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RNA is a unique biopolymer that has the ability to store genetic information, like DNA, but also can have a functional role in the cell, like protein. The function of an RNA is determined by its sequence and structure, and the RNA structure is to a large extent determined by RNA's ability to form base pairs with itself. Most work has been done to predict structures that do not contain pseudoknots. Pseudoknots are usually excluded due to the hardness of examining all possible structures efficiently and model the energy correctly. In this paper we will present characterization of Pseudobase and then we will introduce an improved version of dynamic programming solution to find the conformation with the maximum number of base pairs. After then we will introduce an implementation of predicting H-type pseudoknots based on dynamic programming. Our algorithm called "Iterated Dynamic Programming" has better space and time complexity than the previously known algorithms. The algorithm has a worst case complexity of O(N3) in time and O(N2) in storage. In addition, our approach can be easily extended and applied to other classes of more general pseudoknots. Availability: The algorithm has been implemented in C++ in a program called "IDP", which is available at http://dblab.cbu.ac.kr/idp. [ABSTRACT FROM AUTHOR]

Recently semantic web is an important issue in many areas. Ontology specifies the knowledge in a specific domain and defines the concepts of knowledge and the relationships between concepts. To specify and define the knowledge in a specific domain, it is required to generate the ontology which conceptualizes the knowledge. Accordingly, to search the information of potential customers for home-delivery marketing of post office, we design a specific domain to generate the ontology in this paper. And we propose how to retrieve the information, using the generated ontology. We also implement the data search robot which collects the information based on the generated ontology. Therefore we confirm that the ontology and the search robot perform the information retrieval exactly. [ABSTRACT FROM AUTHOR]

A mass of biological sequence and structural information have been produced in biological laboratories since the techniques to get the sequences of genomes or proteins have been improved in HGP (Human Genome Project). Unfortunately, there are scarcely software packages available to deal with the biological data in most of biological laboratories and they are just stored in file formats. An integrated management system of biological data is required to manage the sequence and annotation data taken from other open databases to improve the analysis of sequence data in biological laboratories. We therefore suggest a personalized system to edit, store and retrieve biological information, and convert the formats of sequence data, as well as to integrate and manage data. [ABSTRACT FROM AUTHOR]

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g. as catalytic molecules. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactory in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modelling. In this paper, we present a computation the maximum number of base pairs of an RNA sequence with simple pseudoknots. Our approach is based on pseudoknot technique proposed by Akutsu. We show that a structure with the maximum possible number of base pairs could be deduced by a improved Nussinov's trace-back procedure. In our approach we also considered wobble base pairings (G·U). We introduce an implementation of RNA secondary structure prediction with simple pseudoknots based on dynamic programming algorithm. To evaluate our method we use the 15 sequences with simple pseudoknots of variable size from 19 to 25 nucleotides. We get our experimental data set from PseudoBase. Our program predicts simple pseudoknots with correct or almost correct structure for 53% sequences. [ABSTRACT FROM AUTHOR]

With the increasing expansion of network scale, users consume much cost for network that is composed of various network environments. In this paper, we propose a dynamic network management system employing the management program actively generating facilities in order to reduce the cost and time of system development. The proposed system consists of basic components for real time network management and Automatic Generation Technique of Network Components (AG-NC) in order to actively support information generation for network management objects through these basic components. The basic components includes configuration management, performance management, and fault management. AG-NC includes NE Basic Info Handler, MIB Handler, Template Handler, and Operation Handler. This system is able to generate a network management program with SNMP manager using the information of network objects. Our system actively generates network objects in an automatic manner instead of the manual manner of the existing systems. Finally, we can make the network structure expansion because the development time and cost of the network management program can be reduced dramatically through our system. [ABSTRACT FROM AUTHOR]

In this paper, the focus is on accelerating XPath location steps for evaluating regular path expression with predicate parameter in particular since it is a core component of many XML processing standards such as XSLT or XQuery. We present a new indexing structure, namely Xp-tree, which is used to speed-up the evaluation of XPath. Based on accelerating a node using planar combined with the numbering scheme, we devise efficiently derivative algorithms. Our experimental results demonstrate that the proposed method outperforms previous approaches using R-tree indexing mechanism in processing XML queries. [ABSTRACT FROM AUTHOR]

The main purpose of our study is to propose a novel methodology to develop the multi-parametric feature including linear and nonlinear features of HRV (Heart Rate Variability) diagnosing cardiovascular disease. To develop the multi-parametric feature of HRV, we used the statistical and classification techniques. This study analyzes the linear and the non-linear properties of HRV for three recumbent positions, namely the supine, left lateral and right lateral position. Interaction effect between recumbent positions and groups (normal and patients) was observed based on the HRV indices and the extracted HRV indices used to classify the CAD (Coronary Artery Disease) group from the normal people. We have carried out various experiments on linear and non-linear features of HRV indices to evaluate several classifiers, e.g., Bayesian classifiers, CMAR, C4.5 and SVM. In our experiments, SVM outperformed the other classifiers. [ABSTRACT FROM AUTHOR]

