Assessing organizations collaboration readiness: a behavioral approach

Dissertation presented at the Faculty of Sciences and Technology of the New University of Lisbon to obtain the degree of Doctor in Electrical Engineering, specialty of Robotics and Integrated ManufacturingThis thesis presents an approach for assessing organizations‘ readiness to collaborate. This assessment is based in three fundamental aspects, namely (1) on collaboration preparedness, which aims at assessing whether a partner has adequate collaboration-related character traits; (2) on competencies fitness which is predominantly aimed at assessing how well an organization is able to use its competencies in a collaboration context; and (3) on willingness to collaborate, which is a concept applied to assess whether an organization is, or is not, really interested to engage in concrete collaboration opportunities. The proposed approach contributes to the formation of improved collaborative networks, increasing their likelihood of success. The principal characteristic of the model lies in the fact that it follows a behavioral perspective. As such, collaboration preparedness is based on the idea of the organizations‘ character, traits and behavioral patterns. Competencies fitness is in turn based on the so-called soft competencies, exploring the performance influences/effects of the soft competencies on the hard ones in a collaboration context. Finally, willingness to collaborate is based on the organization‘s planned behavior, attitudes and intentions that are perceived in/from a partner. The work involved in the conceptualization of readiness to collaborate includes the utilization of text data mining to discover the behavioral aspects, namely the collaboration-related organization‘s traits which are relevant for assessing collaboration readiness. Bayesian belief networks are proposed as a way to deal with the underlying uncertainty in assessing collaboration readiness. A soft versus hard competencies dichotomy is used to develop the concept of competencies fitness, proposing the adjusted competencies profile and the fitness level, as the way to assess whether a partner‘s competencies fit in a collaboration opportunity. The Theory of the Planned Behavior is adapted from social sciences and used in organizations in collaboration contexts. Various modeling experiments were performed to assist in the development of this readiness concept. The validation through some cases of partnerships is proposed to evaluate the underlying collaboration readiness assessment model

Expert System for Crop Disease based on Graph Pattern Matching: A proposal

Para la agroindustria, las enfermedades en cultivos constituyen uno de los problemas más frecuentes que generan grandes pérdidas económicas y baja calidad en la producción. Por otro lado, desde las ciencias de la computación, han surgido diferentes herramientas cuya finalidad es mejorar la prevención y el tratamiento de estas enfermedades. En este sentido, investigaciones recientes proponen el desarrollo de sistemas expertos para resolver este problema haciendo uso de técnicas de minería de datos e inteligencia artificial, como inferencia basada en reglas, árboles de decisión, redes bayesianas, entre otras. Además, los grafos pueden ser usados para el almacenamiento de los diferentes tipos de variables que se encuentran presentes en un ambiente de cultivos, permitiendo la aplicación de técnicas de minería de datos en grafos, como el emparejamiento de patrones en los mismos. En este artículo presentamos una visión general de las temáticas mencionadas y una propuesta de un sistema experto para enfermedades en cultivos, basado en emparejamiento de patrones en grafos.For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching

Model-Driven Methodology for Rapid Deployment of Smart Spaces based on Resource-Oriented Architectures

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym

Selection of Online News for Competitive Intelligence: Use of Business Domain Ontology for Internet Search Semantic Query Expansion

The Internet provides ever increasing volumes of news and information about the environment in which companies operate. This can lead to information overload, in which the volume of information available overwhelms the processing power of the user. Methods and tools that help separate potentially useful information from irrelevant information need to be developed. This research applied design research to investigate the development of a tool to help users refine internet searches on competitive intelligence. It used modeling of the target business area in the form of anontology to aid the formulation of search terms through interactive semantic expansion of the keywords entered by users

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior