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ABSTRACT: One of the most popular queries in vehicle navigation, continuous k nearest neighbor, has been widely addressed. However, none of them focuses on continuous lookahead k nearest neighbor. Hence, in this paper, we propose a new approach, called continuous lookahead K nearest neighbor (CLKNN). CLKNN query is different from the traditional continuous k nearest neighbor, whereby in our CLKNN, mobile users concerns with only the interest points in the forward space of query point according to a predefined moving direction. Interest points, which are behind the moving query point, are not of interest anymore. We propose algorithms for lookahead KNN as well as continuous lookahead KNN. The former is used for static query point, whereas the latter is used for moving query point. Our experiments verify the applicability of the proposed approach to solve queries which involve lookahead k nearest neighbors continuously.
Industrial Technology, 2009. ICIT 2009. IEEE International Conference on; 03/2009
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ABSTRACT: Broadcast indexing is necessary to be applied in a wireless broadcast environment as such the scheme helps mobile clients to find the desired data instances efficiently. This is particularly important considering the inherent limitations in mobile environment. In this paper, we present a global indexing scheme for location dependent queries. The proposed scheme is designed to serve queries in which the query result is relevant to client's location. Global indexing scheme aims to minimise index access time while having all the advantages of index broadcasting. We develop a simulation model to find out the access time performance of global indexing scheme as compared to non-global indexing scheme. Additionally, we analyse the efficiency of valid scope used in the global index scheme as compared with an existing valid scope. It is found that global index performs substantially better than the existing indexing concept.
Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on; 04/2005
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ABSTRACT: Data broadcasting is a scalable mechanism to disseminate information to a large number of mobile clients in a wireless environment. Our paper concerns with developing a data broadcasting system in a mobile environment, which involves data and index dissemination. The broadcasted data is retrieved from a database server. Subsequently, these data may be broadcasted periodically or aperiodically. As wireless environment inherent resource limitations, the use of indexing scheme will provide efficient data retrieval. We use share indices price context to demonstrate our proposed models. The models are developed using wireless ad-hoc network infrastructure.
Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on; 04/2005
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ABSTRACT: Data broadcasting strategy is known as a scalable way to disseminate information to mobile users. However, with a very large set of broadcast items, the query access time of mobile clients raise accordingly, due to high waiting time for mobile clients to find their data of interest. One possible solution is to split the database information into several broadcast channels. In this paper, we introduce taxonomy of index dissemination for multibroadcast channel based on B* tree structure. We consider three indexing schemes namely: (i) nonreplicated indexing scheme (NRI), (ii) partially-replicated indexing scheme (PRI), and (iii) fully-replicated indexing scheme (FRI). Simulation model is developed to find out the access time performance of each scheme.
Advanced Information Networking and Applications, 2004. AINA 2004. 18th International Conference on; 02/2004
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01/2001; PHYSICA-VERLAG.
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ABSTRACT: The growing self-organizing map (GSOM) algorithm is presented in
detail and the effect of a spread factor, which can be used to measure
and control the spread of the GSOM, is investigated. The spread factor
is independent of the dimensionality of the data and as such can be used
as a controlling measure for generating maps with different
dimensionality, which can then be compared and analyzed with better
accuracy. The spread factor is also presented as a method of achieving
hierarchical clustering of a data set with the GSOM. Such hierarchical
clustering allows the data analyst to identify significant and
interesting clusters at a higher level of the hierarchy, and continue
with finer clustering of the interesting clusters only. Therefore, only
a small map is created in the beginning with a low spread factor, which
can be generated for even a very large data set. Further analysis is
conducted on selected sections of the data and of smaller volume.
Therefore, this method facilitates the analysis of even very large data
sets
IEEE Transactions on Neural Networks 06/2000; · 2.95 Impact Factor
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ABSTRACT: The growing self-organizing map (GSOM) has been presented as an extended version of the self-organizing map (SOM), which has significant advantages for knowledge discovery applications. In this paper, the GSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the interesting clusters. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and as such of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.
IEEE Transactions on Neural Networks 02/2000; 11(3):601-14. · 2.95 Impact Factor
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Proceedings of the 6th International Conference on Automation, Robotics, Control and Vision. 01/2000;
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ABSTRACT: Self generating neural networks have been presented as a better
alternative to fixed structure networks in data mining applications. It
has also been shown that the nearest prototype classifier is
functionally equivalent to an alternative fuzzy classifier model.
Several supervised neural networks have been developed to generate
nearest prototypes which can be converted to fuzzy rules. We present an
extended version of our growing self-organising map (GSOM) model which
can also be used to identify nearest prototypes for generating fuzzy
rules
Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International; 02/1999
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ABSTRACT: Supervised and unsupervised self generating neural network
architectures have been used in the recent past. Our previous work
(1998) has described an unsupervised self generating feature map, called
the growing self organising map (GSOM). In this paper we describe some
extensions to the GSOM such that it could be used to map and analyse
more realistic data sets
Neural Networks, 1999. IJCNN '99. International Joint Conference on; 02/1999
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ABSTRACT: Bayesian belief networks have been used widely to solve many
decision problem that involve uncertainty. One major advantage of this
approach compared with other reasoning tools is its semantic richness in
describing the decision process. Some inference algorithms for carrying
out the reasoning process exist, but they are known to be
computationally expensive. Hence, they require optimisation to make them
practical. This paper proposes two optimisation techniques for Bayesian
belief networks. These optimisation techniques were investigated for
information retrieval applications, but can also be applied to different
applications outside the information retrieval area
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on; 11/1998
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ABSTRACT: We describe a data analysis method using a structure adapting
neural network with two additional layers. The neural network used is an
extended version of a self-organising feature map which can adapt its
structure to better represent the clusters in data. Once the clusters
are identified, we use two additional layers on the feature map to
analyse the clusters and the representation of attributes in the
clusters. Simulations and initial results with two simple benchmark data
sets are also described
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on; 11/1998
