Visualisation of Large-Scale Call-Centre Data

The contact centre industry employs 4% of the entire United King-dom and United States’ working population and generates gigabytes of operational data that require analysis, to provide insight and to improve efficiency. This thesis is the result of a collaboration with QPC Limited who provide data collection and analysis products for call centres. They provided a large data-set featuring almost 5 million calls to be analysed. This thesis utilises novel visualisation techniques to create tools for the exploration of the large, complex call centre data-set and to facilitate unique observations into the data.A survey of information visualisation books is presented, provid-ing a thorough background of the field. Following this, a feature-rich application that visualises large call centre data sets using scatterplots that support millions of points is presented. The application utilises both the CPU and GPU acceleration for processing and filtering and is exhibited with millions of call events.This is expanded upon with the use of glyphs to depict agent behaviour in a call centre. A technique is developed to cluster over-lapping glyphs into a single parent glyph dependant on zoom level and a customizable distance metric. This hierarchical glyph repre-sents the mean value of all child agent glyphs, removing overlap and reducing visual clutter. A novel technique for visualising individually tailored glyphs using a Graphics Processing Unit is also presented, and demonstrated rendering over 100,000 glyphs at interactive frame rates. An open-source code example is provided for reproducibility.Finally, a novel interaction and layout method is introduced for improving the scalability of chord diagrams to visualise call transfers. An exploration of sketch-based methods for showing multiple links and direction is made, and a sketch-based brushing technique for filtering is proposed. Feedback from domain experts in the call centre industry is reported for all applications developed

Vysokodimenzionální jednobuněčná cytometrie pro analýzu imunitního systému

Technologický pokrok umožnil nástup nových technologií jednobuněčné analýzy, které jsou schopné současně měřit velký počet buněčných funkcí a vlastností. Tyto technologie byly následně použity k objasnění heterogenity buněčných systémů dříve považovaných za homogenní a pro identifikaci charakteristických vlastností jednotlivých buněk v jejich nikách. Technologie pro studium jednotlivých buněk dnes představují důležitý nástroj pro výzkum imunologických mechanismů, které jsou podstatou různých onemocnění. V této souvislosti je cytometrie jednou z mnoha vysoce výkonných metod schopných prozkoumat více než 50 znaků na jedné buňce. Využití plného potenciálu cytometrie však vyžaduje vývoj optimalizovaného panelu. Výsledná mnohadimenzionální data navíc představují výzvu pro existující výpočetní techniku. Tato práce se pokouší tyto výzvy řešit. První část práce je zaměřena na vývoj nelineárního algoritmu založeného na vkládání vysoko dimenzionálních uzlů do 2D prostoru pro rychlou analýzu cytometrických dat s názvem EmbedSOM. V porovnání s jinými nejmodernějšími algoritmy vykazuje EmbedSOM vyšší rychlost zpracování. To je zásadní pro analýzu velkých datových souborů s miliony buněk. Kromě toho má EmbedSOM další funkce, jako je možnost provádět vkládání uzlů na základě významných bodů, tzv. "landmarks",...Technological advancement allowed for the advent of single-cell technologies capable of measuring a large number of cellular features simultaneously. These technologies have been subsequently used to shed light on the heterogeneity of cellular systems previously considered homogeneous, identifying the exclusive features of individual cells within cellular niches. Today, single-cell technologies represent an essential tool for studying the underlying immunological mechanisms correlating with disease. In this context, cytometry is one of the diverse high-throughput methods capable of examining more than 50 features per cell. However, utilising cytometry at its full potential requires the development of optimized assays. Additionally, the resulting high-dimensional data represent a challenge for existing computational techniques. This thesis attempts to address these challenges. The first part of the thesis is focused on developing a non-linear embedding algorithm for rapid analysis of cytometry datasets called EmbedSOM. The comparison of EmbedSOM with other state-of-the-art algorithms suggested the superiority of EmbedSOM with faster runtime. This is critical for the analysis of large datasets with millions of cells. Furthermore, EmbedSOM has additional functionality such as landmark guided...Department of Genetics and MicrobiologyKatedra genetiky a mikrobiologieFaculty of SciencePřírodovědecká fakult

Multimodal Data Analytics and Fusion for Data Science

Advances in technologies have rapidly accumulated a zettabyte of “new” data every two years. The huge amount of data have a powerful impact on various areas in science and engineering and generates enormous research opportunities, which calls for the design and development of advanced approaches in data analytics. Given such demands, data science has become an emerging hot topic in both industry and academia, ranging from basic business solutions, technological innovations, and multidisciplinary research to political decisions, urban planning, and policymaking. Within the scope of this dissertation, a multimodal data analytics and fusion framework is proposed for data-driven knowledge discovery and cross-modality semantic concept detection. The proposed framework can explore useful knowledge hidden in different formats of data and incorporate representation learning from data in multimodalities, especial for disaster information management. First, a Feature Affinity-based Multiple Correspondence Analysis (FA-MCA) method is presented to analyze the correlations between low-level features from different features, and an MCA-based Neural Network (MCA-NN) ispro- posedto capture the high-level features from individual FA-MCA models and seamlessly integrate the semantic data representations for video concept detection. Next, a genetic algorithm-based approach is presented for deep neural network selection. Furthermore, the improved genetic algorithm is integrated with deep neural networks to generate populations for producing optimal deep representation learning models. Then, the multimodal deep representation learning framework is proposed to incorporate the semantic representations from data in multiple modalities efficiently. At last, fusion strategies are applied to accommodate multiple modalities. In this framework, cross-modal mapping strategies are also proposed to organize the features in a better structure to improve the overall performance