Abdulrahman H. Altalhi

Big Data 2.0 Processing Systems: Taxonomy and Open Challenges

Data is key resource in the modern world. Big data has become a popular term which is used to describe the exponential growth and availability of data. In practice, the growing demand for large-scale data processing and data analysis applications spurred the development of novel solutions from both the industry and academia. For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum in both the research and industrial communities such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing platforms. In this article, we refer to this new wave of systems as Big Data 2.0 processing systems. To better understand the latest ongoing developments in the world of big data processing systems, we provide a taxonomy and detailed analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.

Comparative Study from Several Business Cases and Methodologies for ICT Project Evaluation

Achieving high competitive advantage through Information and Communication Technologies (ICT) has never been easy without proper management and appropriate utilization of ICT resources. Therefore, the statistics suggested that ICT project failures are very common in the organization due to several reasons; it fails to deliver the required objectives of investment, inaccurate budget planning, lack of risk management plan and time overrun are some basic reasons for an ICT project’s failure. To overcome these issues, recently ICT decision makers are emphasizing more on ICT project’s evaluation rather than investment. The practitioner broadly categorized the evaluation techniques in post and pre evaluation methods, which is further divided into measuring the return from financial and non-financial perspectives. The main purpose of this paper is to provide a comparative analysis on ICT investment’s evaluation, their categories based on pre and post evaluation. Thus, the paper offers an extensive literature review that can help ICT decision makers and organizations to better select the evaluation techniques available, where integration of multiple techniques can further improve this process

Image de-noising using noise ratio estimation, K-means clustering and non-local means-based estimator

One of the key issues in removing random-valued impulse noise from digital images using switching filters is the impulse noise detection. Impulse noise is a random, spiked variation in the brightness of the image. In this paper, a new impulse noise detection algorithm is presented that is based on Noise ratio Estimation and a combination of K-means clustering and Non-Local Means based filter (NEK-NLM). Luo-statistic is employed as a non-local means based estimator. The novelty of this work lies in the introduction of a pre-processing step of noise ratio estimation before noise detection, this estimation allows us to select suitable parameters for the noise detection algorithm. In noise filtering stage, nonlocal-means estimator is applied for restoring noisy pixels to their actual values. Using real world datasets, this paper shows that the impulse noise can be removed effectively. Extensive comparison of simulation results with the already published results show that the proposed method outperforms most of the existing impulse noise removal techniques both in terms of noise detection and image restoration.

Mobile cloud computing: An effective multimodal interface tool for students with dyslexia

Abstract The explosive growth of mobile technology and developments in cloud computing have provided new and powerful possibilities for mobile learning (m-learning). M-learning, which is an influential trend in the educational process, promotes learning accessibility and flexibility for a substantial and valuable segment of society: students with dyslexia. Although there is a substantial amount of research that discusses the use of m-learning by students with dyslexia, there are many gaps that must still be addressed. One substantial issue pertains to the lack of an effective, multimodal, human–computer user interface m-learning tool that combines different input and output modes based on each students learning style. In this paper, the authors develop a novel, interactive, multimodal interfaced, cloud-based m-learning tool with which students can naturally interact based on their preferred learning styles. This multimodal interface tool enhances the learning capabilities of students with dyslexia by almost 30% by customizing their multimodal functionality to meet their learning needs.

NOVEL COMPONENT-BASED DEVELOPMENT MODEL FOR SIP-BASED MOBILE APPLICATION

Universities and Institutions these days’ deals with issues related to with assessment of large number of students. Various evaluation methods have been adopted by examiners in different institutions to examining the ability of an individual, starting from manual means of using paper and pencil to electronic, from oral to written, practical to theoretical and many others. There is a need to expedite the process of examination in order to meet the increasing enrolment of students at the universities and institutes. Sip Based Mass Mobile Examination System ( SiBMMES ) expedites the examination process by automating various activities in an examination such as exam paper setting, Scheduling and allocating examination time and evaluation (auto-grading for objective questions) etc. SiBMMES uses the IP Multimedia Subsystem (IMS) that is an IP communications framework providing an environment for the rapid development of innovative and reusable services Session Initial Protocol (SIP) is a signalling (request-response ) protocol for this architecture and it is used for establishing sessions in an IP network, making it an ideal candidate for supporting terminal mobility in the IMS to deliver the services, with the extended services available in IMS like open APIs, common network services, Quality of Services (QoS) like multiple sessions per call, Push to Talk etc often requiring multiple types of media (including voice, video, pictures, and text). SiBMMES is an effective solution for mass education evaluation using mobile and web technology. In this paper, a novel hybrid component based development (CBD) model is proposed for SiBMMES. A Component based Hybrid Model is selected to the fact that IMS takes the concept of layered architecture one step further by defining a horizontal architecture where service enablers and common functions can be reused for multiple applications. This novel model tackle a new domain for IT professionals, its ideal to start developing services as a small increments using the component and then adding new functionalities and improvements over time. The proposed novel model will be highly customizable for any university who acquired to adopt similar IMS based mass examination system. This model will be compatible with the traditional question-answer style examination and could meet the requests of the mass examination, such as university entrance exam, International Computer Driving License exam, etc.

