Browsing by Author "El-Bastawissy A.H."

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    Record linkage approaches in big data: A state of art study
    (Institute of Electrical and Electronics Engineers Inc., 2018) El-Ghafar R.M.A.; Gheith M.H.; El-Bastawissy A.H.; Nasr E.S.; Computer Science Department; Institute of Statistical Studies and Research; Cairo University; Cairo; Egypt; Faculty of Computer Science; Modern Sciences and Arts University; Cairo; Egypt; Independent Researcher; Cairo; Egypt
    Record Linkage aims to find records in a dataset that represent the same real-world entity across many different data sources. It is a crucial task for data quality. With the evolution of Big Data, new difficulties appeared to deal mainly with the 5Vs of Big Data properties; i.e. Volume, Variety, Velocity, Value, and Veracity. Therefore Record Linkage in Big Data is more challenging. This paper investigates ways to apply Record Linkage algorithms that handle the Volume property of Big Data. Our investigation revealed four major issues. First, the techniques used to resolve the Volume property of Big Data mainly depend on partitioning the data into a number of blocks. The processing of those blocks is parallelly distributed among many executers. Second, MapReduce is the most famous programming model that is designed for parallel processing of Big Data. Third, a blocking key is usually used for partitioning the big dataset into smaller blocks; it is often created by the concatenation of the prefixes of chosen attributes. Partitioning using a blocking key may lead to unbalancing blocks, which is known as data skew, where data is not evenly distributed among blocks. An uneven distribution of data degrades the performance of the overall execution of the MapReduce model. Fourth, to the best of our knowledge, a small number of studies has been done so far to balance the load between data blocks in a MapReduce framework. Hence more work should be dedicated to balancing the load between the distributed blocks. � 2017 IEEE.
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    Towards a Composite index for measuring the higher education institutions in Egypt
    (Institute of Electrical and Electronics Engineers Inc., 2014) El-Hefnawy M.R.M.; El-Bastawissy A.H.; Kadry M.A.; Computer Engineering Department; Arab Academy for Science and Technology; Cairo; Egypt; Computer Science Department; Modern Sciences and Arts University; Cairo; Egypt
    Egypt has the largest and most significant higher education system in the Middle East and North Africa but it had been continuously facing serious and accumulated challenges. The gap between what is existing and what is supposed to be for the self-regulation and improvement processes is not entirely clear to face these challenges. The effective use of information technology in higher education requires good and new techniques as well as rational strategies. The reform of higher education through strategies based on data analysis of current situation will affect the overall performance of transitional state and will shape new paradigms in higher education system development in Egypt. This research has objective to develop a model of Composite index (CI) based on a set of key performance indicators (KPI) commensurate with the nature of higher education institutions in Egypt. The outcomes of the composite index aim to measure overall performance of institutions and provide unified ranking method in this context. KPIs are determined as description of key success factors related to institutions sustainability. These KPIs are classified into main areas and sub-indicators. Within this scope, the indicators were weighted via Analytic Hierarchy Process (AHP) method according to their significance levels. Pairwise comparison survey template and database web application were developed to collect narrative responses, apply algorithm and extract results. The research study was conducted with 40 professors from 19 renowned universities in Egypt as education experts. The status of composite index model implementation is discussed from theoretical and technical perspectives. � 2014 The Science and Information (SAI) Organization.