Abstracting qualitative information for process supervision

Supervisory systems evolution makes the obtaining of significant information from processes more important in the way that the supervision systems' particular tasks are simplified. So, having signal treatment tools capable of obtaining elaborate information from the process data is important. In this paper, a tool that obtains qualitative data about the trends and oscillation of signals is presented. An application of this tool is presented as well. In this case, the tool, implemented in a computer-aided control systems design (CACSD) environment, is used in order to give to an expert system for fault detection in a laboratory plant

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

L'hypnose: une ressource en soins palliatifs? Etude qualitative sur l'apport de l'hypnose chez des patients oncologique.s [Hypnosis as a resource in palliative care. A qualitative study of the contribution of hypnosis to the care of oncology patients].

Hypnosis is recognised in medicine as an effective complementary therapy. However, few qualitative data are available concerning the benefits it may bring. This qualitative exploratory study aimed to examine the contribution of hypnosis to the care of advanced cancer patients. Results demonstrate that hypnosis is an effective and efficient means of developing the resources of people suffering from serious illness. After an average of four hypnotherapy sessions, patients said they were able to locate previously unexploited resources within themselves and were able to become autonomous in the use of self-hypnosis. The major benefit reported concerned a reduction in anxiety. For patients experiencing anxiety about death, hypnosis allowed them, within a therapeutic environment perceived as safe, to explore different facets of their fears and to develop adaptive strategies. Aside from slight fatigue experienced during the sessions, no adverse side-effects were reported. In conclusion, this study exploring the effects of hypnosis allowed us to identify important benefits for patients suffering from advanced cancer. Consequently, replication on a larger scale is recommended in order to ascertain the extent to which it is possible to generalise from these results and in order better to define the characteristics of patients most likely to benefit from this therapy.

Inequality measurement for ordered response health data

Because self-reported health status [SRHS] is an ordered response variable, inequality measurement for SRHS data requires a numerical scale for converting individual responses into a summary statistic. The choice of scale is however problematic, since small variations in the numerical scale may reverse the ordering of a given pair of distributions of SRHS data in relation to conventional inequality indices such as the variance. This paper introduces a parametric family of inequality indices, founded on an inequality ordering proposed by Allison and Foster [Allison, R.A., Foster, J., 2004. Measuring health inequalities using qualitative data. Journal of Health Economics 23, 505-524], which satisfy a suitable invariance property with respect to the choice of numerical scale. Several key members of the parametric family are also derived, and an empirical application using data from the Swiss Health Survey illustrates the proposed methodology. [Authors]

Startup IPR strategies: Why should startup companies protect their intellectual property?

This thesis studies intellectual property right (also: IPR) strategies from the perspective of high growth startup companies. Due to technology development and intellectualization of business, large part of companies’ assets are nowadays intangible. At the same time, the importance of protection instruments designed to protect these intangible assets, intellectual property rights, is increasing. Utilization of these instruments, however, requires understanding of the functioning of the IPR system, as well as financial resources. Startup companies aiming for growth need to be able compete with more established companies also in relation to intangible assets, but they might not have the required knowledge ot resources to fully utilize IPRs in their business. This research aims to understand what are the benefits a startup company can have from protecting their IPRs, and how can the company achieve those benefits. Based on a review of previous literature, altogether 11 benefits of IPR registration were recognized. To answer to the research questions, six half-structured interviews were conducted with experts form different fields, all with experience in working with startup companies and IPR issues. The interviews were analyzed using different methods of qualitative data analysis, mainly derived from grounded theory and case study methods. As a result, out of the 11 benefits recognized from earlier literature, 8 were recognized to be relevant for startup companies. The most central benefits were recognized to be linked with the financial lifecycle of the startup company, including increasing credibility of the startup and stimulating an investment. In addition it was noticed, that startup companies are mainly able to utilize these benefits at later stages of their lifecycle. However, to be able to utilize the benefits at later stages, the startup company needs to be aware of the functioning of the IPR system and might need to apply for appropriate protection already early on. As a result of this study, a three-step model was formed to describe different levels of IPR utilization. The first level of the model represents the minimum level of understanding that every startup company should have regarding IPRs. The second level views IPR strategy from a risk management perspective, including securing the minimum protection of the company’s own IPRs, contract management and establishing processes for handling IPR issues. The last stage reflects strategic use of IPRs. At this third stage intellectual property rights have a central role in the startup company’s business, and they are used in the company’s value creation.

