Blockchain-based Continuous Timestamps Tracking System: Towards Ownership Information Believability

Ownership information of high value assets such as property is often concealed and fragmented, adversely affecting information believability. Following the design science research approach, we conceptualize believability as a data quality dimension that supports ownership traceability. We then investigate how blockchain technology might improve information believability in ownership traceability systems. We represent and address our findings via the development of a blockchain-based continuous timestamps tracking system model, framework and implementation for property ownership. A use case of banking transactional data for property ownership traceability is introduced to illustrate our workflow and system design. The proposed system takes advantage of blockchain technology such as traceability and irreversibility to support information believability in the design, management, and use of information systems

Are Importance Ratings Stable? A Study of Perceptions of Information Quality

Information consumption in China occurs in a rapidly shifting social and political environment. Understanding this group of information consumers is likely to play an important role in business and political decision making globally for the foreseeable future. Ratings of the importance of the dimensions of information quality and the way in which these ratings have shifted over time shed light on the beliefs of this group of information consumers. This study reports the results of a nonpanel longitudinal study involving two surveys conducted in China over a five year period examining information consumer ratings of the importance of the dimensions of information quality. Results show that Chinese information consumers rate the information quality dimensions of believability, reputation, and value-added as less important at the end of the five year period than at the beginning and rate representational consistency and concise representation as more important at the end of the five year period than at the beginning

Análisis de métodos y técnicas de limpieza de datos existentes y aplicación en un sistema CRM para una institución educativa limeña

En la actualidad, las organizaciones emplean varios sistemas y varias fuentes de información para las actividades del día a día, y buscan tener toda esta información reunida e integrada en una única base de datos llamada data warehouse ya que permite fortalecer el trabajo del día a día, el análisis de datos y la toma de decisiones. Sin embargo, la información guardada debe de ser de buena calidad ya que una baja calidad de datos puede impactar severamente en el desempeño de la organización, la satisfacción del cliente, la toma de decisiones y reducir la habilidad de la organización de ejecutar correctamente sus planes estratégicos. En este contexto, aparece un problema crítico: la baja calidad de la información en los sistemas; y lo preocupante es que algunas empresas ignoran los impactos y consecuencias mencionados. Un sistema de información muy adquirido y usado por organizaciones Business-to- Consumer (B2C por su abreviatura en inglés) es el sistema de Gestión de Relación con el Cliente (Customer Relationship Management - CRM). Un sistema CRM es un sistema enfocado en la gestión de clientes. Los registros más importantes pertenecen a la entidad “clientes” y esta información es obtenida por las organizaciones a través de varios canales o mediante la compra de bases de datos de terceros. Finalmente, toda la información es almacenada en el data warehouse para ser consumida de allí para la toma de decisiones. Los problemas específicos para un sistema CRM son: registros duplicados de clientes, datos faltantes de un cliente como su teléfono o dirección, datos incorrectos, datos obsoletos que en algún momento fueron correctos y atributos con valores diferentes para un mismo cliente. Mantener estos registros limpios debe ser una actividad vital para la organización. Las instituciones educativas no son ajenas a esta herramienta de soporte CRM, y con el transcurso de los años, están apostando por adoptar sistemas CRM en las organizaciones (KaptureCRM, 2017). En este contexto, tener los datos de los estudiantes limpios es una tarea primordial para la organización. El desarrollo de este proyecto se enfoca en un análisis de los algoritmos, técnicas y métodos usados para la limpieza de datos, la implementación de procesos ETL (extracción, transformación y carga) que permitan la limpieza de cada fuente de datos, la integración de la información a una base de datos transaccional, la carga de la información de la base de datos transaccional a un data warehouse para su próxima explotación y, adicionalmente, el modelamiento de nuevos procesos de negocio para prevenir y mantener la correcta calidad de los datos en el sistema transaccional, para la institución educativa sobre la cual se realiza el proyecto.Tesi

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Contributions to Engineering Big Data Transformation, Visualisation and Analytics. Adapted Knowledge Discovery Techniques for Multiple Inconsistent Heterogeneous Data in the Domain of Engine Testing

In the automotive sector, engine testing generates vast data volumes that are mainly beneficial to requesting engineers. However, these tests are often not revisited for further analysis due to inconsistent data quality and a lack of structured assessment methods. Moreover, the absence of a tailored knowledge discovery process hinders effective preprocessing, transformation, analytics, and visualization of data, restricting the potential for historical data insights. Another challenge arises from the heterogeneous nature of test structures, resulting in varying measurements, data types, and contextual requirements across different engine test datasets. This thesis aims to overcome these obstacles by introducing a specialized knowledge discovery approach for the distinctive Multiple Inconsistent Heterogeneous Data (MIHData) format characteristic of engine testing. The proposed methods include adapting data quality assessment and reporting, classifying engine types through compositional features, employing modified dendrogram similarity measures for classification, performing customized feature extraction, transformation, and structuring, generating and manipulating synthetic images to enhance data visualization, and applying adapted list-based indexing for multivariate engine test summary data searches. The thesis demonstrates how these techniques enable exploratory analysis, visualization, and classification, presenting a practical framework to extract meaningful insights from historical data within the engineering domain. The ultimate objective is to facilitate the reuse of past data resources, contributing to informed decision-making processes and enhancing comprehension within the automotive industry. Through its focus on data quality, heterogeneity, and knowledge discovery, this research establishes a foundation for optimized utilization of historical Engine Test Data (ETD) for improved insights.Soroptimist International Bradfor

The Role of Diversity Management Policies and Practices in Advancing Inclusive Culture

AbstractThe Roles of Diversity Management Policies and Practices in Advancing Inclusive Culture by Christopher Emeka Babundo MBA, Azusa Pacific University, 2014MA, Azusa Pacific University, 2013 BS, University of Benin, Nigeria, 2002 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Industrial and Organizational Psychology Walden UniversityAugust 202