage of self-adjusting computation without
doi:10.1145/2692915.2628150
fatcat:q7xsdfgiyzfb5ek753s3r5zjlm