Circadian Patterns in Twitter

In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspects of activity on Twitter. First, we analyse the daily and weekly number of posts, through which we find clear circadian (daily) patterns emerging in the use of Twitter for multiple languages. We see that both the number of tweets and the daily and weekly activity patterns differ between languages. Second, we analyse the progression of individual tweets through retweets in the Twittersphere. We find that the size of these progressions follow a power-law distribution. Furthermore, we build an algorithm to analyse the actual structure of the progressions and use this algorithm on a limited set of tweets. We find that retweet trees show a star-like structure

Role of the TPC in the cloud age

In recent year the TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC) series have had significant influence in defining industry standards. The 11th TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC 2019) organized an industry panel on the “Role of the TPC in the Cloud Age”. This paper summaries the panel discussions

A benchmarking model for sensors in smart environments

In smart environments, developers can choose from a large variety of sensors supporting their use case that have specific advantages or disadvantages. In this work we present a benchmarking model that allows estimating the utility of a sensor technology for a use case by calculating a single score, based on a weighting factor for applications and a set of sensor features. This set takes into account the complexity of smart environment systems that are comprised of multiple subsystems and applied in non-static environments. We show how the model can be used to find a suitable sensor for a use case and the inverse option to find suitable use cases for a given set of sensors. Additionally, extensions are presented that normalize differently rated systems and compensate for central tendency bias. The model is verified by estimating technology popularity using a frequency analysis of associated search terms in two scientific databases