Marzanah A. Jabar

A mapping study to investigate component-based software system metrics

A component-based software system (CBSS) is a software system that is developed by integrating components that have been deployed independently. In the last few years, many researchers have proposed metrics to evaluate CBSS attributes. However, the practical use of these metrics can be difficult. For example, some of the metrics have concepts that either overlap or are not well defined, which could hinder their implementation. The aim of this study is to understand, classify and analyze existing research in component-based metrics, focusing on approaches and elements that are used to evaluate the quality of CBSS and its components from a component consumers point of view. This paper presents a systematic mapping study of several metrics that were proposed to measure the quality of CBSS and its components. We found 17 proposals that could be applied to evaluate CBSSs, while 14 proposals could be applied to evaluate individual components in isolation. Various elements of the software components that were measured are reviewed and discussed. Only a few of the proposed metrics are soundly defined. The quality assessment of the primary studies detected many limitations and suggested guidelines for possibilities for improving and increasing the acceptance of metrics. However, it remains a challenge to characterize and evaluate a CBSS and its components quantitatively. For this reason, much effort must be made to achieve a better evaluation approach in the future.

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.

Software Quality Models: A Comparative Study

In last decade, researchers have often tried to improve the usability, portability, integrity and other aspects of software in order for it to be more users friendly and gain user trust. Several approaches and techniques have been proposed to reduce the negative effects of software size and complexity. Moreover, several software quality models were proposed to evaluate general and specific type of software products. These models were proposed to evaluate general or specific scopes of software products. The proposed models were developed based on comparisons between the well-known models, in order to customize the closed model to the intended scope. These comparisons are leak of criteria that is conducted based on different perspectives and understanding. Therefore, a formal method of comparison between software quality models is proposed. The proposed method is applied on a comprehensive comparison between well-known software quality models. The result of the proposed method shows the strength and weaknesses of those models.

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.

A threshold-based similarity measure for duplicate detection

In order to extract beneficial information and recognize a particular pattern from huge data stored in different databases with different formats, data integration is essential. However the problem that arises here is that data integration may lead to duplication. In other words, due to the availability of data in different formats, there might be some records which refer to the same entity. Duplicate detection or record linkage is a technique which is used to detect and match duplicate records which are generated in data integration process. Most approaches concentrated on string similarity measures for comparing records. However, they fail to identify records which share the semantic information. So, in this study, a threshold-based method which takes into account both string and semantic similarity measures for comparing record pairs. This method is experimented on a real world dataset, namely Restaurant and its effectiveness is measured based on several standard evaluation metrics. As experimental results indicate, the proposed similarity method which is based on the combination of string and semantic similarity measures outperforms the individual similarity measures with the F-measure of 99.1% in Restaurant dataset. Therefore, based on experimental results, besides string similarity, semantic similarity should be considered in order to detect duplicate records more effectively.

An Evaluation Model for Software Reuse Processes

Software reuse is a major concern in many software development companies. It is one of the main strategies used to reduce the cost of software product development. Studies show that the reuse strategy is the most significant strategy in terms of effort and quality. That it could save the half of the software development effort and increase the quality of the software product. Different ways of software reuse are proposed and discussed. In this study, an evaluation model for software reuse is proposed. The model is developed in order to consider the new methods of software reuse. That developed based on the framework of develop a reusable software components through software development processes. The model is proposed in order to present the applicable methods of software reuse and to evaluate their cost.

The impact of data quality dimensions on business process improvement

Guaranteeing high data quality level is an important issue to increase the efficiency of the business processes. In fact, poor data quality produces wrong information, which leads to the failure of the business process improvement. Identifying data quality problems has positive impact on overall effectiveness and efficiency of the process improvement. In fact, improving data quality often requires modifying business process enriching them with the most improvement activities. Such activities depend and change based on the data quality dimensions. In this paper, we focus on review of the impact of data quality dimensions on business process improvement in order to support managers to facilitate the implementation of process improvement. The evaluations of this research will use to refine and extend knowledge of relationship between data quality dimensions and business process improvement.

A novel watermarking technique in data transmission between QR codes and database

Digital data transmission has become prominent in this era of communication. Digital data such as text, still images, movies, or sounds can be wirelessly distributed through internet, intranet or digital broadcasting. A recent data management technology known as QR Code (Quick Response Code) allows data or information to be communicated wirelessly using a common portable device installed with its scanning software. Even though this new data management technology is easy to handle, it is also easy to be duplicated by unauthorized parties. Besides authentication and encryption techniques for security, using watermark will be an advantage to provide better protections for wireless data transmissions. This paper describes how these protections are being applied in ensuring data transmission for QR Codes safe from intruders without affecting the quality of data transmitted. A different technique of applying watermark is proposed in this paper. In this technique, due to its unique information, data of authentication is used for the watermark. The technique is also supported with the use of binary numbers for the encryption. To confirm the effectiveness of this protection technique, percentage of data loss and processing time for data transmissions are measured. From the experiment, results show that there is no data loss but only a slight delay in processing time is observed. This shows that the proposed technique of security protection is effective with only a negligible amount of time delay.

Data quality comparative model for data warehouse

The growth of daily data and complexity in data warehouse with enhanced the information technology has created new challenges for information user. The demand for quality data has increase an awareness of the quality, reliable and accuracy of information in making fast and reliable decision-making. Nowadays, many organizations are depending on their resources in data warehouse. As that matter of fact, the qualities of data warehouse are greatly concern. The poor and error data will cause more trouble in data warehouse as data accessed from the same resources by the user. Here we present the systematic review comparative model to determine the data quality model as further research in our studies.

Framework for Interrogative Knowledge Identification

The difficulty of defining and capitalizing the knowledge in an organization from the business data captured in text files. These text files defined as unstructured document that is without a specific format example, plain text. Hence, this paper presents an Interrogative Knowledge Identification framework to identify unstructured documents that encompassed knowledge, information, and data. It tries to identify some high-level problems of the area from a higher perspective and then propose a possible solution thru the description of the framework. This research is an experimental approach using an appropriate test collection of unstructured documents. A system was developed based on the Interrogative Knowledge Identification framework. The results obtained are measured in terms of percentage of quantitative retrieval performance recall and precision metrics compared with an expert. This is to improve better understanding the process of making sense the information or knowledge residing in unstructured documents.