Thematic analysis without paradox: sensemaking and context

Purpose In this paper, the authors underpin thematic analysis with a philosophical and methodological dimension and present a nuanced perspective on the application of thematic analysis in a data-driven context. Thematic analysis is a widely used qualitative analytic method; it is perceived as a transparent approach that offers single meaning. However, through Husserlian descriptive phenomenology, this paper aims to examine issues regarding subject/object and multidimensional meanings and realities. Design/methodology/approach In most extant studies, thematic analysis has become a prescriptive approach. This emerging qualitative approach has been applied to a range of studies on social and organisational issues, knowledge management and education. However, despite its wide usage, researchers are divided as to its effectiveness. Many choose quantitative approaches as an alternative, and some disagree as to what counts as the definitive framework and process for thematic analysis. Consequently, the authors provide a level of validity for thematic analysis through emphasising a specific methodological approach based on ontological and epistemological positions. Findings Contrary to the common mantra from contemporary qualitative researchers who claim thematic analysis is often based on a static and enduring approach, the current paper highlights the dynamic nature of a thematic analytic approach and offers a deeper understanding of the ways in which researchers can use the right approach to understand the emerging complex data context. Originality/value Several insights regarding the literature on thematic analysis were identified, including the current conceptualisation of thematic analysis as a dynamic approach. Understanding thematic analysis through phenomenology provides a basis on which to undertake a whole range of inclusive approaches that were previously undifferentiated from a quantitative perspective.

ICHC Framework

The Goal in this paper is to propose a cultural heritage data model and evolve towards the creation of a framework based on MongoDB that will allow to manage a JSON model representing the cultural heritage of a city ICHC (Intelligent Cultural Heritage of a City). This manuscript per the authors noticed that during the census of cultural heritage, the presence of human resources linked to heritage is not something that is represented in a smart engine of a framework. Which is why the goal is to integrate the human resource and therefore add a relational aspect to the NoSql documents so that the resulting framework can have a smart engine to link data.This model is a set of ICHD (Intelligent Cultural Heritage Document) which are JSON documents that represent of the different types of cultural heritage entities. Those documents will be managed in a MongoDB repository architecture that will allow to them, so that the microservices-based ICHC framework can offer a big data context that can handle a huge variety, volume and velocity of data and be based on distributed operations.

Survey on Preprocessing Techniques for Big Data Projects

In the era of big data, a vast amount of data are being produced. This results in two main issues when trying to discover knowledge from these data. There is a lot of information that is not relevant to the problem we want to solve, and there are many imperfections and errors in the data. Therefore, preprocessing these data is a key step before applying any kind of learning algorithm. Reducing the number of features to a relevant subset (feature selection) and reducing the possible values of continuous variables (discretisation) are two of the main preprocessing techniques. This paper will review different methods for completing these two steps, focusing on the big data context and giving examples of projects where they have been applied.

The Exploitation of Distance Distributions for Clustering

Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior knowledge is used. In cluster analysis, current studies evaluate the choice of distance measure after applying unsupervised methods based on error probabilities, implicitly setting the goal of reproducing predefined partitions in data. Such studies use clusters of data that are often based on the context of the data as well as the custom goal of the specific study. Depending on the data context, different properties for distance distributions are judged to be relevant for appropriate distance selection. However, if cluster analysis is based on the task of finding similar partitions of data, then the intrapartition distances should be smaller than the interpartition distances. By systematically investigating this specification using distribution analysis through the mirrored-density (MD plot), it is shown that multimodal distance distributions are preferable in cluster analysis. As a consequence, it is advantageous to model distance distributions with Gaussian mixtures prior to the evaluation phase of unsupervised methods. Experiments are performed on several artificial datasets and natural datasets for the task of clustering.