Developing a framework and algorithm for scalability to evaluate the performance and throughput of CRM systems

AbstractScalability in hardware and/or software is an important factor for enhancing the performance of running processes as well as the throughput of the system of business organizations. This paper explores the need for scalability and issues related to extending the resources in order to ensure an improved and scaled-up Customer Relationship Management (CRM) architecture. The main contribution discussed in this paper is the proposal of a conceptual framework for measuring the process performance and throughput of the system beyond the selection of the type of scalability. Furthermore, this paper concerns the CRM system, as customer requests, their online transactions, and responses need a fast and efficient system. Taking into consideration all these factors, ultimately this paper proposed a customer-friendly framework for measuring the process performance and throughput of the system. Finally, the proposed framework’s steps are shown in an algorithm calculating process performance and throughput of th...

A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

This paper proposes a nonlinear Goal Programming Model (GPM) for solving the problem of admission capacity planning in academic universities. Many factors of university admission capacity planning have been taken into consideration among which are number of admitted students in the past years, total population in the country, number of graduates from secondary schools, desired ratios of specific specialties, faculty-to-students ratio, and the past number of graduates. The proposed model is general and has been tested at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia, where the work aims to achieve the key objectives of a five-year development plan in addition to a 25-year future plan (AAFAQ) for universities education in the Kingdom. Based on the results of this test, the proposed GPM with a modified differential evolution algorithm has approved an ability to solve general admission capacity planning problem in terms of high quality, rapid convergence speed, efficiency, and robustness.

Improvement of students curricula in educational environments by means of online communities and social networks

The school is part of our society, which increasingly uses more and more the social networks. Therefore, we must continue this momentum, not only by using them, but by also guiding the students of its proper use. Thus, most educators working with high and middle level schools are aware of the involvement of young people in social networks, but few of these educators are prepared to deal with them the issue. Experts discuss the risks and benefits of such sites, and the role of schools, to offer a comprehensive approach that addresses the needs of online students. This paper seeks a solution to the complex world of social networking in education, where users can belong to a social network not as such, but as a set of objects that define a necessity. This research work tries to redefine social networking of an online community, where objects that work on the same stage define the team. To do this we propose a school architecture, where objects define the curricular activities, with known characteristics.

Characteristics of mobility models for mobile ad hoc networks

Recent advances in wireless communication technology have generated a great interest in building and using ad hoc mobile networks in many diverse applications. A mobility model is an algorithm that directs the node movement in a wireless mobile network simulation environment. This research points out most of the characteristic that could differentiate those mobility models. Different attribute distinguishes the existing mobility models. This study will also review some real-world deployments along with their attributes. Studying those models will help researchers deciding upon appropriate mobility model to be used in the simulation of mobile ad hoc network protocol.

Evaluation and comparison of open source software suites for data mining and knowledge discovery.

The growing interest in the extraction of useful knowledge from data with the aim of being beneficial for the data owner is giving rise to multiple data mining tools. Research community is specially aware of the importance of open source data mining software to ensure and ease the dissemination of novel data mining algorithms. The availability of these tools at no cost, and also the chance of better understanding of the approaches by examining their source code, provides the research community with an opportunity to tune and improve the algorithms. Documentation, updating, variety of algorithms, extensibility, and interoperability among others can be major issues to motivate users for opting for a specific open source data mining tool. The aim of this paper is to evaluate 19 open source data mining tools and to provide the research community with an extensive study based on a wide set of features that any tool should satisfy. The evaluation is carried out by following two methodologies. The first one is based on scores provided by experts to produce a subjective judgment of each tool. The second procedure performs an objective analysis about which features are satisfied by each tool. The ultimate aim of this work is to provide the research community with an extensive study on different features included in any data mining tool, either from a subjective and an objective point of view. Results reveal that RapidMiner, Konstanz Information Miner, and Waikato Environment for Knowledge Analysis are the tools that include higher percentage of these features. WIREs Data Mining Knowl Discov 2017, 7:e1204. doi: 10.1002/widm.1204