Network problems detection and classification by analyzing syslog data

Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the network problems. It is found that in many studies conducted previously, analyzing syslog data which can be a guideline for network problems and their causes was used. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. Classifying syslog data needs to identify the syslog messages that describe the network problems for each layer, taking into account the different formats of various syslog for vendors’ devices. This study provides a method to classify syslog messages that indicates the network problem in terms of network layers. The method used data mining tool to classify the syslog messages while the description part of the syslog message was used for classification process. Related syslog messages were identified; features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set which was obtained from the Universiti Utara Malaysia’s (UUM) network devices is used for the prediction stage. Results indicate that SVM shows the best performance during the training and prediction stages. This study contributes to the field of network troubleshooting, and the field of text data classification

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data

Deploying a Model for Assessing Cognitive Automation Use Cases: Insights from Action Research with a Leading European Manufacturing Company

Cognitive automation moves beyond rule-based automation and thus imposes novel challenges on organizations when assessing the automation potential of use cases. Thus, we present an empirically grounded and conceptually operationalized model for assessing cognitive automation use cases, which consists of four assessment dimensions: data, cognition, relationship, and transparency requirements. We apply the model in a real-world organizational context in the course of an action research project at the customer service department of ManuFact AG, and present unique empirical insights as well as the impact the application of the model had on the organization. The model shall help practitioners to make more informed decisions on selecting use cases for cognitive automation and to plan respective endeavors. For research, the identified factors affecting the suitability of a use case for cognitive automation shall deepen our understanding of cognitive automation in particular, and AI as the driving force behind cognitive automation in general

Radical agenda - settings? exploring informality and the spatial and economic practices of informal people within the ambit of suggestion, contestation and movement towards an alternative city

This research report examines the extent to which the economic and spatial practices of informal people can be classed as radical genda-setting towards an alternative city. In so doing the practices and perceptions of business owners, market traders and street traders in Yeoville are explored. To give greater context of what informal people are possibly pushing up against, state practice and policy are also considered. The discussion further draws on the nexus between politics and governance as well as between the state and capital on the making of contemporary cities. Social movement theory provides the initial basis to carry out the discussion. The interweaving theories of quiet encroachment (Bayat), insurgent citizenship (Holston) and subaltern urbanism (Roy) give the exploration greater depth

Investigating channels of cash circulation adopted by unbanked (African) migrants in Pretoria Central Business District (CBD)

Research Report submitted in partial fulfilment of the requirements of a Master of Arts Degree in Development studies by Coursework and Research Report. Faculty of Humanities, University of Witwatersrand 2016This study explored cash circulation channels adopted by unbanked migrants in Pretoria Central Business District (CBD), South Africa. To understand the complex nature of cash circulation and the subjective practices of migrants, in-depth interviews were conducted with sixteen migrants selected through snowballing sampling. Collected primary data were analysed thematically, from particular to general themes depending on the responses provided by the informants. The study adopted the Sustainable Livelihoods Framework (SLF) as an analytical tool to show how in the face of structural and institutional barriers, unbanked migrants have the capability to adopt digital solutions and socially embedded channels which are more flexible and sustainable in their livelihoods. These include informal channels such as hawala, malaichas and digital solutions like Kawena and Mukuru. By using this framework, the report reveals what unbanked migrants are doing on the ground, what shapes adopted cash circulation processes and the resultant livelihood outcomes. The study aimed at contributing to previous research on money transfer mechanisms adopted by unbanked African migrants. The conclusion reached is that, by adopting various socially embedded cash circulation channels, unbanked migrants circumvent structural constraints and, by so doing, financially include more people who were previously excluded. Although the study was limited to a small sample, it raises strong implications for policy makers to look at the inherent strength of migrants as development actors. Findings from this exploratory study are critical in that they open new niches for research on migrants and financial exclusion in Africa and beyond.GR201