From KAPTUR to VADS4R: Exploring Research Data Management in the Visual Arts

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Project Resource Management in R&D Public Sector Organization

In R&D organizations multiple projects are executed concurrently. Problems arises in managing shared resources since they are needed by multiple projects simultaneously. The objective of this thesis was to study how the project and resource management could be developed in a public sector R&D organization. The qualitative research was carried out in the Magnetic Measurements section at CERN where the section measures magnets for particle accelerators and builds state of the art measurement devices for various needs. Hence, the R&D and measurement projects are very time consuming and very complex. Based on the previous research and the requirements from the organization the best alter- native for resource management was to build a project management information system. A centralized database was constructed and on top of it was built an application for interacting and visualizing the project data. The application allows handling project data, which works as a basis for resource planning before and during the projects are executed. It is one way to standardize the work-flow of projects, which strengthens the project process. Additionally, it was noted that the inner customer’s database, the measurement system and the new application needed to be integrated. Further integration ensures that the project data is received efficiently from customers and available not only within the application but also during the concrete work. The research results introduced a new integrated application, which centralizes the project information flow with better visibility.

A qualitative study of curriculum and instructional efforts in relation to high stakes federal and district policies in one low-performing elementary school

Les enjeux liés aux politiques éducatives ont considérablement changé au cours des dernières décennies. Ces changements sont liés, entre autres, à l’accroissement de l’imputabilité et de la reddition de compte qui est devenue une caractéristique importante des réformes curriculaires et pédagogiques. Les politiques à enjeux élevés exercent une pression énorme sur les districts et les écoles états-unienne afin qu’ils augmentent le rendement des élèves en utilisant des systèmes de conséquences (Hall & Ryan, 2011; Loeb & Strunk, 2007). Ces politiques envoient de puissants messages sur l'importance de certaines matières scolaires au détriment d'autres - circonscrivant les exigences en termes de compétences et de connaissances. La langue maternelle d’enseignement et les mathématiques sont devenues des mesures centrales sur lesquelles reposent l’évaluation et le degré de performance des districts et des écoles. Conséquemment, les administrateurs de districts et les directions d’écoles ont souvent recours à des réformes curriculaires et pédagogiques comme moyen d'augmenter le rendement des élèves dans les matières scolaires visées par ces politiques. Les politiques contraignent les acteurs scolaires de concentrer les ressources sur les programmes curriculaires et les évaluations, le développement professionnel, et la prise de décision pilotée par les données (Anagnostopoulos & Ruthledge, 2007; Honig & Hatch, 2004; Spillane, Diamond, et al., 2002; Weitz White & Rosenbaum, 2008). Cette thèse examine la manière dont les politiques à enjeux élevés opèrent quotidiennement dans les interactions et les pratiques au sein des écoles. Nous analysons plus particulièrement les différents messages provenant de la politique transmis aux acteurs scolaires sur les manières d'apporter des changements substantiels dans le curriculum et l'enseignement. Nous élargissons l’analyse en prenant en compte le rôle des administrateurs de district ainsi que des partenaires universitaires qui façonnent également la manière dont certains aspects des messages provenant des politiques sont transmis, négociés et/ou débattus et d’autres sont ignorés (Coburn & Woulfin, 2012). En utilisant l’analyse de discours, nous examinons le rôle du langage comme constituant et médiateur des interactions sociales entre les acteurs scolaires et d’autres parties prenantes. De telles analyses impliquent une investigation approfondie d’un nombre d’étude de cas limité. Les données utilisées dans cette thèse ont été colligées dans une école primaire états-unienne du mid-West. Cette étude de cas fait partie d’une étude longitudinale de quatre ans qui comprenait huit écoles dans les milieux urbains entre 1999 et 2003 (Distributed Leadership Studies, http://www.distributedleadership.org). La base de données analysée inclut des observations de réunions formelles et des entrevues auprès des administrateurs du district, des partenaires universitaires, de la direction d’école et des enseignants. En plus de l’introduction et de la problématique (chapitre 1) et de discussion et conclusion (chapitre 5), cette thèse comprend un ensemble de trois articles interdépendants. Dans le premier article (chapitre 2), nous effectuons une recension des écrits portant sur le domaine de l’implantation de politiques (policy implementation) et la complexité des relations locales, nationales et internationales dans les systèmes éducatifs. Pour démystifier cette complexité, nous portons une attention particulière à la construction de sens des acteurs scolaires comme étant une dimension clé du processus de mise en œuvre des réformes. Dans le deuxième article (chapitre 3), nous cherchons à comprendre les processus sociaux qui façonnent les réponses stratégiques des acteurs scolaires à l’égard des politiques du district et de l’état et en lien avec la mise en œuvre d’un curriculum prescrit en mathématiques. Plus particulièrement, nous explorons les différentes situations dans lesquelles les acteurs scolaires argumentent au sujet des changements curriculaires et pédagogiques proposés par les administrateurs de district et des partenaires universitaires afin d’augmenter les résultats scolaires en mathématiques dans une école à faible performance. Dans le troisième article (chapitre 4), nous cherchons à démystifier les complexités liées à l’amélioration de l’enseignement dans un environnement de politiques à enjeux élevés. Pour ce faire, nous utilisons l'interaction entre les notions d'agentivité et la structure afin d'analyser la manière dont les conceptions d’imputabilité et les idées qui découlent de l'environnement politique et les activités quotidiennes jouent dans les interactions entre les acteurs scolaires concernant sur l’enseignement de la langue maternelle. Nous explorons trois objectifs spécifiques : 1) la manière dont les politiques à enjeux élevés façonnent les éléments de l’enseignement qui sont reproduits et ceux qui sont transformés au fil du temps ; 2) la manière dont la compréhension des leaders de l’imputabilité façonne les aspects des messages politiques que les acteurs scolaires remarquent à travers les interactions et les conversations et 3) la manière les acteurs scolaires portent une attention particulière à certaines messages au détriment d’autres. Dans le dernier chapitre de cette thèse, nous discutons les forces et les limites de l’analyse secondaire de données qualitatives, les implications des résultats pour le domaine d’études de l’implantation de politiques et les pistes futures de recherches.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Abstracting qualitative information for process supervision

Supervisory systems evolution makes the obtaining of significant information from processes more important in the way that the supervision systems' particular tasks are simplified. So, having signal treatment tools capable of obtaining elaborate information from the process data is important. In this paper, a tool that obtains qualitative data about the trends and oscillation of signals is presented. An application of this tool is presented as well. In this case, the tool, implemented in a computer-aided control systems design (CACSD) environment, is used in order to give to an expert system for fault detection in a laboratory plant

COMP6043 Analysis of Textual Data

Class exercise to analyse qualitative data mediated on use of a set of transcripts, augmented by videos from web site. Discussion is around not only how the data is codes, interview bias, dimensions of analysis. Designed as an introduction.

WAIS seminar: Alan Walks Wales: Data and Challenges

Abstract This seminar is a research discussion around a very interesting problem, which may be a good basis for a WAISfest theme. A little over a year ago Professor Alan Dix came to tell us of his plans for a magnificent adventure:to walk all of the way round Wales - 1000 miles 'Alan Walks Wales'. The walk was a personal journey, but also a technological and community one, exploring the needs of the walker and the people along the way. Whilst walking he recorded his thoughts in an audio diary, took lots of photos, wrote a blog and collected data from the tech instruments he was wearing. As a result Alan has extensive quantitative data (bio-sensing and location) and qualitative data (text, images and some audio). There are challenges in analysing individual kinds of data, including merging similar data streams, entity identification, time-series and textual data mining, dealing with provenance, ontologies for paths, and journeys. There are also challenges for author and third-party annotation, linking the data-sets and visualising the merged narrative or facets of it.

Qualitative research and consumer psychology: alternatives for application

Qualitative research is a research strategy used to analyze the reality. When applied to consumer psychology, it allows a deeper knowledge about consumer’s behavior and associated emotions and motivations. Qualitative research goes beyond the description of buyers’ behavior and shows information about how and why that behavior is produced.The purpose of this paper is to demonstrate how qualitative research is relevant for the knowledge and the understanding of consumers’ behavior and how, through its techniques, it approaches the consumer’s socio-cultural reality and provides an interpretation of it. The present paper resumes the key aspects of qualitative research, mentioning its related antecedents of its contributions to the marketing and explaining the four most applied techniques in consumer psychology (interviews, focus group, ethnography and observation); moreover, it also studies the way to carry them out and gives some examples of some of the market issues which it can analyze. Finally, we take up again the qualitative data analysis as one of the most relevant topics because it produces important information for the decision making process related to the consumer. In addition, we explain the steps, strategies, types and technological tools to carry it out. 

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.