The RePhrase Extended Pattern Set for Data Intensive Parallel Computing

High-performance parallel systems for data-intensive computing

High-performance parallel systems for data-intensive computing

Data Intensive Computing for Bioinformatics

Parallel Computing for Process Mining (Extended abstract)

Many process mining tools and techniques produce output models based on the counting of transitions between tasks or users in an event log. Although this counting can be performed in a forward pass through the event log, when analyzing large event logs according to different perspectives it may become impractical or timeconsuming to perform multiple such passes. In this work, we show how transi...

Data-Parallel Computing Meets STRIPS: Extended Technical Report

The increased demand for distributed computations on big data has led to solutions such as SCOPE, DryadLINQ, Pig, and Hive, which allow the user to specify queries in an SQL-like language, enriched with sets of user-defined operators. The lack of exact semantics for user-defined operators interferes with the query optimization process, thus putting the burden of suggesting, at least partial, qu...

Asymptotic algorithm for computing the sample variance of interval data

The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the samp...