Abstract: Effective evaluation of software development effort is an important aspect of successful project management. Based on a large database with 4106 projects ever developed, this study statistically examines the factors that influence development effort. The factors found to be significant for effort are project size, average number of developers that worked on the project, type of development, development language, development platform, and the use of rapid application development. Among these factors, project size is the most critical cost driver. Unsurprisingly, this study found that the use of CASE tools does not necessarily reduce development effort, which adds support to the claim that the use of tools is subtle. As many of the current estimation models are rarely or unsuccessfully used, this study proposes a parsimonious parametric model for the prediction of effort which is both simple and more accurate than previous models.
Abstract: In this paper a novel algorithm is proposed that integrates the process of fuzzy hierarchy generation and rule discovery for automated discovery of Production Rules with Fuzzy Hierarchy (PRFH) in large databases.A concept of frequency matrix (Freq) introduced to summarize large database that helps in minimizing the number of database accesses, identification and removal of irrelevant attribute values and weak classes during the fuzzy hierarchy generation.Experimental results have established the effectiveness of the proposed algorithm.
Abstract: The use of a Bayesian Hierarchical Model (BHM) to interpret breath measurements obtained during a 13C Octanoic Breath Test (13COBT) is demonstrated. The statistical analysis was implemented using WinBUGS, a commercially available computer package for Bayesian inference. A hierarchical setting was adopted where poorly defined parameters associated with a delayed Gastric Emptying (GE) were able to "borrow" strength from global distributions. This is proved to be a sufficient tool to correct model's failures and data inconsistencies apparent in conventional analyses employing a Non-linear least squares technique (NLS). Direct comparison of two parameters describing gastric emptying ng ( tlag -lag phase, t1/ 2 -half emptying time) revealed a strong correlation between the two methods. Despite our large dataset ( n = 164 ), Bayesian modeling was fast and provided a successful fitting for all subjects. On the contrary, NLS failed to return acceptable estimates in cases where GE was delayed.
Abstract: Mining Sequential Patterns in large databases has become
an important data mining task with broad applications. It is
an important task in data mining field, which describes potential
sequenced relationships among items in a database. There are many
different algorithms introduced for this task. Conventional algorithms
can find the exact optimal Sequential Pattern rule but it takes a
long time, particularly when they are applied on large databases.
Nowadays, some evolutionary algorithms, such as Particle Swarm
Optimization and Genetic Algorithm, were proposed and have been
applied to solve this problem. This paper will introduce a new kind
of hybrid evolutionary algorithm that combines Genetic Algorithm
(GA) with Particle Swarm Optimization (PSO) to mine Sequential
Pattern, in order to improve the speed of evolutionary algorithms
convergence. This algorithm is referred to as SP-GAPSO.
Abstract: In this paper, we present a system for content-based
retrieval of large database of classified satellite images, based on
user's relevance feedback (RF).Through our proposed system, we
divide each satellite image scene into small subimages, which stored
in the database. The modified radial basis functions neural network
has important role in clustering the subimages of database according
to the Euclidean distance between the query feature vector and the
other subimages feature vectors. The advantage of using RF
technique in such queries is demonstrated by analyzing the database
retrieval results.
Abstract: This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in
system modeling of nonlinear systems; the five nonlinear systems are
dual tank system, single tank system, DC motor system, and two
academic models. The feed forward method is considered in this
work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of
radial basis function network, because it is reliable, offers fast
convergence and can handle large data sets. The least mean square
method is used to adjust the weights to the output layer, and
Euclidean distance method used to measure the width of the Gaussian
function.
Abstract: Response surface methodology (RSM) is a very
efficient tool to provide a good practical insight into developing new
process and optimizing them. This methodology could help
engineers to raise a mathematical model to represent the behavior of
system as a convincing function of process parameters.
Through this paper the sequential nature of the RSM surveyed for process
engineers and its relationship to design of experiments (DOE), regression
analysis and robust design reviewed. The proposed four-step procedure in
two different phases could help system analyst to resolve the parameter
design problem involving responses. In order to check accuracy of the
designed model, residual analysis and prediction error sum of squares
(PRESS) described.
