Earticle

A novel technique for updating in XML using semantic constraints is proposed. In the proposed technique for XML updating, we consider close value pair semantics in ordered XML documents. Further, how semantic constraints can be incorporated with integrity constraints for XML updat-ing and how to update XML views using semantic constraints are discussed.

Performance of web query processing becomes slow caused by increasing number of data. In this paper, intelligent algorithm was created to fix this problem. Four main components involved in this intelligent algorithm are assigning initial query, exploit query, assign to any possible query and query matching. Firstly, web user is needed to enter a keyword then this keyword automatically will assign as an initial query. After that, this initial query must be exploited before assigning process to any possible queries. These possible queries will match to existing query form temporary file that contains data schema and map to only select data sources. In this methodology, XML will use for mapping to heterogeneous databases. XML is really effective for mapping to heterogeneous databases. This methodology has been implemented in a prototype and applied to web queries. Another issue in with web query is difficult to search for information that best reflects the user’s need information. According this problem, intelligent algorithm was created and tested by developing simple application based on system architecture. This intelligent algorithm that was good for fixed problems by improving web query processing for heterogeneous databases. The intelligent algorithm was implemented and tested for heterogeneous database environment.

There are many algorithms developed for improvement the time of mining frequent itemsets (FI) or frequent closed itemsets (FCI). However, the algorithms which deal with the time of generating association rules were not put in deep research. In reality, in case of a database containing many FI/FCI (from ten thousands up to millions), the time of generating association rules is much larger than that of mining FI/FCI. Therefore, this paper presents an application of frequent closed itemsets lattice (FCIL) for mining minimal non-redundant association rules (MNAR) to reduce a lot of time for generating rules. Firstly, we use CHARM-L for building FCIL. After that, based on FCIL, an algorithm for fast generating MNAR will be proposed. Experimental results show that the proposed algorithm is much faster than frequent itemsets lattice-based algorithm in the mining time.

The data warehouse (DW) technology was developed to integrate heterogeneous information sources for analysis purposes. Information sources are more and more autonomous and they often change their content due to perpetual transactions (data changes) and may change their structure due to continual users' requirements evolving (schema changes). Handling properly all type of changes is a must. In fact, the DW which is considered as the core component of the modern decision support systems has to be update according to different type of evolution of information sources to reflect the real world subject to analysis. . The goal of this paper is to propose a solution, based on versioning approach, able to handle structure changes in order to keep track of the DW evolution.

Range-aggregate queries are popular in many applications in data warehouse environments with large business relational databases. To evaluate these efficiently, several studies on data cubes (such as the aggregate cubetree) have been carried out. In the well-known aggregate cubetree, each entry in every node stores the aggregate values of its corresponding subtree. Therefore, range-aggregate queries can be processed without visiting the child subtree whose nodes are all fully included in the query range. However, the aggregate cubetree does not consider range queries using partial dimensions and range queries without aggregation operations. Concretely, 1) a great deal of information that is irrelevant to the queries also has to be read from the disk for partial-dimensional range queries, and 2) while it improves the performance of range queries with aggregate operations, it degrades the performance of range queries without aggregate operations. As part of our research on this problem, previously we proposed an index structure, called the Aggregate-tree (denoted as Ag-tree), which does away with the above-mentioned weaknesses of the aggregate cubetree. Additionally in this paper, we make the Ag-tree more complete by sorting the entries in each of the nodes. The final index structure proposed in this study is called an Ag+-tree.

The standard back propagation algorithm for training artificial neural networks utilizes two terms, a learning rate and a momentum factor. The major limitations of this standard algorithm are the existence of temporary, local minima resulting from the saturation behaviour of the activation function, and the slow rates of convergence. Previous research demonstrated that in ‘feed forward’ algorithm, the slope of the activation function is directly influenced by a parameter referred to as ‘gain’. This research proposed an algorithm for improving the performance of the back propagation algorithm by introducing the adaptive gain of the activation function. The efficiency of the proposed algorithm is compared with conventional Gradient Descent Method and verified by means of simulation on four classification problems. In learning the patterns, the simulations result demonstrate that the proposed method converged faster on Wisconsin breast cancer and diabetes classification problem with an improvement ratio of nearly 2.8 and 1.2, 65% better on thyroid data sets and 97% success on IRIS classification problem. The results clearly show that the proposed algorithm significantly improves the learning speed of the conventional back-propagation algorithm.

In Web Server Cluster (WSC), replication techniques are necessary when providing accesses to data at different locations. In WSC, when designing the cluster architecture, the elements of availability and reliability for cases of data sharing or backing-up data are given the top priority. This is to ensure that the response time and throughput is still within the acceptable range. In ensuring the values of response time and throughput have not surpassed the limit, thus the replication technique must be employed intelligently. The replication technique that is been proposed is based on asynchronous replication technique. In doing so, we have managed to improve the reliability of the web cluster system. We have also managed to control the response time to a very nominal value but at the same time able to provide utmost throughput.

Ontology tends to change over time. Therefore, ontology change management such as ontology versioning is essential to keep ontology up to date. As one of the important phases in ontology versioning, change detection should be able to provide information about the changes in the database or metadata, which may need to be reflected as changes in the underlying ontological model. This paper proposes a bottom-up change detection method from a database or metadata perspective to preserve compatibility serialization between ontology and the associated database. In addition, with respect to ontology versioning tasks, the quality of detectable concept changes is measured using Precision, Recall and F-measure.

The tremendous development in information technology led to an explosion of data and motivated the need for powerful yet efficient strategies for data mining and knowledge discovery. Question Answering (QA) systems made it possible to ask questions and retrieve answers using natural language (NL) queries, rather than the keyword-based retrieval mechanisms used by current search engines. In Ontology-based QA system, the knowledge based data, where the answers are sought, has a structured organization. The question-answer retrieval of ontology knowledge base provides a convenient way to obtain knowledge for use, but the natural language need to be mapped to the query statement of ontology. QASYO is a sentence level question-answering system that integrates natural language processing, ontologies and information retrieval technologies in a unified framework. It accepts queries expressed in natural language and YAGO ontology as inputs and provides answers drawn from the available semantic markup. QASYO combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Semantic analysis of questions is performed in order to extract keywords used in the retrieval queries and to detect the expected answer type. In this paper we describe the current version of the system, in particular discussing its reasoning capabilities, and Performance.