Bayesian belief network-based project complexity measurement considering causal relationships

This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects

Incremental algorithm for Decision Rule generation in data stream contexts

Actualmente, la ciencia de datos está ganando mucha atención en diferentes sectores. Concretamente en la industria, muchas aplicaciones pueden ser consideradas. Utilizar técnicas de ciencia de datos en el proceso de toma de decisiones es una de esas aplicaciones que pueden aportar valor a la industria. El incremento de la disponibilidad de los datos y de la aparición de flujos continuos en forma de data streams hace emerger nuevos retos a la hora de trabajar con datos cambiantes. Este trabajo presenta una propuesta innovadora, Incremental Decision Rules Algorithm (IDRA), un algoritmo que, de manera incremental, genera y modifica reglas de decisión para entornos de data stream para incorporar cambios que puedan aparecer a lo largo del tiempo. Este método busca proponer una nueva estructura de reglas que busca mejorar el proceso de toma de decisiones, planteando una base de conocimiento descriptiva y transparente que pueda ser integrada en una herramienta decisional. Esta tesis describe la lógica existente bajo la propuesta de IDRA, en todas sus versiones, y propone una variedad de experimentos para compararlas con un método clásico (CREA) y un método adaptativo (VFDR). Conjuntos de datos reales, juntamente con algunos escenarios simulados con diferentes tipos y ratios de error, se utilizan para comparar estos algoritmos. El estudio prueba que IDRA, específicamente la versión reactiva de IDRA (RIDRA), mejora la precisión de VFDR y CREA en todos los escenarios, tanto reales como simulados, a cambio de un incremento en el tiempo.Nowadays, data science is earning a lot of attention in many different sectors. Specifically in the industry, many applications might be considered. Using data science techniques in the decision-making process is a valuable approach among the mentioned applications. Along with this, the growth of data availability and the appearance of continuous data flows in the form of data stream arise other challenges when dealing with changing data. This work presents a novel proposal of an algorithm, Incremental Decision Rules Algorithm (IDRA), that incrementally generates and modify decision rules for data stream contexts to incorporate the changes that could appear over time. This method aims to propose new rule structures that improve the decision-making process by providing a descriptive and transparent base of knowledge that could be integrated in a decision tool. This work describes the logic underneath IDRA, in all its versions, and proposes a variety of experiments to compare them with a classical method (CREA) and an adaptive method (VFDR). Some real datasets, together with some simulated scenarios with different error types and rates are used to compare these algorithms. The study proved that IDRA, specifically the reactive version of IDRA (RIDRA), improves the accuracies of VFDR and CREA in all the studied scenarios, both real and simulated, in exchange of more time