## 4 Discussion  
Results show that the proposed iterative kappa architecture is useful for processing data and for determining patterns in real time. From the correlation analysis, it was found that there is a relation between the activity in social networks, i.e., Twitter, and the behavior of cryptocurrency markets. This evidences a positive correlation between the number of tweets and the buy and sell volumes of the cryptocurrencies. The findings support previous studies [19; 22; 23; 24], in which it was found that the number of tweets and sentiment were positively correlated with cryptocurrencies' transaction volumes and prices. In addition, by means of the \(k\)-means clustering approach, it was found that some data lie outside the common trends regarding transaction volumes of the cryptocurrencies. We have identified the outliers by grouping data in three clusters; two of them correspond to a steady behavior of the cryptocurrencies, while the third gathers data related to unusual transaction volumes. Thus, this latter group is useful for identifying anomalous behaviors in the market which are characterized mainly by a higher volume of tweets with a more positive sentiment, and higher transaction volumes. From the executed \(k\)-means clustering approach, we have found that around 14% of data fall in the third cluster. In that cluster, on average, Bitcoin (BTC) was sold and bought around 128% and 170% more times than in cluster 1, while for Ethereum (ETH), the percentages were 54% and 52%, respectively, in comparison with cluster 2, thus resulting in higher transaction volumes. In both cases, the number of tweets was around 9.5% higher than in the first two clusters. Additionally, the sentiment of the tweets shows higher values (12% for Bitcoin (BTC) and 25% for Ethereum(ETH)) in the third cluster. The previous findings demonstrate that positive sentiment in the environment regarding cryptocurrencies promotes the activity in the market, thus giving sense to the correlation found between the number of tweets and the buy and sell volumes. The proposed architecture may be misidentified with a lambda architecture because both have a batch step; nevertheless, they do accomplish different tasks, and thus different purposes. While the lambda architecture contains an extra batch layer that receives data simultaneously with the streaming layer, our proposed variant of the kappa architecture applies a batch step inside the existent serving layer to temporarily store processed data. In that sense and in comparison with the simple kappa architecture, our proposal has the advantage of being able to relate various datasets in the second iteration by considering a different time span than the one selected for data streaming at the first iteration. It is a flexible architecture, which offers an alternative solution for real-time data processing and modeling from the perspective of traditional techniques, i.e., relational databases [44]. The application of our proposal is not limited to the execution of a \(k\)-means clustering approach. Other unsupervised machine learning algorithms may be explored, such as hierarchical cluster analysis (HCA) or fuzzy C-means clustering, which could help find different patterns regarding the behavior of cryptocurrencies. In addition, supervised machine learning algorithms may be supported. Some other studies proposed a similar application of the kappa architecture to process and model data in real time [33; 45]; nevertheless, our proposal differs in the way data are processed. None of the previous studies found combined an iterative approach with a batch step involving a database management system