An Integrated Decision Support Toolbox (DST) for the Management of Mountain Protected Areas

New tools and methodologies are required in systemic planning and management of mountain protected areas. Among others we propose here a decision support toolbox (DST) conceived as an integrated collection of both soft and hard system methodologies, consisting of participatory and computer-based modules to provide a set of integrated, self-contained tools and approaches to support decision-making processes in the management of mountain protected areas. The Sagarmatha National Park and Buffer Zone (SNPBZ) in Nepal was taken as a pilot case. A number of participatory exercises such as participatory 3-dimensional modeling, scenario planning, and qualitative modeling were carried out to understand social-ecological processes and generate a systemic view over space and time. The qualitative models were then converted into computer-based system dynamics models. The design and development of DST software were carried out with an incremental and modular approach. This process involved stakeholder analysis and decision-making processes through a series of consultations. The software was developed with the main modules including scenario analysis, spatial analysis, and knowledge base. The scenario analysis module runs system dynamics models built in Simile software and provides functions to link them with spatial data for model inputs and outputs. The spatial analysis module provides the basic geographic information system functions to explore, edit, analyze, and visualize spatial information. The knowledge base module was developed as a metadata management system for different categories of information such as spatial data, bibliography, research data, and models. The development of DST software, especially system dynamics modeling and its linkage with spatial components, provided an important methodological approach for spatial and temporal integration. Furthermore, training and interactions with park managers and concerned stakeholders showed that DST is a useful platform for integrating data and information and better understanding ecosystem behavior as a basis for management decisions

Advanced Information System for Safety-Critical Processes

The paper deals with the design and implementation of an intelligent modular information system (IMIS) for modeling and predictive decision making supervisory control of some important critical processes in a nuclear power plant (nuclear reactor) using selected soft computing methods. The developed IMIS enables monitoring critical states, safety impact analysis and prediction of dangerous situations. It also recommends the operator possibilities how to proceed to ensure safety of operations and humans and environment. The proposed complex IMIS has been tested on real data from a nuclear power plant process primarily used as supervisory information for decision making support and management of critical processes. The core of the proposed IMIS is a general nonlinear neural network mathematical model. For prediction of selected process variables an artificial neural network of multilayer perceptron type (MLP) has been used. The effective Levenberg-Marquardt method was used to train the MLP network. Testing and verification of the neural prediction model were carried out on real operating data measurements obtained from the NPP Jaslovske Bohunice

Business Domain Modelling using an Integrated Framework

This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modelling Language (UML), and an implementation pattern known as “Naked Objects”. This framework have been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study “Information Retrieval System for academic research” is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modelling. The framework is overviewed and justified as multimethodology using Mingers multimethodology ideas

A goal-oriented requirements modelling language for enterprise architecture

Methods for enterprise architecture, such as TOGAF, acknowledge the importance of requirements engineering in the development of enterprise architectures. Modelling support is needed to specify, document, communicate and reason about goals and requirements. Current modelling techniques for enterprise architecture focus on the products, services, processes and applications of an enterprise. In addition, techniques may be provided to describe structured requirements lists and use cases. Little support is available however for modelling the underlying motivation of enterprise architectures in terms of stakeholder concerns and the high-level goals that address these concerns. This paper describes a language that supports the modelling of this motivation. The definition of the language is based on existing work on high-level goal and requirements modelling and is aligned with an existing standard for enterprise modelling: the ArchiMate language. Furthermore, the paper illustrates how enterprise architecture can benefit from analysis techniques in the requirements domain

Environments to support collaborative software engineering

With increasing globalisation of software production, widespread use of software components, and the need to maintain software systems over long periods of time, there has been a recognition that better support for collaborative working is needed by software engineers. In this paper, two approaches to developing improved system support for collaborative software engineering are described: GENESIS and OPHELIA. As both projects are moving towards industrial trials and eventual publicreleases of their systems, this exercise of comparing and contrasting our approaches has provided the basis for future collaboration between our projects particularly in carrying out comparative studies of our approaches in practical use

Applying tropos to socio-technical system design and runtime configuration

Recent trends in Software Engineering have introduced the importance of reconsidering the traditional idea of software design as a socio-tecnical problem, where human agents are integral part of the system along with hardware and software components. Design and runtime support for Socio-Technical Systems (STSs) requires appropriate modeling techniques and non-traditional infrastructures. Agent-oriented software methodologies are natural solutions to the development of STSs, both humans and technical components are conceptualized and analyzed as part of the same system. In this paper, we illustrate a number of Tropos features that we believe fundamental to support the development and runtime reconfiguration of STSs. Particularly, we focus on two critical design issues: risk analysis and location variability. We show how they are integrated and used into a planning-based approach to support the designer in evaluating and choosing the best design alternative. Finally, we present a generic framework to develop self-reconfigurable STSs

Fuzzy investment decision support for brownfield redevelopment

Tato disertační práce se zaměřuje na problematiku investování a podporu rozhodování pomocí moderních metod. Zejména pokud jde o analýzu, hodnocení a výběr tzv. brownfieldů pro jejich redevelopment (revitalizaci). Cílem této práce je navrhnout univerzální metodu, která usnadní rozhodovací proces. Proces rozhodování je v praxi komplikován též velkým počet relevantních parametrů ovlivňujících konečné rozhodnutí. Navržená metoda je založena na využití fuzzy logiky, modelování, statistické analýzy, shlukové analýzy, teorie grafů a na sofistikovaných metodách sběru a zpracování informací. Nová metoda umožňuje zefektivnit proces analýzy a porovnávání alternativních investic a přesněji zpracovat velký objem informací. Ve výsledku tak bude zmenšen počet prvků množiny nejvhodnějších alternativních investic na základě hierarchie parametrů stanovených investorem.This dissertation focuses on decision making, investing and brownfield redevelopment. Especially on the analysis, evaluation and selection of previously used real estates suitable for commercial use. The objective of this dissertation is to design a method that facilitates the decision making process with many possible alternatives and large number of relevant parameters influencing the decision. The proposed method is based on the use of fuzzy logic, modeling, statistic analysis, cluster analysis, graph theory and sophisticated methods of information collection and processing. New method allows decision makers to process much larger amount of information and evaluate possible investment alternatives efficiently.

AI-assisted Software Development Effort Estimation

Effort estimation is a critical aspect of software project management. Without accurate estimates of the developer effort a particular project will require, the project's timeline and resourcing cannot be efficiently planned, which greatly increases the likelihood of the project failing to meet at least some of its goals. The goal of this thesis is to apply machine learning methods to analyze the work hour data logged by individual employees in order to provide project management with useful estimations of how much more effort it will take to finish a given project, and how long that will take. The work is conducted for ATR Soft Oy, using the data from their internal work hour logging tool. At first a literature review is conducted to determine what kind of estimation methods and tools are currently used in the software industry, and what kind of objectives and requirements organizations commonly set for their estimation processes. The basics of machine learning are explained, and a brief look is taken at how machine learning is currently used to support software engineering and project management. The literature review revealed that while machine learning methods have been applied to software project estimation for decades at this point, such data-driven methods generally suffer from a lack of relevant historical project data, and thus aren't commonly used in the industry. Initial insights were gathered from the work hour data and analysis goals were refined accordingly. The data was pre-processed to a form suitable for training machine learning models. Two different modeling scenarios were tested: Creating a single general model from all available data, and creating multiple project-specific models of a more limited scope. The modeling performance data indicates that machine learning models based on work hour data are capable of achieving better results in some situations than traditional expert estimation. The models developed here are not reliable enough to be used as the sole estimation method, but can provide useful additional information to support decision making

A Call to Arms: Revisiting Database Design

Good database design is crucial to obtain a sound, consistent database, and - in turn - good database design methodologies are the best way to achieve the right design. These methodologies are taught to most Computer Science undergraduates, as part of any Introduction to Database class. They can be considered part of the "canon", and indeed, the overall approach to database design has been unchanged for years. Moreover, none of the major database research assessments identify database design as a strategic research direction. Should we conclude that database design is a solved problem? Our thesis is that database design remains a critical unsolved problem. Hence, it should be the subject of more research. Our starting point is the observation that traditional database design is not used in practice - and if it were used it would result in designs that are not well adapted to current environments. In short, database design has failed to keep up with the times. In this paper, we put forth arguments to support our viewpoint, analyze the root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change