Hamidah Ibrahim

New method for scheduling heterogeneous multi-installment systems

Since the past decade, the use of parallel and distributed systems has become more common. In these systems, a huge chunk of data or computations is distributed among many systems in order to obtain better performance. Dividing data is one of the challenges in this type of systems. Divisible Load Theory (DLT) is a proposed method for scheduling data distribution in parallel or distributed systems. Many studies have been done in this field but only a few articles about distributing data in a heterogeneous multi-installment system can be found. In this paper, we present some closed-form formulas for the different steps of scheduling jobs in a heterogeneous multi-installment system (finding the proper number of processors, the proper number of installments, closed-form formula for scheduling internal installments and closed-form formula for scheduling last installment). Two different systems are studied: Computation-Based Systems and Communication-Based Systems. The results of our experiments show that both methods gave better performances than the previous methods (Hsu et al.s method and Beaumont et al.s method) and the Communication-Based method has a smaller response time than the Computation-Based method.

Data quality: A survey of data quality dimensions

Nowadays, activities and decisions making in an organization is based on data and information obtained from data analysis, which provides various services for constructing reliable and accurate process. As data are significant resources in all organizations the quality of data is critical for managers and operating processes to identify related performance issues. Moreover, high quality data can increase opportunity for achieving top services in an organization. However, identifying various aspects of data quality from definition, dimensions, types, strategies, techniques are essential to equip methods and processes for improving data. This paper focuses on systematic review of data quality dimensions in order to use at proposed framework which combining data mining and statistical techniques to measure dependencies among dimensions and illustrate how extracting knowledge can increase process quality.

Checking Integrity Constraints - How it Differs in Centralized, Distributed and Parallel Databases

An important aim of a database system is to guarantee database consistency, which means that the data contained in a database is both accurate and valid. Integrity constraints represent knowledge about data with which a database must be consistent. The process of checking constraints to ensure that update operations or transactions which alter the database will preserve its consistency has proved to be extremely difficult to implement, particularly in distributed and parallel databases. In distributed databases the aim of the constraint checking is to reduce the amount of data needing to be accessed, the number of sites involved and the amount of data transferred across the network. In parallel databases the focus is on the total execution time taken in checking the constraints. This paper highlights the differences between centralized, distributed and parallel databases with respect to constraint checking

A review: accuracy optimization in clustering ensembles using genetic algorithms

The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research.

Adaptive Divisible Load Model for Scheduling Data-Intensive Grid Applications

In many data grid applications, data can be decomposed into multiple independent sub datasets and schedule for parallel execution and analysis. Divisible Load Theory (DLT) is a powerful tool for modelling data-intensive grid problems where both communication and computation load is partitionable. This paper presents an Adaptive DLT (ADLT) model for scheduling data-intensive grid applications. This model reduces the expected processing time approximately 80% for communication intensive applications and 60% for computation intensive applications compared to the previous DLT model. Experimental results show that this model can balance the loads efficiently.

A strategy for semantic integrity checking in distributed databases

Integrity constraints represent knowledge about data with which a database must be consistent. The process of checking constraints to ensure that the update operations or transactions which alter the database will preserve its consistency has proved to be extremely difficult to implement efficiently, particularly in a distributed environment. In the literature, most of the approaches/methods proposed for finding/deriving a good set of integrity constraints concentrate on deriving simplified forms of the constraints by analyzing both the syntax of the constraints and their appropriate update operations. These methods are based on syntactic criteria and are limited to simple types of integrity constraints. Also, these methods are only able to produce one integrity test for each integrity constraint. In Ibrahim, Gray, and Fiddian (1997), we introduced an integrity constraint subsystem for a relational distributed database. The subsystem consists of several techniques necessary for efficient constraint checking, particularly in a distributed environment where data distribution is transparent to application domain. However, the technique proposed for generating integrity tests is limited to several types of integrity constraints, namely: domain, key, referential and simple general semantic constraint and only produced two integrity tests (global and local) for a given integrity constraint. In this paper, we present a technique for deriving several integrity tests for a given integrity constraint where the following types of integrity constraints are considered: static and transition constraints.

Shortcoming, problems and analytical comparison for flooding-based search techniques in unstructured P2P networks

Peer-to-Peer networks attracted a significant amount of interest because of their capacity for resource sharing and content distribution. Content distribution applications allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content. Searching in unstructured P2P networks is an important problem, which has received considerable research attention. Acceptable searching techniques must provide large coverage rate, low traffic load, and optimum latency. This paper reviews flooding-based search techniques in unstructured P2P networks. It then analytically compares their coverage rate, and traffic overloads. Our simulation experiments have validated analytical results.

Optimal workload allocation model for scheduling divisible data grid applications

In many data grid applications, data can be decomposed into multiple independent sub-datasets and distributed for parallel execution and analysis. This property has been successfully employed using Divisible Load Theory (DLT), which has been proved a powerful tool for modeling divisible load problems in data-intensive grids. There are some scheduling models that have been studied but no optimal solution has been reached due to the heterogeneity of the grids. This paper proposes a new model called the Iterative DLT (IDLT) for scheduling divisible data grid applications. Recursive numerical closed form solutions are derived to find the optimal workload assigned to the processing nodes. Experimental results show that the proposed IDLT model leads to a better solution than other models (almost optimal) in terms of makespan.

A2DLT: Divisible Load Balancing Model for Scheduling Communication-Intensive Grid Applications

Scheduling an application in data grid is significantly complex and very challenging because of its heterogeneous in nature of the grid system. Divisible Load Theory (DLT) is a powerful model for modelling data-intensive grid problem where both communication and computation loads are partitionable. This paper presents a new divisible load balancing model known as adaptive ADLT (A2DLT) for scheduling the communication intensive grid applications. This model reduces the maximum completion time (makespan) as compared to the ADLT and Constraint DLT (CDLT) models. Experimental results showed that the model can balance the load efficiently, especially when the communication-intensive applications are considered.

An Investigation into Methods and Concepts of Qualitative Research in Information System Research

This paper is an initial review of literature, investigating qualitative research, to show its relevance in information system disciplines. Qualitative research involves the use of qualitative data, such as interviews, documents, and participant observation data, to understand and explain social phenomena. Qualitative research can be found in many disciplines and fields, using a variety of approaches, methods and techniques. In Information Systems (IS), there has been a general shift in information system research away from technological to managerial and organizational issues, hence an increasing interest in the application of qualitative research methods. Frequently used methods are the action research, case study, ethnography and grounded theory. Review of each research approaches in qualitative methods, will be discussed. Important considerations in the methods are identified, and cases for each research method are described. Then we will present some benefits and limitations of each method. Based on the result, a framework of an action research was proposed and might be useful in starting a research project in information system using qualitative method.