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International Journal of Big Data Intelligence (3 papers in press)
Temporal outlier analysis of Online Civil Trial cases based on graph and process mining techniques by Beniamino Di Martino, Luigi Colucci Cante, Antonio Esposito, Pietro Lupi, Massimo Orlando Abstract: Since the complete digitisation of civil processes that took place in Italy in 2008, a lot of data regarding the life cycle of thousands of civil proceedings has been collected. However, despite the continuous monitoring that Italian courts are subjected to, and regulatory changes to the procedures that have been enacted in recent years, the average duration of proceedings is still far from acceptable. In order to identify elements which could point to the causes of such slowness, data provided by the Court of Livorno have been analysed through process mining and graph techniques, in order to assess the coherence and correct application of the process model. A methodology to identify and analyse the outlier processes has been developed, as described in this work, to also detect characteristics which could justify delays in the processes completion. In this work, process mining and statistical techniques have been applied to the analysis of the proceedings data and outliers, with their characteristics, have been recognised.
Keywords: process mining; data model enrichment; outlier analysis; graph-based techniques. DOI: 10.1504/IJBDI.2021.10037170
Special Issue on: SNTA2019 Systems and Network Telemetry and Analytics
Network Traffic Performance Analysis from Passive Measurements using Gradient Boosting Machine Learning by Astha Syal, Alina Lazar, Jinoh Kim, Alex Sim, Kesheng Wu Abstract: Effective monitoring and analysis of network traffic are vital for scientific computing, since scientific applications often require moving massive data from one site to another. A body of statistical and machine learning techniques have been introduced for network traffic monitoring and analysis, but it is a highly challenging task due to several reasons, such as unavailability of label information, complication of real-time analysis, generalization property of machine learning models, and so forth. In this paper, we present a novel method that identifies the continuous time windows of low throughput for the purpose of network performance analysis and anomaly detection, in order to facilitate data transfers for high-performance scientific computing. The presented method is based on supervised learning techniques with an adaptive labeling function that automatically determines if the time window is whether slow or normal. The presented method is validated on real large datasets collected from several data transfer nodes (DTNs) located in Science DMZ. Our experimental results show that the proposed method is able to quickly predict time windows of low performing network transfers, that require attention from network engineers. Keywords: Network traffic; TCP performance; UMAP; classification; Tstat; supervised machine learning; accuracy; cross-validation.
Predicting WAN Traffic Volumes using Fourier and Multivariate SARIMA Approaches by Bashir Mohammed, Mariam Kiran, Nandini Krishnaswamy Abstract: Network traffic has been a vital research issue which has attracted huge attention both in the network operations research domain and the industry. It has become crucial to develop techniques to better understand and predict the behavior and performance of networks. Understanding how network links are used and data movements can help network operations improve link utilization and capacity. In this paper, we tackle the need to understand traffic patterns across a large network topology by developing statistical models that use multivariate data model, incorporating seasonality, peak frequencies, and link relationships to improve future predictions. Using Fourier Transforms to extract seasons and peak frequencies from individual traces, we perform seasonality tests and ARIMA measures to determine optimal parameters to use in our prediction model. Our study shows that network traffic is non-stationary and possess seasonality, making SARIMA prediction approach, the most suitable for network traffic prediction. We also develop a multivariate model on real network traces that are collected from multiple time-zones of a large R&E WAN. Our results indicate an improved prediction accuracy with better RMSE and smaller confidence intervals using multivariate approach rather than univariate approaches. Our work provides key insights into studying network traffic, creating a deeper understanding of prediction methods, necessary for future research in network capacity management and planning. Keywords: Traffic forecasting; Multi-variate Time-series analysis; FourierTransforms; SARIMA.