Browsing by Author "Beyene, Melkamu (PhD)"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Assessing Big Data Analytics Readiness in Ethio Telecom(Addis Ababa University, 2021-09-13) Teka, Wujira; Beyene, Melkamu (PhD)In the age of technological and advanced world big data is important as a world new currency. Organizations believe that big data can improve their performance. However, as with any innovation & adoption, big data analytics should be strictly evaluated for a specific organizational context before adoption. Numerous organizations adopt technology without seeing their level of readiness for that technology which, leads to ineffective implementation and may lead to failure. Big data analytics is an expensive, resource-intensive, and complex process that has been plagued by many problems leading to significant project failures in different industries. The organizational readiness for embracing big data technology must be assessed to discover the organization’s maturity in revealing the potential of this technology. Assessment of readiness for big data analytics indicates the company’s current state of progress in terms of Big Data analytics and provides a practical roadmap of future steps need to be taken. The main objective of this research is assessing the readiness of Ethio Telecom for big data technology adoption. To evaluate its readiness, the study used five-dimensional framework which consists of organization specifications, data ecosystem, data governance, data management, and resources. The research used mixed research approach, Explanatory sequential which quantitative data collection and analysis conducted first, followed by qualitative data collection and analysis to assess big data analytics in Ethio Telecom. The research data relevant to measure the readiness of the organization based on the five dimensions is collected using survey questionnaires and interviews from Ethio Telecom information system security division. Stratified simple random sampling for quantitative and purposive sampling for qualitative was used in this study. The data was analyzed using descriptive statistic and data analysis using theme methods. Result indicate that Ethio Telecom is ready in terms of organization specification, data ecosystem, and data management. The findings of this study show that Ethio Telecom is ready to adopt big data in terms of organizational specification, data ecosystem, and data management and with some effort on data governance and resource (technology and human resource). Five-dimensional model that used by this study helps to Ethio Telecom, to improve their chances of successfully implementing big data analytics.Item Assessment of Enterprise Resource Planning Post- Implementation Success: the Case of Ethio Telecom(Addis Ababa University, 2018-06-02) Tadesse, Abraham; Beyene, Melkamu (PhD)The main objective of this study is to assess the success of ERP post-implementation at Ethio telecom. The general approach of this research was a case study in which a combination of quantitative and qualitative methods has been used to collect and analyze data. The quantitative data was collected using questionnaires from sample population of 320 users from HR, Finance and sourcing & facility departments with different job positions, roles, and work experience. The collected data was analyzed using SPSS. In the qualitative study, direct interviews were used to collect data from three executive management members of Ethio telecom. A theoretical ERP success model was used to assess the success of ERP Post- implementation at Ethio telecom. The results of this study show the deployed ERP system has brought significant impact on individual performance by enhancing employee awareness and recall of job- related information, increased employee productivity and decision-making activities during post-implementation phase. Similarly, the study report shows there is positive impact in organizational business process managements by integrating and improving internal communication, providing accurate & realtime information, increasing efficiency and productivity, reduces administrative and operations costs, and resulted better positioning for e-government and online business transactions. Even though ET has gained many benefits from deployed ERP system, some challenges were encountered during post implementation phase. Among those challenges ERP system response time delay (slow response), ERP system data integration with other ET soft-wares, ERP system is not always up and running as necessary, lack of training & IT-helpdesk support to solve the problem were the main challenges during post implementation phase. Hence, the researcher has recommended that Ethio telecom is expected to resolved challenges of ERP system interruptions, ERP data integration, ERP system slow performance, ERP system flexibility, lack of adequate training and IT-help desk support as top priority to get the full-fledged benefits from deployed ERP System.Item Assessment of Post Implementation of Enterprise Resource Planning System (ERP) Case of Ethiopia Red Cross Society(Addis Ababa University, 2021-10-04) Tadesse, Shimelis; Beyene, Melkamu (PhD)In order to