Conjoint data mining of structured and semi-structured data

With the knowledge management requirement growing, enterprises are becoming increasingly aware of the significance of interlinking business information across structured and semi-structured data sources. This problem has become more important with the growing amount of semi-structured data often found in XML repositories, web logs, biological databases, etc. Effectively creating links between semi-structured and structured data is a challenging and unresolved problem. Once an optimized method has been formulated, the process of data mining can be implemented in a conjoint manner. This paper investigates a way in which this challenging problem can be tackled. The proposed method is experimentally evaluated using a real world database and the effectiveness and the potential in discovering collective information is demonstrated

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user\u27s comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development

Structured and unstructured data integration with electronic medical records

In recent years there has been a great population and technological evolution all over the world. At the same time, more areas beyond technology and information technology have also developed, namely medicine, which has led to an increase in average life expectancy which in turn, leads to a greater need for healthcare. In order to provide the best possible treatments and healthcare services, nowadays the hospitals store large amounts of data regarding patients and diseases (in the form of electronic medical records) or the logistics of some departments in their storage systems. Therefore, computer science techniques such as data mining and natural language processing have been used to extract knowledge and value from these information-rich sources in order not only to develop, for example, new models for disease prediction, as well as improving existing processes in healthcare centres and hospitals. This data storage can be done in one of three ways: structured, unstructured or semi-structured. In this paper, the author tested the integration of structured and unstructured data from two different departments of the same Portuguese hospital, in order to extract knowledge and improve hospital processes. Aiming to reduce the value loss of loading data that is not used in the healthcare providers systems.Nos últimos anos tem-se assistido a uma grande evolução populacional e tecnológica por todo o mundo. Paralelamente, mais áreas para além da tecnologia e informática têm-se também desenvolvido, nomeadamente a área da medicina, o que tem permitido um aumento na esperança média de vida que por sua vez leva a uma maior necessidade de cuidados de saúde. Com o intuito de fornecer os melhores serviços de saúde possíveis, nos dias que hoje os hospitais guardam nos seus sistemas informáticos grandes quantidades de dados relativamente aos pacientes e doenças (sobre a forma de registos médicos eletrónicos) ou relativos à logística de alguns departamentos dos hospitais, etc. Por conseguinte, a estes dados têm vindo a ser utilizadas técnicas da área das ciências da computação como o data mining e o processamento da língua natural para extrair conhecimento e valor dessas fontes ricas em informação com o intuito não só de desenvolver, por exemplo, novos modelos de predição de doenças, como também de melhorar processos já existentes em centros de saúde e hospitais. Este armazenamento de dados pode ser feito em uma de três formas: de forma estruturada, não estruturada ou semi-estruturada. Neste trabalho o autor testou a integração de dados estruturados e não estruturados de dois departamentos diferentes do mesmo hospital português, com o intuito de extrair conhecimento e melhorar os processos do hospital. Com o intuito de reduzir a perda do armazenamento de dados que não são utilizados

Development of a self-report measure of capability wellbeing for adults: the ICECAP-A

Purpose The benefits of health and social care are not confined to patient health alone and therefore broader measures of wellbeing may be useful for economic evaluation.\ud This paper reports the development of a simple measure of capability wellbeing for adults (ICECAP-A).\ud Methods In-depth, informant-led, interviews to identify the attributes of capability wellbeing were conducted with 36 adults in the UK. Eighteen semi-structured, repeat interviews were carried out to develop a capability-based descriptive system for the measure. Informants were purposively selected to ensure variation in socio-economic status, age, sex, ethnicity and health. Data analysis was carried out inductively and iteratively alongside interviews, and findings were used to shape the questions in later interviews.\ud Results Five over-arching attributes of capability wellbeing were identified for the measure: ‘‘stability’’,‘‘attachment’’, ‘‘achievement’’, ‘‘autonomy’’ and ‘‘enjoyment’’. One item, with four response categories, was developed for each attribute for the ICECAP-A descriptive system.\ud Conclusions The ICECAP-A capability measure represents a departure from traditional health economics outcome measures, by treating health status as an influence over broader attributes of capability wellbeing. Further work is required to value and validate the attributes and test the sensitivity of the ICECAP-A to healthcare interventions

A methodological approach to consumer research on second-hand fashion platforms in Italy: online clothing reselling platforms: perceptions and preferences of Italian consumers

The research focuses on second-hand fashion platforms (Vinted, Vestiaire Collective, Depop, Zalando Second-hand) in Italy from a consumer standpoint. The study assesses the platform's positioning, and most preferred characteristics, as well as the potential consumer segments in the Italian market. By conducting surveys with consumers, and applying market research techniques such as perceptual maps, conjoint analysis, and k-means clustering, we were able to learn consumers' perceptions, preferences, and their relevance to the platforms. The main discoveries are then used to suggest recommendations for the companies to improve their market presence and competitive edge

Mining of patient data: towards better treatment strategies for depression

An intelligent system based on data-mining technologies that can be used to assist in the prevention and treatment of depression is described. The system integrates three different kinds of patient data as well as the data describing mental health of therapists and their interaction with the patients. The system allows for the different data to be analysed in a conjoint manner using both traditional data-mining techniques and tree-mining techniques. Interesting patterns can emerge in this way to explain various processes and dynamics involved in the onset, treatment and management of depression, and help practitioners develop better prevention and treatment strategies

Structured and unstructured data integration with electronic medical records

Medicine is a field with high volatility of changes. Everyday new discoveries and procedures are tested with the sole goal of providing a better-quality life to patients. With the evolution of computer science, multiple fields saw an increase of productivity and solutions that could be implemented. More specifically, in medicine new techniques started being tested in order to understand how the systems and practices used can reach higher performances, while maintaining the predefined high standards of quality. For many years data generated in hospital was collected and stored yet few tools were implemented to extract knowledge or any type of advantage. One of the areas that successfully implemented in medicine was the usage of data processing tools and techniques to further extract information regarding the high abundance of data generated in a daily basis, in this field of work. This data can be stored in different ways which leads to multiple approaches on how to deals with it. The sole purpose of this paper is to give an overview of some case studies where structured and unstructured data was used, joint and separately and the value of it.info:eu-repo/semantics/publishedVersio

Boosting the accuracy of hedonic pricing models

Hedonic pricing models attempt to model a relationship between object attributes andthe object's price. Traditional hedonic pricing models are often parametric models that sufferfrom misspecification. In this paper we create these models by means of boosted CARTmodels. The method is explained in detail and applied to various datasets. Empirically,we find substantial reduction of errors on out-of-sample data for two out of three datasetscompared with a stepwise linear regression model. We interpret the boosted models by partialdependence plots and relative importance plots. This reveals some interesting nonlinearitiesand differences in attribute importance across the model types.pricing;marketing;data mining;conjoint analysis;ensemble learning;gradient boosting;hedonic pricing