An economic energy approach for queries on data centers

Energy consumption is an issue that involves all of us, both as individuals and as members of a society, and covers all our areas of activity. It is something so broad that its impact has important reflections on our social, cultural and financial structures. The domain of software, and in particular database systems, is not an exception. Although it seems to be a little bit strange to study the energy consumption of just one query, when we consider the execution of a a few thousand queries per second, quickly we see the importance of the querying consumption in the monthly account of any company that has a conventional data center. To demonstrate the energy consumption of queries in data centers, we idealized a small dashboard for monitoring and analyzing the sales of a company, and implemented all the queries needed for populating it and ensuring its operation. The queries were organized into two groups, oriented especially to two distinct database management systems: one relational (MySQL) and one non relational (Neo4J). The goal is to evaluate the energy consumption of different types of queries, and at the same time compare it in terms of relational and non-relational database approaches. This paper relates the process we implemented to set up the energy consumption application scenario, measure the energy consumption of each query, and present our first preliminary results

A framework for IoT implementation in the public healthcare system in Zimbabwe.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.The study’s main objective was to ascertain the viability of implementing a “best practices” implementation of an Internet of Things (IoT) intervention in the public healthcare system in Zimbabwe. The motivation for conducting this study is that currently, Zimbabwe is faced with a huge challenge to meet the healthcare requirements of its citizens. A major source of the problem lies in a lack of coordination between the various healthcare professionals and health management systems that are meant to ensure the optimum availability of expertise and infrastructure so that health related challenges are effectively countered. An efficient healthcare system will enhance the affordability and access to medical healthcare for many Africans who are in dire need of such services. This can only be achieved if there is an identification of critical areas of healthcare delivery where a technological intervention would provide a new enhanced dimension for the delivery of a quality healthcare service for the citizens of Zimbabwe. Currently, the technological systems are being used in an ad hoc manner with no structured mechanisms for ensuring a coherent, systemic approach to healthcare delivery. The current study was designed to obtain knowledge of the effectiveness of the current IoT setup that is used in the Zimbabwean healthcare sector. The empirical evidence obtained in the current study was used to initiate the synthesis of a framework based on IoT technology to enhance healthcare service delivery in the public healthcare system in Zimbabwe. The empirical phase of the studyconsisted of a qualitative phase where phenomenology was used to obtain insights from medical healthcare professionals into the healthcare system in Zimbabwe. The qualitative phase was followed up by a quantitative phase where the “best practices” framework was presented to a cohort of healthcare professionals for validation by ascertaining the behavioural intention to use the framework. The outcome of the validation phase indicated that 81% of the respondents indicated a high level of acceptance of the proposed framework. Minor changes to the framework were suggested by the study’s respondents and these were incorporated into a refined version of the “best practices” framework for IoT implementation in Zimbabwe

Towards Efficient Intrusion Detection using Hybrid Data Mining Techniques

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but also helps classifiers make accurate decisions during attack recognition time, especially when coping with large-scale and heterogeneous data such as network traffic data. Furthermore, the continued evolution of network attack patterns has resulted in the emergence of zero-day cyber attacks, which nowadays has considered as a major challenge in cyber security. In this threat environment, traditional security protections like firewalls, anti-virus software, and virtual private networks are not always sufficient. With this in mind, most of the current intrusion detection systems (IDSs) are either signature-based, which has been proven to be insufficient in identifying novel attacks, or developed based on absolute datasets. Hence, a robust mechanism for detecting intrusions, i.e. anomaly-based IDS, in the big data setting has therefore become a topic of importance. In this dissertation, an empirical study has been conducted at the initial stage to identify the challenges and limitations in the current IDSs, providing a systematic treatment of methodologies and techniques. Next, a comprehensive IDS framework has been proposed to overcome the aforementioned shortcomings. First, a novel hybrid dimensionality reduction technique is proposed combining information gain (IG) and principal component analysis (PCA) methods with an ensemble classifier based on three different classification techniques, named IG-PCA-Ensemble. Experimental results show that the proposed dimensionality reduction method contributes more critical features and reduced the detection time significantly. The results show that the proposed IG-PCA-Ensemble approach has also exhibits better performance than the majority of the existing state-of-the-art approaches