Fatimah Sidi

Intrusion detection using data mining techniques

As the network dramatically extended, security considered as major issue in networks. Internet attacks are increasing, and there have been various attack methods, consequently. Intrusion detection systems have been used along with the data mining techniques to detect intrusions. In this work we aim to use data mining techniques including classification tree and support vector machines for intrusion detection. As results indicate, C4.5 algorithm is better than SVM in detecting network intrusions and false alarm rate in KDD CUP 99 dataset.

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.

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.

Processing skyline queries in incomplete distributed databases

Due to its great benefits over many database applications, skyline queries have received formidable concern in the last decades. Skyline queries attempt to assist users by identifying the set of data items which represents the best results that meet the conditions of a given query. Most of the existing skyline techniques concentrate on identifying skylines over a single relation. However, in distributed databases, the process of skyline queries required accessing multiple relations which might be located at different sites. Consequently, data items from these multiple relations need to be joined and thus transferring these data items from one site to another is unavoidable. Moreover, the previous techniques also assume that the values of dimensions for every data item are presented (complete) which is not always true as some values may be missing. In this paper, we proposed an approach for processing skyline queries in incomplete distributed databases. The approach derives skylines from multiple relations where dominated data items are removed before joining the relations to reduce the processing time and the network cost. The experimental results illustrate that our proposed approach outperforms the previous approaches in terms of processing time and network cost.

Mining Students' Characteristics and Effects on University Preference Choice: A Case Study of Applied Marketing in Higher Education

University servers and databases store a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Mining such data offers a huge potential in advancing the educational field in the country because data mining is able to extract important models and hidden patterns beneath the data, which will help in decision-making to improve the outcome of educational establishments. This work concerns with data related to students. Understanding the characteristics of students enrolled in the university is important as it helps the university or institution to strategize on marketing their education programmes. This paper analyzes the student characteristics of Universiti Putra Malaysia based on their preference choice during registration at the university. The experiments are carried out using the Oracle Data Miner software and the results are analyzed and discussed.

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 framework for evaluating skyline query over uncertain autonomous databases

The perception of skyline query is to find a set of objects that is much preferred in all dimensions. While this theory is easily applicable on certain and complete database, however, when it comes to data integration of databases where each has different representation of data in a same dimension, it would be difficult to determine the dominance relation between the underlying data. In this paper, we propose a framework, SkyQUD, to efficiently compute the skyline probability of datasets in uncertain dimensions. We explore the effects of having datasets with uncertain dimensions in relation to the dominance relation theory and propose a framework that is able to support skyline queries on this type of datasets.

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.