Cloud computing is a business paradigm wherein computers and computing related services are provided by Cloud Service Providers to consumers either as software, development platform, or infrastructure. Innovative applications are growing in a productive manner on the cloud landscape. Innovative applications are being developed for use in the area of e-learning, automotive processes, cloud containers and machine learning. The objective of this paper is to conduct a systematic mapping study of innovative cloud applications and experiences. The systematic map provided a structured overview of research work carried out and the frequency of publications, presenting them pictorially in form of a map. The obtained results showed that 7.34% of the publications were on development of innovative cloud applications in terms of model. Architecture and modelling, and simulation in relation to model were both at 13.76%, 11.93% of the papers respectively, while 8.26% of the articles were on deployment in terms of process. Architecture had most publication in the area of solution research, with 15.2%. For articles published on deployment and development, most were on solution research with 8.80% and 14.40% respectively. The outcome of this study will be beneficial to practitioners in the industry and academic researchers alike.

This paper presents a finite state model of reduplication processes in Igbo. Identified Igbo reduplication processes are based on (i) verbal reduplication with prefixation, (ii) total nominal reduplication (iii) ideophone reduplication. However, this work identifies and includes a fourth and fifth type that occurs in the language, namely; (iv) adverbial reduplication and (v) prepositional reduplication. Xerox Finite State Tool (XFST) was used in representing the five Igbo reduplication processes computationally. Igbo verbal reduplication exhibits selective reduplication process and is characterized by prefixation and vowel replacement. Vowel harmony phenomenon was taken into consideration in achieving verbal reduplication to cater for phonological changes. Model testing results showed 84% accuracy in both analysis and recognition of reduplicated forms in Igbo.

Computational studies of Igbo language are constrained by non-availability of large electronic corpora of Igbo text, a prerequisite for data-driven morphological induction. Existing unsupervised models, which are frequent-segment based, do not sufficiently address non-concatenative morphology and cascaded affixation prevalent in Igbo morphology, as well achieving affix labelling. This study devised a data-driven model that could induce non-concatenative aspects of Igbo morphology, cascaded affixation and affix labelling using frequent pattern-based induction. Tenfold Cross Validation (TCV) test was used to validate the propositions using percentages. An average accuracy measure of 88% was returned for the developed model. Ten purposively selected Igbo first speakers also evaluated samples of 100 model-analysed words each and the mean accuracy score of 82% was recorded. We conclude that morphology induction can be realized with a modestly sized corpus, demonstrating that electronic corpora scarcity does not constrain computational morphology studies as it would other higher levels of linguistic analysis.