Special issue on “advances in visual analytics and mining visual data”

Visual and multimedia analytics provides an emerging field of research combining strengths from information analytics, geospatial analytics, scien-tific analytics, statistical analytics, knowledge discovery, data management and knowledge representation, presentation, production and dissemi-nation, cognition, perception, and interaction (Chen, Chiang and Storey, 2012). The aim is to gain insight into homogeneous, contradictory, and incomplete data through the combination of automatic analysis methods with human background knowledge and intuition. While the scope of visual analytics is broad, one principle that has emerged over the years is the need for visual analytics systems to leverage computational methods in data mining, knowledge discovery, and machine learning for large-scale data analysis. In these systems, the human operator works alongside the computational processes in an integrated fashion. Therefore, computing systems or services can sift through large amounts of data and identify the relevant information, while the human interactively explores the reduced data space to discover trends and patterns and make informed decisions. These two components operate in coordination, allowing for a continuous and cooperative analytical loop (Cybulski et al., 2015; Valdez et al., 2016). Top papers from Data 2018, Madrid, Spain and the best paper from FEMIB 2019 in Crete, Greece, have been invited. Through a robust and competitive review process, six papers have been selected. The summary of their contributions is as follows. Arhipova et al. (2020) propose a data aggregation approach for mobile phones, which was conducted at 15-min intervals in the area of each cellular base station. The case study examines all of Latvia's municipalities, analysing the economic activity level in each municipality in comparison to the mobile phone activity in three periods: 2015 – 2016, 2017, and 2018. The authors concluded that economic activity in municipalities could be estimated, and the positive dynamics of regional development have been detected. Such data and the data analytics method, which provides an understanding of how economic activities evolve in real-time in particular to locations and economic activity centres, can improve regional development planning and plan implementation. In order to assess which are the centres of economic activity in each municipality and its sphere of influence, the patterns of human commuting and fluctuations of internal activity on workdays and weekends/holidays in 2017 – 2018 evaluation of an efficient solution to the cold start problem. Third, this method works well even when no co-rated items exist between two users. They demonstrate that their proposal is efficient in terms of accuracy, execution time, and applicability. Their proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and execution time as low as 0.03 seconds, while the existing similarity measures from the literature achieve an MAE of 0.88 at their best. These results demonstrate the superiority of their proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time. Vangipuram et al. (2020) propose (a) a novel imputation technique for imputation of missing data values; (b) a classifier based on feature transformation to perform classification and (c) imputation measure for similarity computation between any two instances that can also be used as the similarity measure. The performance of the proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using the proposed imputation technique. For experiments, authors have used an open-source dataset named distributed smart space orchestration system publicly available from Kaggle. Experimental results are also validated using the Wilcoxon non-parametric statistical test. The performance of their proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when both the proposed imputation and classification technique are applied.