Though the number of completely sequenced genomes quickly grows in recent years, the methods to predict protein functions by homology from the genomes have not been used sufficiently. It has been a successful technique to construct an OPCs(Orthologous Protein Clusters) with the best reciprocal BLAST hits from multiple complete-genomes. But it takes time-consuming-processes to make the OPCs with manual work. We, here, propose an automatic method that clusters OPs(Orthologous Proteins) from multiple complete-genomes, which is, to be extended, based on INPARANOID which is an automatic program to detect OPs between two complete-genomes. We also prove all possible clustering mathematically. [ABSTRACT FROM AUTHOR]

The converge of location-aware devices, GIS functionalities and the increasing accuracy and availability of positioning technologies pave the way to a range of new types of location-based services. The field of spatiotemporal data mining where relationships are defined by spatial and temporal aspect of data is encountering big challenges since the increased search space of knowledge. In this study, we aim to propose algorithms for mining spatiotemporal patterns in mobile environment. Moving patterns are generated utilizing two algorithms called All_MOP and Max_MOP. The first one mines all frequent patterns and the other discovers only maximal frequent patterns. Our approach is applicable to location-based services such as tourist service, traffic service, and so on. [ABSTRACT FROM AUTHOR]

We propose a framework of the context awareness system which processes a large amount of sensor data from the application areas. The proposed framework consists of a context acquisition, a knowledge base, a rule manager, and a context information manager, etc. we implement the proposed framework of in-situ sensor data processing system that manages the data transmitted from various sensors and notifies the manager of the alarm message for specific conditions. Our proposed framework is able to be applied to the prevention of a forest fire, the warning system for detecting environmental pollution, etc. [ABSTRACT FROM AUTHOR]

In this paper, we propose an automatic generation method of network components for active network management based on SNMP. At most the components in the network have been managed manually. It wastes time and cost for network management program development. Thus, we propose an active program generator, which is called AG-NC, in order to solve these disadvantages. AG-NC consists of NE Basic Info Handler, MIB Handler, Template Handler, and Operation Handler. This can generate a network management program automatically using information that was provided along with SNMP library. Therefore, we can make the network structure expansion because the development time and cost of the network management program can be reduced dramatically through AG-NC. [ABSTRACT FROM AUTHOR]

We introduce a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes as input a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a simple text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Naïve Bayes Model and SVM, and for unsupervised, we tested Jaccard, TFIDF, Jaro and JaroWinkler. In our experiments, SVM and TFIDF outperformed the other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols. [ABSTRACT FROM AUTHOR]

We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for texture descriptors based on spatial distribution of textural features. Our approach is based on the observation that regional properties of textures are well captured by correlations among local texture patterns. The proposed method has been evaluated through experiments using real textures, and has shown significant improvements in recognition rates. [ABSTRACT FROM AUTHOR]

Selectivity estimation for spatial query is very important process in finding the most efficient execution plan. Many works have been performed to estimate accurate selectivity. However, the existing works require a large amount of memory to retain accurate selectivity, and these works can not get good results in little memory environments such as mobile-based small database. In order to solve this problem, we propose a new technique called MW histogram which is able to compress summary data and get reasonable results. The proposed method is based on the spatial partitioning algorithm of MinSkew histogram and wavelet transformation. The experimental results showed that the MW histogram has lower relative error than MinSkew histogram and gets a good selectivity in little memory. [ABSTRACT FROM AUTHOR]

A spatiotemporal database system manages data of geospatial objects whose geometry change over time and vagueness is their inherent nature. In this paper, we concentrate on the applications dealing with objects whose extents are indeterminate and change constantly. To support geographic information systems (GIS) in representing and handing such objects efficiently, we propose a fuzzy spatiotemporal data model called FSTDM based on fuzzy set theory. Also, we introduce algorithms for proceeding queries depending on whether the predicate is fuzzy spatial, temporal, or fuzzy spatiotemporal (FST). This work is applicable to many applications handling time-varying geospatial data, including global change (as in climate or land cover change), social (demographic, health, etc.) applications. [ABSTRACT FROM AUTHOR]

XML has recently become very popular for representing semi-structured data and a standard for data exchange over the web because of its varied applicability in a number of applications. Therefore, XML documents form an important data mining domain. In this paper, we propose a new XML document clustering technique using sequential pattern mining algorithm. Our approach first extracts the representative structures of frequent patterns from schemaless XML documents by using a sequential pattern mining algorithm. And then, unlike most previous document clustering methods, we apply clustering algorithm for transactional data without a measure of pairwise similarity, considering that an XML document as a transaction and the extracted frequent structures of documents as the items of the transaction. We have experimented our clustering algorithm by comparing it with the previous methods. The experimental results show the effectiveness of the proposed method in performance and in producing clusters with higher cluster cohesion. [ABSTRACT FROM AUTHOR]