It is believed that the proposed procedure in this study can resolve a
complex parameter design problem with one or more responses. It can be
applied to those areas where there are large data sets and a number of
responses are to be optimized simultaneously. In addition, the proposed
procedure is relatively simple and can be implemented easily by using
ready-made standard statistical packages.
Abstract: Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
Abstract: The goal of data mining algorithms is to discover
useful information embedded in large databases. One of the most
important data mining problems is discovery of frequently occurring
patterns in sequential data. In a multidimensional sequence each
event depends on more than one dimension. The search space is quite
large and the serial algorithms are not scalable for very large
datasets. To address this, it is necessary to study scalable parallel
implementations of sequence mining algorithms.
In this paper, we present a model for multidimensional sequence
and describe a parallel algorithm based on data parallelism.
Simulation experiments show good load balancing and scalable and
acceptable speedup over different processors and problem sizes and
demonstrate that our approach can works efficiently in a real parallel
computing environment.
Abstract: As many scientific applications require large data processing, the importance of parallel I/O has been increasingly recognized. Collective I/O is one of the considerable features of parallel I/O and enables application programmers to easily handle their large data volume. In this paper we measured and analyzed the performance of original collective I/O and the subgroup method, the way of using collective I/O of MPI effectively. From the experimental results, we found that the subgroup method showed good performance with small data size.
Abstract: In this paper, we present a new learning algorithm for
anomaly based network intrusion detection using improved self
adaptive naïve Bayesian tree (NBTree), which induces a hybrid of
decision tree and naïve Bayesian classifier. The proposed approach
scales up the balance detections for different attack types and keeps
the false positives at acceptable level in intrusion detection. In
complex and dynamic large intrusion detection dataset, the detection
accuracy of naïve Bayesian classifier does not scale up as well as
decision tree. It has been successfully tested in other problem
domains that naïve Bayesian tree improves the classification rates in
large dataset. In naïve Bayesian tree nodes contain and split as
regular decision-trees, but the leaves contain naïve Bayesian
classifiers. The experimental results on KDD99 benchmark network
intrusion detection dataset demonstrate that this new approach scales
up the detection rates for different attack types and reduces false
positives in network intrusion detection.
Abstract: Computation of facility location problem for every
location in the country is not easy simultaneously. Solving the
problem is described by using cluster computing. A technique is to
design parallel algorithm by using local search with single swap
method in order to solve that problem on clusters. Parallel
implementation is done by the use of portable parallel programming,
Message Passing Interface (MPI), on Microsoft Windows Compute
Cluster. In this paper, it presents the algorithm that used local search
with single swap method and implementation of the system of a
facility to be opened by using MPI on cluster. If large datasets are
considered, the process of calculating a reasonable cost for a facility
becomes time consuming. The result shows parallel computation of
facility location problem on cluster speedups and scales well as
problem size increases.
Abstract: With the rapid development in the field of life
sciences and the flooding of genomic information, the need for faster
and scalable searching methods has become urgent. One of the
approaches that were investigated is indexing. The indexing methods
have been categorized into three categories which are the lengthbased
index algorithms, transformation-based algorithms and mixed
techniques-based algorithms. In this research, we focused on the
transformation based methods. We embedded the N-gram method
into the transformation-based method to build an inverted index
table. We then applied the parallel methods to speed up the index
building time and to reduce the overall retrieval time when querying
the genomic database. Our experiments show that the use of N-Gram
transformation algorithm is an economical solution; it saves time and
space too. The result shows that the size of the index is smaller than
the size of the dataset when the size of N-Gram is 5 and 6. The
parallel N-Gram transformation algorithm-s results indicate that the
uses of parallel programming with large dataset are promising which
can be improved further.
Abstract: The present study presents a new approach to automatic
data clustering and classification problems in large and complex
databases and, at the same time, derives specific types of explicit rules
describing each cluster. The method works well in both sparse and
dense multidimensional data spaces. The members of the data space
can be of the same nature or represent different classes. A number
of N-dimensional ellipsoids are used for enclosing the data clouds.
Due to the geometry of an ellipsoid and its free rotation in space
the detection of clusters becomes very efficient. The method is based
on genetic algorithms that are used for the optimization of location,
orientation and geometric characteristics of the hyper-ellipsoids. The
proposed approach can serve as a basis for the development of
general knowledge systems for discovering hidden knowledge and
unexpected patterns and rules in various large databases.