achieve the many benefits ERP systems, the organization have to offer, an organization needs to achieve ERP post implementation success. The challenge is that many organizations don’t realize meaningful business process improvements after their ERP implementations [48]. The main objective of this study is to assess post implemented ERP in Ethiopian Red Cross Society in quality, user satisfaction and net benefit dimension and to assess the major issues in the post implementation ERP in ERCS. The data was collected through online survey questionnaire and interview which are adopted from D&M Model [13]. The general approach of this research was a case study in which combination of quantitative and qualitative methods have been used to collect and analyze data using SPSS software. From the findings of the study, the quality of work ERP, user satisfaction of ERP and net benefits of ERP have positive impact in ERP post implementation success. Network connectivity problem in regional branches and lack of adequate training and unreliability IT support are found to be major issue of ERP post implementation. The study recommends Network connectivity issue the organization should upgrade the band width the network in regional branch so as to have smooth operation of ERP system. Periodical refreshment training for employee should is recommended. Improvement of ERP support system should be done by hiring additional dedicated IT experts.Item Big Data Analytics to Predict Cancer Based on Diagnosed Clinical Data(Addis Ababa University, 2019-05-03) Alemayehu, Belay; Beyene, Melkamu (PhD)These days, vast amount of medical data (i.e. medical images, biomedical signals and handwritten prescriptions) are available that can be utilized for pre-diagnostic tasks on the existence of cancer cells by adopting big data analytic concepts. Hence, the main objective of the study was designing a big data analytics model that predicts the occurrence of cancer cells from medical data (medical images, biomedical signals and handwritten prescriptions) available in St. Paul’s hospital. . A big data analytics model that predict the occurrence of cancer cells from the big medical data that have been collected by different academic and medical imaging departments in the St paul’s hospital millennium medical college is designed. Novel data engineering techniques are applied to ensure the quality of data and integrate data from different sources. Deep learning approach based on a logistic activation function is employed to build the model. The deep learning is implemented on a hadoop framework by configuring five commodity machines in which each of them comprised core i3 processor, 4 GB RAM and 1TB of hard disk storage.Item Community Detection in Facebook: a Big Data Analytics Approach(Addis Ababa University, 2020-06-06) Terefe, Badimaw; Beyene, Melkamu (PhD)This thesis is about designing a community detection model on Facebook using relational data. It is mainly aimed at helping companies by grouping Facebook users based on their interests which has many applications. The researchers selected four algorithms based on the concept of modularity optimization/maximization. Modularity based algorithms were selected since modularity is the best quality measure of a community detection algorithm. The algorithms selected and then compared were the ‘girvan_newman’ algorithm, the ‘greedy_modularity_communities’ algorithm, the ‘fast_greedy’ algorithm, and the ‘Louvain’ algorithm. Each of these four algorithms was studied separately before, but no detailed study was done to show which algorithm best detects communities especially when it comes to relational learning. Five datasets required to undergo an experiment (five experiments since it is difficult to ensure an algorithm as the best algorithm by doing a single experiment) were extracted from Facebook and prepared into matrix and graph data structures. An attempt was made to select the best algorithm among the aforementioned algorithms and the results gained were cheering. The results gained in the experiment showed that the Louvain algorithm was the best in many aspects. First and for most, it is the one that detected communities with the best modularity value (average modularity of 0.491). Second, it is the algorithm that fulfilled all of the conditions to say an algorithm as the best algorithm such as ease of use, ability to detect overlapping communities, state of the art, support to the visualization of networks, and the possibility to run it on a single or distributed computing resource. Finally, it is also the one that best balances the speed quality tradeoff in community detection. That means, it is the algorithm that detected quality communities within an acceptable time. There were many challenges the researchers faced during community detection. But two of them were the most difficult ones. The first challenge was scrapping data from Facebook and the second one was understanding the data type algorithms need to perform community detection. The first challenge was solved by understanding the HTML structure of Facebook pages and the second one was solved by studying graph data structures and file extensions in detail.