Dr Salam Fraihat earned his MSc and PhD degrees in Computer Science from Aix Marseille University, France in 2010. He earned a MSc in Information System and Science at the same university in 2004. During his graduate studies at the University of Aix Marseille, Dr. Salam worked as a teaching assistant for various courses from the freshman to graduate levels and had the opportunity to develop his skills as an educator. He is currently working as an Associate Professor at the College of Information Technology, Ajman University, United Arab Emirates. Prior, he was Head of the Computer Science Department, King Hussein School of Computing Sciences at Princess Sumaya University for Technology and Head of the Software Engineering department in Al-Ahliyya Amman University in Amman, Jordan. His main areas of research interests are in applied research solving challenging problems in Data Science and Deep Learning. Advanced theoretical knowledge and applied hands-on experience in Machine Learning (ML), Business Intelligence, Text/Image/speech, and Web Semantics.
This article builds on previous work in the area of real-world applications of Business Intelligence (BI) technology. It illustrates the analysis, modeling, and framework design of a BI solution with high data quality to provide reliable analytics and decision support in the Jordanian real estate market. The motivation is to provide analytics dashboards to potential investors about specific segments or units in the market. The article ekxplains the design of a BI solution, including background market and technology investigation, problem domain requirements, solution architecture modeling, design and testing, and the usability of descriptive and predictive features. The resulting framework provides an effective BI solution with user-friendly market insights for investors with little or no market knowledge. The solution features predictive analytics based on established Machine Learning modeling techniques, analyzed and …
Telecom Big Data: Social Media Sentiment Analysis
Weather can be described as the status of atmospheric conditions at a specific time. on the other hand, the climate is the weather’s status over a long period. both are very important for people’s life management on multiple levels. Weather prediction is a complicated process that requires input from experts. This paper describes a weather business intelligence solution starting from requirements gathering and analysis all the way to the creation of a dashboard with weather prediction capabilities based on a machine learning technique to fulfill the business needs.
Business Intelligence and Analytics has gained prominent focus among organizations with information systems that collect and process vast amounts of data. Voluminous, unprocessed data does not lend itself to offering useful insights for businesses, especially with basic statistical methods and traditional reporting techniques. In this work, we design a Business Intelligence and Data Analytics Framework for Refugee Registration System serving over six million refugees to collect, collate and filter demographic data. The proposed reporting mechanism leverages the power of interactive dashboards to offer informative and intuitive reports and visualizations that are accessible and interpretable by stakeholders.
Developing a Business Intelligence system has a major benefit for business owners as it supports and helps with decision making and strategy development, where a well-designed business intelligence system enables businesses to have a full and holistic view of the historical, current and future insights based on the available data. In this paper we develop a business intelligence system that can be deployed for a mobile money system to serve different user groups based on the needs and business goal of each group.
Real estate is one of the essential and challenging fields in the market which reflects the economy, and it needs constant improvement. Business intelligence nowadays plays a significant role in enhancing the process of decision making and risk management in many different fields. One of the promising fields is the real estate investment market. This paper proposes a framework for an effective BI solution for analyzing the real estate market and estimating the price of the properties. The building of the BI solution, which passes through multiple phases is demonstrated.
Business intelligence is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. The efficiency of making decisions can increase significantly using business intelligence solutions, by taking advantage of the existing historical or realtime data of the business. Trading in stock markets is imminent with taking risks of losing money, which requires extensive experience in the market, to make efficient decisions. In this paper, we propose a framework that makes use of stock prices historical data, to help investors in making more efficient trading decisions.
Mobile technologies have become the most rapidly growing and adopted technology in recent years. Currently, many higher education institutions are using mobile technologies, due to their portability and accessibility, to support a variety of activities in the education process. Course advising is an important part of the education process and it plays an essential role in students’ academic success. However, course advising is a challenging task due to the intensive human effort required from advisors; the unavailability of committed advisors due to other academic commitments; the advisors lack of knowledge; the time-consuming nature of this task; and the unavailability of related information on academic curriculum to the advisors. Nevertheless, such problems make the use of an automated course advising system desirable and helpful. This paper presents the design and implementation of a mobile application for university course advising, called m-advisor, that can be used to reduce the time and effort for both the students and advisors during the course advising process at the beginning of each academic semester. The evaluation results of the m-advisor application revealed that informative advices for students can be given on which appropriate courses, that can fit their needs in accordance with the requirements of the student’s academic program, to register in the upcoming semester.
Machine Translation is the use of computerized methods to automate all or part of the translation process from one natural language into another. Machine Translation systems used to overcome the language barriers, for example, by making digital information understandable to people across the world in minimum amount of time. A Multiagent system is a software system that consists of multiple active, task-oriented and autonomous intelligent agents. Such agents can communicate and coordinate between each other in order to produce high quality solutions to complex problems in different domains. The semantic web is realized by adding semantics to the web in which it gives well-defined semantic meaning of information. It makes it possible to facilitate the representation, interpretation, sharing, searching, and reusing of information. This paper proposes a Semantic Multi-Agent Architecture for Multilingual
Automatic document classification has become increasingly important and difficult due to the large scale of the electronic documents used in the last years. Traditional information retrieval systems are based on the extraction of keywords from documents; these keywords serve as a basis for documents classification. This paper proposes a new semantic approach for documents classification. Specifically, our approach captures, in addition to the keywords frequency, the meaning of these keywords in documents using domain ontology.
Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items’ semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based Multi-Criteria Collaborative Filtering algorithm that integrates the items’ semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques
Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed.
Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm.