This study investigates the mobile phone data during ephemeral event (Armada). The statistical techniques have been used for modeling human mobility collectively and individually. The undertaken substantial parameters are: inter-event times, travel distances (displacements), and radius of gyration. They have been analyzed and simulated using computing platform by integrating various applications for huge database management, visualization, analysis, and simulation. Accordingly, the general population pattern law has been extracted. This study has revealed the individuals mobility in dynamic perspective for 615,712 mobile users, also the simulated observed data are classified according to general, work, and off days.

This work focuses on the use of multi-criteria decision-making method AHP for using in educational and vocational guidance. Analytical Hierarchy Process (AHP), proposed by the mathematician Thomas Saaty in 1980, is a method of analysis greatly used in the context of a multi-criteria analysis; it allows the comparison and the choice between the preset options. To achieve this goal, a vital work, preceded the use of the AHP method, which consists in doing a prototyping of trades according to the guidance criteria and sub-criteria. The IT system based on this method allows the student to find, firstly, the activities' sectors which are the most appropriate to his/her profile, to choose subsequently the trades and finally, to identify, the potential training paths.

The skills for big data technology provide a window of new job opportunities for the information technology (IT) professionals in the emerging data science landscape. Consequently, the objective of this paper is to introduce the research results of suitable skills required to work with big data technology. Such skills include Document Stored Database; Key-value Stored Database; Column-oriented Database; Object-oriented Database; Graph Database; MapReduce; Hadoop Distributed File System (HDFS); YARN Framework; Zookeeper; Oozie; Hive; Pig; HBase; Mahout; Sqoop; Spark; Flume; Drill; Programming Languages; IBM Watson Analytics; Statistical Tools; SQL; Project Management; Program Management; and Portfolio Management. This paper is part of an ongoing research that addresses the link between economic growth and big data.

This paper focuses on the enhanced initial centroids for the K-means algorithm. The original k-means is using the random choice of initial seeds which is a major limitation of the original K-means algorithm because it produces less reliable result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initialization. This paper shows the comparison between K-Means and the enhanced K-Means algorithm, and it proves that the new method of selecting initial seeds is better in terms of mathematical computation and reliability.

An Airline Reservation system is very important because it has the strong ability to reduce errors that might have occurred when using a manual system of reservation and helps speed up the boarding process. Overland Airways has an existing Airline Reservation System, but this paper analyzed the problems of the existing system. The problems are: inability of passengers to select their preferred seat(s) from the reservation system, No option of passengers printing their boarding pass from the existing system, non-notification of passengers of flight cancellation or delays and passengers don’t have access to aircraft maintenance report to ease the fears associated with air travel and its disasters. In this paper, an Improved Airline Reservation System that is convenient for passengers to solve the aforementioned problems was designed. The Improved Airline Reservation system is designed and implemented using data obtained from interviewing airline personnel, passengers, and materials on Airline Reservation Systems. In this regard, the Improved Airline Reservation System will assist Overland Airways in variety of airline administration tasks and service needs from time of initial reservation through completion of the task. The following programming languages were used: PHP, JavaScript, HTML and CSS for designing the interface of the system, and SQL for the database. The designed airline system was tested with 50 passengers.

Refactoring is used to improve deteriorated software design, code and their maintainability. In object-oriented (OO) code, before refactoring is performed, its opportunities must be identified and several approaches exist this regard. Among the approaches is the software metric-based approach where quality software metrics are used. Therefore, this paper provide analysis of existing empirical studies that utilized software metrics to identify refactoring opportunities in OO software systems. We performed a comprehensive analysis on 16 studies to identify the state-of-the-practice. The focal point was on the workings, refactoring activities, the programming language and the impact on software quality. The results obtained shows approaches were not unique, each was designed either for a single refactoring activity or couple of them, move method and extract class dominated the refactorings activities, and most approaches were fully automated while few were semi-automated. Moreover, OO metrics played acritical role in both opportunities detection and factoring decisions. Based on the results, it would be beneficial if generic refactoring approach is developed that is capable of identifying needs for all refactoring activities.

Association rule mining aims to determine the relations among sets of items in transaction database and data repositories. It generates informative patterns from large databases. Apriori algorithm is a very popular algorithm in data mining for defining the relationships among itemsets. It generates 1, 2, 3,…, n-item candidate sets. Besides, it performs many scans on transactions to find the frequencies of itemsets which determine 1, 2, 3,…, n-item frequent sets. This paper aims to eradicate the generation of candidate itemsets so as to minimize the processing time, memory and the number of scans on the database. Since only those itemsets which occur in a transaction play a vital role in determining frequent itemset, the methodology that is proposed in this paper is extracting only single itemsets from each transaction, then 2,3,..., n itemsets are generated from them and their corresponding frequencies are also calculated. Further, each transaction is scanned only once and no candidate itemsets is generated both resulting in minimizing the memory space for storing the scanned itemsets and minimizing the processing time too. Based on the generated itemsets, association rules are generated using minimum support and confidence.

Cloud computing systems provide virtualized resources that can be provisioned on demand basis. Enormous number of cloud providers are offering diverse number of services. The performance of these services is a critical factor for clients to determine the cloud provider that they will choose. However, determining a provider with efficient and effective services is a challenging task. There is a need for an efficient model that help clients to select the best provider based on the performance attributes and measurements. Cloud service ranking is a standard method used to perform this task. It is the process of arranging and classifying several cloud services within the cloud, then compute the relative ranking values of them based on the quality of service required by clients and the features of the cloud services. The objective of this study is to propose an enhanced performance based ranking model to help users choose the best service they need. The proposed model combines the attributes and measurements from cloud computing field and the well-defined and established software engineering field. SMICloud Toolkit has been used to test the applicability of the proposed model. The experimentation results of the proposed model were promising.