Nowadays, with a great number of mobile (moving) objects using wireless communication or GPS technology, monitoring systems having the capability of handling anticipated future queries of the mobile objects are literally important. To cater for these applications, the TPR-tree indexing method, which is able to support the future queries, is substantially feasible for the real world. The crucial idea of them is to make the bounding rectangle as a motion function of time. In this paper, we propose an extended method of the TPR-tree, named Horizon TPR-tree or HTPR-tree. The general idea that we make a new motion function of time is to reduce speed of growing sizes of MBR (minimum bounding rectangles). Smaller MBR alleviates overlap problem and increases the effect of query in R-tree based methods. During creating and updating index, motion function influences organizing operations, such as splitting and adjusting. Our experiments show that the HTPR-tree outperforms the TPR-tree for anticipated future queries. In addition, the idea of the proposed method is able to apply to not only the TPR-tree but also variants of the TPR-tree. The proposed method is applicable to vehicle-traffic network systems. [ABSTRACT FROM AUTHOR]

XML becomes increasingly important in data exchange and information management. Starting point for retrieving the information and integrating the documents efficiently is clustering the documents that have similar structure. Thus, in this paper, we propose a new XML document clustering method based on similar structure. Our approach first extracts the representative structures of XML documents by sequential pattern mining. And then we cluster XML documents of similar structure using the clustering algorithm for transactional data, assuming that an XML document as a transaction and the frequent structure of documents as the items of the transaction. We also apply our technique to XML retrieval. Our experiments show the efficiency and good performance of the proposed clustering method. Keywords: Document Clustering, XML Document, Sequential Pattern, Structural Similarity, Structural Retrieval [ABSTRACT FROM AUTHOR]

Data mining applications over on-body sensor data have earned great attention in recent years. We propose a novel Online Multi-divisive Hierarchical Clustering Method on on-body sensor data. Our method evolves tree-like top down hierarchy cluster, which splits and agglomerates clusters as needed. Experimental results prove a competing quality for our method over existing ones. [ABSTRACT FROM AUTHOR]

Queries that return a list of frequently occurring items are important in the analysis of real-time Internet packet streams. While several results exist for computing Top-k queries using limited memory in the infinite stream model (e.g., limited-memory sliding windows). To compute the statistics over a sliding window, a synopsis data structure can be maintained for the stream to compute the statistics rapidly. Usually, a Top-k query is always processed over an equal synopsis, but it΄s very hard to implement over an unequal synopsis because of the resulting inaccurate approximate answers. Therefore, in this paper, we focus on periodically refreshed Top-k queries over sliding windows on Internet traffic streams; we present a deterministic DSW (Dynamic Sub-Window) algorithm to support the processing of Top-k aggregate queries over an unequal synopsis and guarantee the accuracy of the approximation results. [ABSTRACT FROM AUTHOR]

With the advances in information technology, the amount of multimedia metadata captured, produced, and stored is increasing rapidly. As a consequence, multimedia content is widely used for many applications in today΄s world, and hence, a need for organizing multimedia metadata and accessing it from repositories with vast amount of information has been a driving stimulus both commercially and academically. MPEG-7 is expected to provide standardized description schemes for concise and unambiguous content description of data/documents of complex multimedia types. Meanwhile, other metadata or description schemes, such as Dublin Core, XML, TV-Anytime etc., are becoming popular in different application domains. In this paper, we present a new prototype Multimedia Metadata Management System. Our system is good at sharing the integration of multimedia metadata from heterogeneous sources. This system enables the collection, analysis and integration of multimedia metadata semantic description from some different kinds of services. (UCC, IPTV, VOD and Digital TV et al.) [ABSTRACT FROM AUTHOR]

In this paper, we propose a system architecture for handling and storing sensor data stream in real-time to support the spatial and/or temporal queries besides continuous queries. We exploit a segment-based method to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results. In addition, we offer a method to reduce the cost of join operations in processing spatiotemporal queries by filtering out the list of irrelevant sensors from the query range before making the join operation. We then present a design of the system architecture for processing spatial and/or temporal queries. Finally, we implement a climate monitoring application system. [ABSTRACT FROM AUTHOR]

LBS (Location-based services) are rapidly emerging as a prominent area of deployment of geographic data management technologies. One of the research challenges posed by LBS is to support location management and spatial data mining on vehicle movement. In this paper, we introduce a method that investigates how to deduce future locations of mobile users. The idea is to employ the movement patterns of users in the past. All frequent patterns are enumerated, which are applied to produce movement rules, which are in turn employed to predict future location of moving object. Our proposed algorithms are quite efficient to enable push-driven LBS applications. [ABSTRACT FROM AUTHOR]