/library/oar/handle/123456789/77440 2025-11-13T19:28:01Z 2025-11-13T19:28:01Z /library/oar/handle/123456789/95971 2022-05-19T08:08:20Z 2015-01-01T00:00:00Z

Title: Using social media as a basis for marketing initiatives Abstract: In computing, social media is represented as a mathematical graph which depicts users as nodes, and interactions as edges. Users are either persons or company profiles - even a specific product. The edges convey feelings, likings and acceptance which are known to be very useful for businesses to understand better their product's perception by users and consequently influence the business's marketing tactics. Due to the remarkable popularity reached by social media platforms, the data available is so vast that many marketing experts cannot ascribe significant meaning when looking at it as it is. The purpose of this project is to tap into this data from two popular social media platforms, and transform and process it in a way to enable deeper knowledge extraction that is usable in a marketing initiative. The final artefact of this project acquires data from the two platforms by connecting, and sending requests to their APIs. These requests return responses consisting of data available with regards to that particular request. This data is then processed to create one consolidated model encompassing the data acquired from both sources. This is then stored in a graph based data model which is queried as required. The data returned by the APIs highly depends on the social media platform itself as they have included further privacy concerns in their latest APIs which limits the data in the response. This artefact works with the given public data, however if users give permission to use their data in the application, it would be able to give more insightful information. The end result of this artefact consists of a number of charts which portray significant information that help business users to understand their fans and hence improve their marketing campaign. Description: B.Sc. IT (Hons)(Melit.)

2015-01-01T00:00:00Z /library/oar/handle/123456789/95519 2022-10-06T10:05:00Z 2014-01-01T00:00:00Z

Title: Exam time-table scheduling Abstract: Exam timetable scheduling is a complex problem which often has to be dealt with at Universities. This project investigates this problem for the Faculty of ICT in University of Malta (UoM). Currently, examination officers start this process several weeks before examination period. The work involves dedicating approximately 2 working days to produce an initial draft, and performing changes upon student and/ or staff complaints, until a final timetable is prepared, possibly weeks later. This dissertation demonstrates how, using Genetic Algorithms, this process can be complete in just about 1 minute, while also improving the quality of timetables from those generated by hand. Apart from generating a timetable which is conflict free and significantly minimizes near exam occurrences for students during this stressful period, an intuitive user interface, with a calendar design, is also provided to aid examination officers through the whole process of producing a final timetable. This includes making the whole system interactive and providing useful information to accommodate for late changes, optional units from other faculties, and special requests which are especially common in real-word problem applications. Description: B.Sc. IT (Hons)(Melit.)

2014-01-01T00:00:00Z /library/oar/handle/123456789/95381 2022-05-10T09:35:29Z 2013-01-01T00:00:00Z

Title: Improving airlines' real-time, online reservation systems Abstract: The use of online Airline Reservation Systems in these last years has lead to an increase demand for customer satisfaction and loyalty which would then enable the airline to remain competitive. State of the art technology advancement in this area is vital in order to make online booking systems more user-friendly and customer oriented especially nowadays with the notion of low cost airlines and other external forces in the industry. It is clear from this study that not all the airlines realise the difference between a common website and an ideal one. [ 4] In order to achieve my aims and objectives, the following procedures were incorporated. Firstly, information was gathered by making use of a survey and a set of fifteen questionnaires, to formulate a better picture as to what customers expect from such systems. Apart from that existing frameworks were considered as the foundation on which to build up my improved airline reservation system. The airline website was developed using the .NET technology, specifically the ASP.NET for the front-end of the system. C# was then used for the handling of logic aspects of the site. Additionally, the database tables were stored in Sequel Server 2008 RS. This research evaluates some of the most leading airline websites in the industry. The aim is to propose and design an improved airline reservation website following certain specified criteria which encourages more users to make use of the new e-commerce phenomenon and rendering more money to the company. The final result is aimed at providing passengers with an inbuilt, user friendly interface and additional features to cater for their needs. Finally, the online booking system is geared at integrating travellers from all sectors of society. This is setup to manifest the benefit of e-commerce to both passengers using conventional methods and also those technological oriented individuals. conventional methods and also those technological oriented individuals. Description: B.Sc. IT (Hons)(Melit.)

2013-01-01T00:00:00Z /library/oar/handle/123456789/95316 2022-05-09T13:16:15Z 2013-01-01T00:00:00Z

Title: Decision tree learning algorithms in a cloud computing environment, utilizing the map reduce programming framework Abstract: Decision tree learning algorithms are one of the most commonly used techniques for learning from sets of collected data [22] [21 J. Their wide use in several real-life applications means that such algorithms have to work on data sets with ever-increasing sizes and on machines with limited processing power. The recently discovered MapReduce framework is a parallel programming framework, which enables users to develop parallel algorithms [36]. These parallel algorithms are executed in a grid-computing environment in order to utilize resources from the various machines connected to the grid. In this project a cloud-based, grid-computing environment is used, which is pre-set to execute MapReduce algorithms. This project explores the use of Apache Hadoop, which is an open-source implementation of the MapReduce framework [14], to parallelize algorithms, particularly Decision Tree Learning algorithms. An implementation of a parallelized version of Quinlan's [23] ID3 algorithm using the MapReduce framework is presented. Two more variations of the algorithm are also presented in this project; one implemented using breadth-first tree induction and the other implemented in a partially parallel manner. The aim of these implementations is to propose other methods for the implementation of decision tree learning algorithms which execute more efficiently on larger data sets. Reference was made to other similar parallel decision tree learning algorithm implementations [19] [28] [22] in order to develop the aforementioned algorithm variations. The evaluation of the project shows that all the implemented algorithms successfully derive a decision tree from the data set supplied. The performance of the algorithms was compared and the results show that having partially parallelized algorithm can be more efficient than a completely parallel one. Findings have also shown that implementing such algorithms using a breadth-first induction have a hidden inefficiency which could lead to a less efficient performance of the algorithm. Description: B.Sc. IT (Hons)(Melit.)

2013-01-01T00:00:00Z