Abstract
This paper analyses the causal relationship between external events and sports content TV audiences. To accomplish this, we explored external data related to sports TV audience behaviour within a specific time frame and applied a Granger causality analysis to evaluate the effect of external events on both TV clients' volume and viewing times. Compared to regression studies, Granger causality analysis is essential in this research as it provides a more comprehensive and accurate understanding of the causal relationship between external events and sports TV viewership. The study results demonstrate a significant impact of external events on the TV clients' volume and viewing times. External events such as the type of tournament, match popularity, interest and home team effect proved to be the most informative about the audiences. The findings of this study can assist TV distributors in making informed decisions about promoting sports broadcasts.

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Abstract
In the last few years we have been seeing a drastic change in the way software is developed. Large-scale software projects are being assembled by a flexible composition of many (small) components possibly written in different programming languages and deployed anywhere in the cloud - the so-called microservices-based applications. The dramatic growth in popularity of microservices-based applications has pushed several companies to apply major refactorings to their software systems. However, this is a challenging task that may take several months or even years. We propose a methodology to automatically evolve monolithic web applications that use object-relational mapping into microservices-based ones. Our methodology receives the source code and a microservices proposal and refactors the original code to create each microservice. Our methodology creates an API for each method call to classes that are in other services. The database entities are also refactored to be included in the corresponding service. The evaluation performed in 120 applications shows that our tool can successfully refactor about 72% of them. The execution of the unit tests in both versions of the applications yield exactly the same results.

Abstract
In a partitioned Bloom Filter (PBF) the bit vector is split into disjoint parts, one per hash function. Contrary to hardware designs, where they prevail, software implementations mostly ignore PBFs, considering them worse than standard Bloom filters (SBF), due to the slightly larger false positive rate (FPR). In this paper, by performing an in-depth analysis, first we show that the FPR advantage of SBFs is smaller than thought; more importantly, by deriving the per-element FPR, we show that SBFs have weak spots in the domain: elements that test as false positives much more frequently than expected. This is relevant in scenarios where an element is tested against many filters. Moreover, SBFs are prone to exhibit extremely weak spots if naive double hashing is used, something occurring in mainstream libraries. PBFs exhibit a uniform distribution of the FPR over the domain, with no weak spots, even using naive double hashing. Finally, we survey scenarios beyond set membership testing, identifying many advantages of having disjoint parts, in designs using SIMD techniques, for filter size reduction, test of set disjointness, and duplicate detection in streams. PBFs are better, and should replace SBFs, in general purpose libraries and as the base for novel designs.

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