Abstract: This paper presents an approach for easy creation and
classification of institutional risk profiles supporting endangerment
analysis of file formats. The main contribution of this work is the
employment of data mining techniques to support set up of the most
important risk factors. Subsequently, risk profiles employ risk factors
classifier and associated configurations to support digital preservation
experts with a semi-automatic estimation of endangerment group
for file format risk profiles. Our goal is to make use of an expert
knowledge base, accuired through a digital preservation survey
in order to detect preservation risks for a particular institution.
Another contribution is support for visualisation of risk factors for
a requried dimension for analysis. Using the naive Bayes method,
the decision support system recommends to an expert the matching
risk profile group for the previously selected institutional risk profile.
The proposed methods improve the visibility of risk factor values
and the quality of a digital preservation process. The presented
approach is designed to facilitate decision making for the preservation
of digital content in libraries and archives using domain expert
knowledge and values of file format risk profiles. To facilitate
decision-making, the aggregated information about the risk factors
is presented as a multidimensional vector. The goal is to visualise
particular dimensions of this vector for analysis by an expert and
to define its profile group. The sample risk profile calculation and
the visualisation of some risk factor dimensions is presented in the
evaluation section.
Abstract: The design of distribution logistics has a decisive impact on a company's logistics costs and performance. Hence, such solutions make an essential contribution to corporate success. This article describes a decision support system for analyzing the potential of distribution logistics in terms of logistics costs and performance. In contrast to previous procedures of business process re-engineering (BPR), this method maps distribution logistics holistically under variable distribution structures. Combined with qualitative measures the decision support system will contribute to a more efficient design of distribution logistics.
Abstract: This paper presents an approach for the classification of
an unstructured format description for identification of file formats.
The main contribution of this work is the employment of data mining
techniques to support file format selection with just the unstructured
text description that comprises the most important format features for
a particular organisation. Subsequently, the file format indentification
method employs file format classifier and associated configurations to
support digital preservation experts with an estimation of required file
format. Our goal is to make use of a format specification knowledge
base aggregated from a different Web sources in order to select file
format for a particular institution. Using the naive Bayes method,
the decision support system recommends to an expert, the file format
for his institution. The proposed methods facilitate the selection of
file format and the quality of a digital preservation process. The
presented approach is meant to facilitate decision making for the
preservation of digital content in libraries and archives using domain
expert knowledge and specifications of file formats. To facilitate
decision-making, the aggregated information about the file formats is
presented as a file format vocabulary that comprises most common
terms that are characteristic for all researched formats. The goal is to
suggest a particular file format based on this vocabulary for analysis
by an expert. The sample file format calculation and the calculation
results including probabilities are presented in the evaluation section.
Abstract: Flash Floods, together with landslides, are a common
natural threat for people living in mountainous regions and foothills.
One way to deal with this constant menace is the use of Early
Warning Systems, which have become a very important mitigation
strategy for natural disasters.
In this work we present our proposal for a pilot Flash Flood Early
Warning System for Santiago, Chile, the first stage of a more
ambitious project that in a future stage shall also include early
warning of landslides.
To give a context for our approach, we first analyze three existing
Flash Flood Early Warning Systems, focusing on their general
architectures. We then present our proposed system, with main focus
on the decision support system, a system that integrates empirical
models and fuzzy expert systems to achieve reliable risk estimations.
Abstract: The purposes of this study were to design and find
users’ satisfaction after using the decision support system for tourism
northern part of Thailand, which can provide tourists touristic
information and plan their personal voyage. Such information can be
retrieved systematically based on personal budget and provinces. The
samples of this study were five experts and users 30 persons white
collars in Bangkok. This decision support system was designed via
ASP.NET. Its database was developed by using MySQL, for
administrators are able to effectively manage the database. The
application outcome revealed that the innovation works properly as
sought in objectives. Specialists and white collars in Bangkok have
evaluated the decision support system; the result was satisfactorily
positive.
Abstract: This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well.
Abstract: This paper presents an alternate approach that uses
artificial neural network to simulate the flood level dynamics in a
river basin. The algorithm was developed in a decision support
system environment in order to enable users to process the data. The
decision support system is found to be useful due to its interactive
nature, flexibility in approach and evolving graphical feature and can
be adopted for any similar situation to predict the flood level. The
main data processing includes the gauging station selection, input
generation, lead-time selection/generation, and length of prediction.
This program enables users to process the flood level data, to
train/test the model using various inputs and to visualize results. The
program code consists of a set of files, which can as well be modified
to match other purposes. This program may also serve as a tool for
real-time flood monitoring and process control. The running results
indicate that the decision support system applied to the flood level
seems to have reached encouraging results for the river basin under
examination. The comparison of the model predictions with the
observed data was satisfactory, where the model is able to forecast
the flood level up to 5 hours in advance with reasonable prediction
accuracy. Finally, this program may also serve as a tool for real-time
flood monitoring and process control.
Abstract: This paper proposes a novel architecture for developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be incorporated into a computerized system and, at the same time, to preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.
Abstract: Ontology is widely being used as a tool for organizing
information, creating the relation between the subjects within the
defined knowledge domain area. Various fields such as Civil,
Biology, and Management have successful integrated ontology in
decision support systems for managing domain knowledge and to
assist their decision makers. Gross pollutant traps (GPT) are devices
used in trapping and preventing large items or hazardous particles in
polluting and entering our waterways. However choosing and
determining GPT is a challenge in Malaysia as there are inadequate
GPT data repositories being captured and shared. Hence ontology is
needed to capture, organize and represent this knowledge into
meaningful information which can be contributed to the efficiency of
GPT selection in Malaysia urbanization. A GPT Ontology framework
is therefore built as the first step to capture GPT knowledge which
will then be integrated into the decision support system. This paper
will provide several examples of the GPT ontology, and explain how
it is constructed by using the Protégé tool.