Ali Mamat

An efficient replicated data access approach for large-scale distributed systems

In data-intensive distributed systems, replication is the most widely used approach to offer high data availability, low bandwidth consumption, increased fault-tolerance and improved scalability of the overall system. Replication-based systems implement replica control protocols that enforce a specified semantics of accessing the data. Also, the performance depends on a host of factors chief of which is the protocol used to maintain consistency among object replica. In this paper, we propose a new low-cost and high data availability protocol for maintaining replicated data on networked distributed computing systems. We show that the proposed approach provides high data availability, low bandwidth consumption, increased fault-tolerance and improved scalability of the overall system as compared to standard replica control protocols.

Association rules on significant rare data using second support

Association rule is one of the data mining techniques involved in discovering information that represents the association among data. Data in the database sometimes appear infrequent but highly associated with a specific data. This paper proposes a technique for significant rare data by introducing second support in discovering the association rules of such data. We show that the proposed approach provides better performance as compared to standard association rules techniques.

OPWUMP: An Architecture for Online Predicting in WUM-Based Personalization System

The Internet is one of the fastest growing areas of intelligence gathering. During their navigation web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Sophisticated mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one web usage mining application. However, the accuracy of the prediction and classification in the current architecture of predicting users’ future requests systems can not still satisfy users especially in Huge Web sites. To provide online prediction efficiently, we develop an architecture for online predicting in WUM-based personalization system (OPWUMP).This article advances an architecture of Web usage mining for enhancing accuracy of classification by interaction between classification, evaluation, current user activates and user profile in online phase of this architecture.

A new clustering approach based on graph partitioning for navigation patterns mining

We present a study of the Web based user navigation patterns mining and propose a novel approach for clustering of user navigation patterns. The approach is based on the graph partitioning for modeling user navigation patterns. For the clustering of user navigation patterns we create an undirected graph based on connectivity between each pair of Web pages and we propose novel formula for assigning weights to edges in such a graph. The experimental results represent that the approach can improve the quality of clustering for user navigation pattern in Web usage mining systems. These results can be use for predicting userpsilas next request in the huge Web sites.

A new classification model for online predicting users’ future movements

Nowadays many internet users prefer to navigate their interest web pages in special web site rather than navigating all web pages in the web site. For this reason some techniques have been developed for predicting user’s future requests. Data manning algorithms can be applied to many prediction problems. We can exploit Web Usage Mining for Knowledge extracting based on user behavior during the web navigation. The WUM applies data mining techniques for extracting knowledge from user log files in the particular web server. The WUM can model user behavior and, therefore, to forecast their future movements by mining user navigation patterns. To provide online prediction efficiently, we advance architecture for online predicting in web usage mining system by proposing novel model based on Longest Common Subsequence algorithm for classifying user navigation patterns. The prediction of users’ future movements by this manner can improve accuracy of recommendations.

Minimum Completion Time for Power-Aware Scheduling in Cloud Computing

Reducing power consumption has been an essential requirement for Cloud resource providers not only to decrease operating costs, but also to improve the system reliability. This paper tackles the problem of minimizing power consuming in data centers hosts and improving their load balancing simultaneously. Cloud computing based on the idea of offering services going to be executed on data centers. These data centers need huge amount of power if they are in the peak load or the tasks are not distributed efficiently in their machines. The paper presents an algorithm for task scheduling that lowering the power consuming and reducing the total data center load. An empirical study has been done to simulate the proposed algorithm, which is proved by the results.

An encoding scheme based on fractional number for querying and updating XML data

In order to facilitate the XML query processing, several labeling schemes have been proposed to directly determine the structural relationships between two arbitrary XML nodes without accessing the original XML documents. However, the existing XML labeling schemes have to re-label the pre-existing nodes or re-calculate the label values when a new node is inserted into the XML document during an update process. In this paper, we devise a novel encoding scheme based on the fractional number to encode the labels of the XML nodes. Moreover, we propose a mapping method to convert our proposed fractional number based encoding scheme to bit string based encoding scheme with the intention to minimize the label size and save the storage space. By applying our proposed bit string encoding scheme to the range-based labeling scheme and the prefix labeling scheme, the process of re-labeling the pre-existing nodes can be avoided when nodes are inserted as leaf nodes and sibling nodes without affecting the order of XML nodes. In addition, we propose an algorithm to control the increment of label size when new nodes are inserted frequently at a fix place of an XML tree. Experimental results show that our proposed bit string encoding scheme provides efficient support to the process of XML updating without sacrificing the query performance when it is applied to the range-based labeling schemes.

A new method for mining maximal frequent itemsets

In this paper, we propose a new method for mining maximal frequent itemsets. Our method introduces an efficient database encoding technique, a novel tree structure called PC_Tree and also PC_Miner algorithm. The database encoding technique utilizes Prime number characteristics and transforms each transaction into a positive integer that has all properties of its items. The PC_Tree is a simple tree structure but yet powerful to capture whole of transactions by one database scan. The PC_Miner algorithm traverses the PC_Tree and builds the gcd (greatest common divisor) set of its nodes to mine maximal frequent itemsets. Experiments verify the efficiency and advantages of the proposed method.

Multi-Check-Out Timestamp Order Technique (MCTO) for Planned Disconnections in Mobile Database

Tremendous advances in wireless networks and portable computing devices have led to development of mobile computing. Traditional transaction management in multidatabase systems cannot be applied to mobile environment. In this paper, we analyze the features of mobile disconnection modes. A mobile transaction model on multi-checkout timestamp order technique (MCTO) is proposed and a serializability theory is presented for this model

SwSim: Discovering semantic similarity association in semantic web

Similarity is an important and fundamental concept in AI and many other fields. In different applications, users need to discover the relations between objects and find the level of semantic similarity between them. (I.e. find two similar papers or two similar events). In order to answer these types of complex queries, discovering semantic similarity association is one of the important steps.