How much technology can help our kinds and how much it can damage their lives?

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Semantic intelligence in a seaport context

This research work proposes the framework for seaport partners to interact on a semantic level and scope related with jurisdictions/ecosystems and regions to share knowledge among partners. New steps towards dealing with the traditional common sense for managing or governing the seaport are required for assisting the new generation of managers and port authorities. Sematic intelligence answers dilemmas of complex realities and alignments of strategies such as which strategic position may have the seaport facing the growing number of international networks and international treaties. In management and computational sciences, semantic intelligence has been discussed mostly from technological perspectives; however, a higher thinking intelligence for managing and govern the seaport surplus the classical intelligence approach found in literature

Computational intelligence for development of strategic decision making in port informational integration

Empirical thesis.Introduction -- Chapter 2. Literature review -- Chapter 3. The i-DMSS methodology for port integration -- Chapter 4. A multiple case study to support empirically port information integration - Chapter 5. Conclusions - Bibliography -- Appendices.Bibliography: pages 366-389.Cooperation between nations and states can lead to their mutual growth and prosperity. One aspect of integration concerns integration of information that has been the focus of this thesis, specifically, informational integration for ports, in recognition of the important roles that ports play in the nation's and world's economy. The motivation for this thesis has been the advancement of developing countries, particularly in Latin America and specifically in Colombia, the mother country of the author.This thesis focuses on port informational integration and the use of Computational Intelligence to assist knowledge discovery and aid in the complex decision-making modelling. To aid port informational integration and associated decision-making creation of an intelligent decisionmaking support system (i-DMSS) is needed. This thesis proposes the conceptual design, development, and empirical validation of a proof-of-concept prototype of an i-DMSS for port informational integration. To guide the design and validation of the i-DMSS, various strategies and business intelligence processes are presented. Computational Intelligence is implemented to demonstrate its use with a systemic-driven, data-driven and knowledge-driven perspective of the modelling problem. The systemic-driven perspective follows a systems approach towards port´s informational decisions, mainly shaped in this thesis through consideration of port sustainability issues where knowledge from coastal (eco) system and different types of port proximities (spatial and institutional) are essential conditions in becoming port partners for informational integration. The data-driven perspective contributes to the task of assembling datasets from information residing in publicly available repositories, promoting the need for standardised formats and query processing that is increasingly becoming a priority for ICT users. Finally, the knowledge-driven perspective offers to the community and practitioners the ability to learn from the metadata and metafeatures to build intelligent models for port informational integration that support the prototype design for a port-to-port solution, that to the best of the author's knowledge, is the first time for a solution of this type to be offered. Looking to the future implications of this research, the author estimates a new view of these information systems will offer to the port decision makers an opportunity to integrate their information, and informing stakeholders on relevant issues.Benefits can be delivered through cooperation and integration of ports (despite size, capacity, spatial proximity, regulations and jurisdictions/ecosystems). Towards answering to what extent the conceptual design of the i-DMSS for integration is relevant to the port domain, this thesis employs a port cluster perspective where multiple-case studies provide empirical validation to guide future i-DMSS deployment. The multiple-cases describe: 1) an analysis of local (existing or potential) port clusters in the United States (US), b) cross-regional (existing or potential) port clusters in both US (NAFTA-Corridors East, West and Gulf Coasts) and The European Union (EU) (Rijn-Schelde delta Region), and c) institutional port proximities based on jurisdictional mechanisms which represent influential dynamics far from the port borders in the context of Latin America, and specifically in Colombia.Conclusions consider the challenge of building Business Intelligence (BI) Systems for the port domain due to the requirements that need to be met from both the decisional and engineering sides of the system.Mode of access: World wide web1 online resource (539 pages) illustrations (some colour

A Customised dataset to assist legal and ethical governance of seaports

Attention to the legal and ethical principles of governance of seaport authorities (PAs) can enhance the future possibility of sustainable development of a port. This chapter presents a customised dataset and accompanying descriptions compiled from multiple sources and repositories that can be mined to provide adequate understanding over key decisional variables to assist the implementation of three Port State Control (PSC) mechanisms. Considerable care is given to the selection and combination of variables which may identify potentially serious accidents and the port’s legal and ethical liabilities. The authors seek to clarify the relationship between the Corporate Social Responsibility (CSR) of PAs and, what is possibly the most important issue facing PAs nowadays, the issue of security. In order to validate the relationship between PSC and CSR, the authors suggest the use of the Regression Approach in Time Series Analysis (RATS) method that offers an assessment of mutual impacts of the PSC variables and a forecast of future values of CSR. RATS would enable PAs to be aware of the CSR challenges occurring among partner ports at least one time-step ahead. This may represent an important advance in using decision support systems to assist managers in performing complex analyses and making strategic choices.19 page(s

