Characterizing Scales of Genetic Recombination and Antibiotic Resistance in Pathogenic Bacteria Using Topological Data Analysis

Pathogenic bacteria present a large disease burden on human health. Control of these pathogens is hampered by rampant lateral gene transfer, whereby pathogenic strains may acquire genes conferring resistance to common antibiotics. Here we introduce tools from topological data analysis to characterize the frequency and scale of lateral gene transfer in bacteria, focusing on a set of pathogens of significant public health relevance. As a case study, we examine the spread of antibiotic resistance in Staphylococcus aureus. Finally, we consider the possible role of the human microbiome as a reservoir for antibiotic resistance genes.Comment: 12 pages, 6 figures. To appear in AMT 2014 Special Session on Advanced Methods of Interactive Data Mining for Personalized Medicin

Designing attention-aware business intelligence and analytics dashboards

The design of user interface is known to influence the users’ attention while they are interacting with applications such as Business Intelligence and Analytics (BI&A) dashboards. BI&A dashboards are considered as critical because they contain a lot of compressed information and managers only spend a little time to process the provided information. Thereby, they need to manage their visual attention properly due to inattentional blindness and change blindness issues. We propose to investigate the design of BI&A dashboards that are sensitive to the users’ attention. So called attention-aware BI&A dashboards are of utmost importance in the field of BI&A systems since attention is known to play a major role in constructing decisions. We motivate our research project and present the initial design of attention-aware BI&A dashboards. Especially the inclusion of eye-tracking technology is an important aspect of our proposed design

Sensing distress – towards a blended method for detecting and responding to problematic customer experience events

Excellent Customer Experience (CE) is a strategic priority for many large service organisations in a competitive marketplace. CE should be seamless, and in most cases it is, with customers ordering, paying for and receiving services that align with their expectations. However, in rare cases, an exceptional process event leads to service delivery delay or failure, and both the customer and organ-isation end up in complex recovery situations as a result. Unless this recovery is handled effectively inefficiency, avoidable costs and brand damage can result. So how can organisations sense when these problems are occurring and how can they respond to avoid these negative consequences? Our paper proposes a blended methodology where process mining and qualitative user research com-bine to give a holistic picture of customer experience issues, derived from a par-ticular customer case study. We propose a theoretical model for detecting and responding to customer issues, and discuss the challenges and opportunities of such a model when applied in practice in large service organisations

Information Sharing for Customized Dynamic Visual Analytics: A Framework

Supply chain activities generate massive amount of data by several actors such as, suppliers, manufacturers, warehouses, distributers, and wholesalers. Visual analytics (VA) plays a key role in knowledge discovery and insight generation from this data and helps various players to enhance their operational and strategic decision making. This is more essential for Fast moving consumer goods (FMCG) industry, given the size of the industry and its sensitivity to the diverse market uncertainties. In this paper, we present a PhD research plan that responds to the requirements of a FMCG supply chain VA system by means of a comprehensive framework. In this regard, the information flow throughout the supply chain is a significant factor for developing a reliable and efficient VA solution and a proper information flow throughout the supply chain can be enhanced with the help of the framework consisting of modules including Data Generation, Data Integration and Management, Data Analytics, Data Visualization, and Data-driven decision making. The aim of the study is to explore the development of a VA framework that acts as a guideline for supply chain players to improve their analytical capabilities.publishedVersio

Social Analytics in an Enterprise Context: From Manufacturing to Software Development

Although customers become more and more vocal in expressing their experiences, demands and needs in various social networks, companies of any size typically fail to effectively gain insights from such social data and to eventually catch the market realm. This paper introduces the Anlzer analytics engine that aims at leveraging the "social" data deluge to help companies in their quest for deeper understanding of their products' perceptions as well as of the emerging trends in order to early embed them into their product design phase. The proposed approach brings together polarity detection and trend analysis techniques as presented in the architecture and demonstrated through a simple walkthrough in the Anlzer solution. The Anlzer implementation is by design domain-independent and is being tested in the furniture domain at the moment, yet it brings significant added value to software design and development, as well, through its experimentation playground that may provide indirect feedback on future software features while monitoring the reactions to existing releases

Visual analytics of big data from distributed systems

Distributed Systems are challenging to debug because the temporal order of events and distributed states are hard to track. The high complexity of distributed systems make fully automatic reasoning difficult to apply. Domain experts are often required to reason about the behavior of a system based on log files from various sources. This situation presents a good opportunity for visual analytics. Data from multiple sources can be preprocessed and visualized, and then domain experts can conduct exploratory analysis to accelerate the identification of issues. The goal of this master thesis was to create such a visual analytics tool to help domain experts explore data collected from distributed systems more efficiently and assist in identifying bugs and anomalies. The system was used by domain experts and helped to identify issues in a distributed system, showing that visual analytics can be a useful tool to assist domain experts in their daily work.Fehlersuche in verteilten Systemen ist eine Herausforderung, da es schwierig ist, die zeitliche Ordnung von Ereignissen sowie verteilte Zustände im Auge zu behalten. Die hohe Komplexität von verteilten Systemen macht es schwierig, vollautomatisch Schlussfolgerungen zu ziehen. Domänenexperten müssen oft Rückschlüsse über ein komplexes, verteiltes System auf Grundlage von Logdateien aus verschiedenen Quellen ziehen. Diese Situation bietet eine gute Möglichkeit, Visual Analytics anzuwenden. Daten aus diversen Quellen können vorverarbeitet und visualisiert werden, woraufhin Domänenexperten explorative Analyse zur Beschleunigung der Fehlersuche betreiben können. Das Ziel dieser Masterarbeit war es, solch ein Visual Analytics-Werkzeug zu erschaffen, um Domänenexperten das Erkunden von Daten von verteilten Systemen zu erleichtern und bei der Identifizierung von Fehlern und Anomalien zu helfen. Das System wurde von Domänenexperten verwendet und half bei der Identifizierung von Problemen in einem verteilten System, was zeigt, dass Visual Analytics ein nützliches Werkzeug ist, um Domänenexperten bei ihrer täglichen Arbeit zu unterstützen