Mr. Alex Munyole

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.

Digitalization is transforming governments across the globe. At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of massive digitalization. Digitalization is compelling governments at all levels to embrace voluminous data and institute appropriate multi-channel platforms to support digital transformation. While this is the case, most governments have been caught unprepared thwarting maximum benefits spurred by digitalization. Inherently, the social media and e-participation tools for generating huge amount of data have convoluted most governments’ appetite in Big Data management. This situation is further compounded with the slow pace of adoption of these technological tools by citizens and the public sectors. For enhanced e-citizen satisfaction and engagement, as well as e-participation processes, public institutions need to promote engagement and collaboration. In view of advancing benefits to their citizens, public institutions need to institute appropriate measures to collect citizen’s data. The information collected is vital for public institutions in actualizing what services the citizens want. Using literature reviews and cases, the authors examine Big Data benefits in counties and propose a Big Data model to improve efficiency of e-governance services and productivity in county governments. The authors demonstrate Big Data framework has the aptitude of molding citizen’s opinion in county decision making process. Better use of e-technologies is shown in the proposed model which illustrates sharing resources among various data analytics sources. Our proposed framework based on Big Data analytics is a viable initiative to progress effectiveness and productivity, strengthen citizen engagement and participation and encourage decision-making in e-governance services delivery in the counties.

Content Delivery Networks has created a sharp rise in internet traffic in recent years. New technologies
have simplified users access to rich content, however, as the internet continues to grow and more users access
content stored in CDNs, issues such as reduced scalability, reliability and availability becomes a challenge to end
users. This study investigates end user request redirection performance in content development network using
software-defined networking - based network. The results should assist researchers in network management,
Internet Service Providers (ISPs) in managing request redirection as a service. An experiment to determine effects
of client-side DNS caching on the contemporary DNS-based redirection technique found out that both Firefox and
Chrome are prone to client-side DNS caching. When subjected to a passive testing, Firefox and Chrome
“timeout.” Moreover, users had to refresh their browsers for over a minute to get a response from a CDN server.
The findings further indicate that Software Defined Networking improves users’ quality of service in request
redirection. It took ten seconds for multiple users accessing CDN servers to download content and get redirected.
Further, the Domain Name System augmented with Network Address Translation technique, based on SDN
approach can reliably redirect user request to an optimal CDN server. This is when if it discovers the DNS
contains out of date information. The Ryu controller gathered network statistics and pushed SNAT and DNAT
rules. The rules directed TCP packets, hence reliably redirecting users to a better performing CDN server.