Addressing challenges for knowledge discovery from data in the domain of seaport integration

Discovering knowledge from data for decision making is dependent on the existence of data relevant to the decision at hand. For decisions in domains that involve many different factors and concerns, such as seaport integration, data may exist across many repositories managed by different organizations with different goals and foci, not to mention different data structures, entities, labels, units of measurement, categories and time periods. To use this data for decision making, approaches to combine the data and handle missing values are two of the problems, among others, that need to be addressed. In this paper we discuss the need for managing micro and macro-level data and our approach to handle missing values.13 page(s

Identifying characteristics of seaports for environmental benchmarks based on meta-learning

In this paper we discuss a model which classifies any seaport in the context of environmental management system standards as leader, follower and average user. Identification of this status can assist Port Authorities (PAs) in making decisions concerned with finding collaborating seaport partners using clear environmental benchmarks. This paper demonstrates the suitability of meta-learning for small datasets to assist pre-selection of base-algorithms and automatic parameterization. The method is suitable for small number of observations with many attributes closely related with potential issues concerning environmental management programs on seaports. The variables in our dataset cover main aspects such as reducing air emissions, improving water quality and minimizing impacts of growth. We consider this model will be suitable for Port authorities (PAs) interested in effective and efficient methods of knowledge discovery to be able to gain the maximum advantage of benchmarking processes within partner ports. As well as for practitioners and non-expert users who want to construct a reliable classification process and reduce the evaluation time of data processing for environmental benchmarking.14 page(s

A Baseline Time Series Data Mining model for forecasts in port logistics and economics

This paper addresses the question of how to develop forecasting models resulting from business processes that can be embodied in an intelligent decision support system. Moreover the design is suitable for evolving logistics and economic situations in which ports plan or foresees to have an improved economic role. The key objective of this work is to offer a model-based approach to Time Series Data Mining (TSDM) based on the assumptions that the time series may be produced by an underlying model, and that its flexibility is suitable to perform multivariate time-series analysis encompassing the notion of model selection and statistical learning known as the core of forecasting systems. Results indicate that for the period 2001 to 2005, the commodity throughput of coffee (tons) handled in the port of Buenaventura gains importance in the prediction of the Colombian national exports of coffee, thus indicating that the port operation was able to affect the economy in this regard. The previous period was strongly affected by outliers, creating a random walk process difficult to fit but feasible to produce due to unstable conditions evidenced in the economy.6 page(s

Forecasting in port logistics and economics using Time Series Data Mining model

This paper addresses the question of how to develop forecasting models resulting from business processes that can be embodied in an intelligent decision support system. Moreover the design is suitable for evolving logistics and economic situations in which ports plan or foresee to have an improved economic role. The work presented in this paper also forms one component of a conceptual intelligent decision-making support system (i-DMSS) for port integration. The key objective of this work is to offer a model-based approach to Time Series Data Mining (TSDM) based on the assumptions that the time series may be produced by an underlying model, and that its flexibility is suitable to perform multivariate time-series analysis encompassing the notion of model selection and statistical learning known as the core of forecasting systems. The interrelated activities to induce domain knowledge are specified as the data collection principles, the descriptive modelling and normative modelling. Results indicate that for the period 2001 to 2005, the commodity throughput of coffee (tons) handled in the port of Buenaventura gains importance in the prediction of the Colombian national exports of coffee, thus indicating that the port operation was able to affect the economy in this regard. The previous period was strongly affected by outliers, creating a random walk process difficult to fit but feasible to produce due to unstable conditions evidenced in the economy.12 page(s

Computational Intelligence to Support Cooperative Seaport Decision-Making in Environmental and Ecological Sustainability

15 páginasThe substantial amounts of information that must be gathered, preserved, and used to analyse environmental and ecological impacts on seaports such as the international standards, deserve a direct way to manage and improve those impacts in a seaport through a systematic environmental management system (EMS). We present an artefact called the conceptual intelligent decision-making support module (i-DMSS) to enhance cooperative seaport decision-making (COSEADM) in environmental and ecological sustainability. Three interrelated activities of data collection, descriptive and normative modelling, incorporate processes of handling the decision-making side and processes integrating engineering requirements to produce the conceptual i-DMSS module. We include two data-driven models to handle the decision-making side of this module and automatically induce domain knowledge. Besides, we deploy and standardise the data-driven models and use the Predictive modelling markup language (PMML) to show advantages of data interoperability. Finally, we offer the rationale of the ontological process to anticipate and provide illustration of how to describe concepts in regard to COSEADM for environmental and ecological sustainability. This module demonstrates how the capture and interoperation of information and decisional structures can be managed