Social media are gaining popularity and are increasingly used in regular operations of many organizations, including Micro Enterprises (MEs). Several studies have thus developed interests on investigating the impact of social media in MEs. Unfortunately most of the existing studies either did not consider performance factor of MEs or were not done in the context of developing counties like Tanzania. It is on this ground that this study explored how social media influence the MEs' performance in the context of customer's base, sales growth, profit maximization, and brand enhancement. Specifically the study focused on identifying the most used social media by MEs; influence of social media to the performance of MEs; and how MEs use social media in their business process. Data were collected in Moshi, Tanzania from a representative sample of 90 MEs. The study adopted case study research design where structured questionnaires and interview were used to collect data. Findings depict that all else being equal, the use of social media enhance business performance. WhatsApp was found to be the most, preferred social media by MEs, followed by Facebook, Instagram and Twitter. On the other hand, awareness, risks and insecurity of information, and costs was some of the observed challenges that hinder MEs from using social media. It was concluded that effective use of social media is an efficient tool for enhancing MEs performance. Among others, the study calls for further research on financial and marketing aspect of social media and relative involvement associated with the possible solution toward the challenges of social media

No abstract

FP-Growth is one of the most effective and widely used association rules mining algorithm for discovering interesting relations between items in large datasets. Unfortunately, classical FP-Growth mines frequent patterns by using single user-defined minimum support threshold. This is not adequate for real life applications such as crime patterns mining. On one side, if minimum support is set too low, huge amount of crime patterns (including uninteresting patterns) may be generated, and on the other side, if it is set too high lots of interesting patterns (including seasonal patterns) may be lost. This paper proposes the use of Multiple Item Support (MIS) thresholds instead of single minimum support to tackle the challenge. We employ Shannon entropy method to develop an algorithm that obtains MIS values from crime datasets. The proposed approach is tested on different sizes of input data via a developed working prototype. Experimental results show that our suggested approach outperforms classical FP-Growth in terms of running time and memory use.

No abstract

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, literature have identified several challenges associated with existing big data analytics frameworks in HEIs. It was on this line that the present study, which was based on desk research, was carried out to propose an effective big data framework for analytics of such data. The proposed framework is composed of five stages; data collection, data pre-processing, data storage, data analytics, and data visualization. The stages were arranged systematically to address the identified challenges in the existing frameworks. Effective implementation of this framework will help HEIs to make a productive use of various data they generate. This will ultimately be beneficial to not only HEIs but also to aspired students, labour market, the government and the public at large.