Big Data Technology And Leveraging Open Data For Electoral Processes In Nigeria

What comes to our minds in Nigerians during electioneering processes are the humongous rallies, colourful campaigning, myriad party symbols and distribution of items and monies now called ” Stomach Infrastructure”. The election results are marred with electoral frauds such as rigging, snatching of ballot boxes and consequent political violence. The political communication with citizens is changing and experiencing serious revolution globally through the use of Big Data and electronic technologies. Nigeria is gradually introducing electronic technologies in the conduct of election but much has not been achieved in this direction. This paper therefore examines Big Data Technologies and how Open Data can be leveraged for electoral processes in Nigeria. The study makes use of secondary data in the form of content analysis of books, journals and internet materials. The paper discovers that the combination of Big Data and computational politics allow for massive, latent data collection and sophisticated modeling. There is an increase in the capacity of those with resources and access to use these tools to carry out highly effective and unaccountable campaign of persuasion and social engineering in political, civic and commercial spheres. It is therefore recommended that, the initiative of Big Data Technology and the leveraging Open Data for electoral processes in Nigeria should be encouraged in order to reduce election rigging and malpractices. However, not only Independent National Electoral Commission (INEC) should be involved but also by other various bodies, which have well-defined roles for a greater coherence in achieving quality electoral objectives

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Software Engineering for Big Data Systems

Software engineering is the application of a systematic approach to designing, operating and maintaining software systems and the study of all the activities involved in achieving the same. The software engineering discipline and research into software systems flourished with the advent of computers and the technological revolution ushered in by the World Wide Web and the Internet. Software systems have grown dramatically to the point of becoming ubiquitous. They have a significant impact on the global economy and on how we interact and communicate with each other and with computers using software in our daily lives. However, there have been major changes in the type of software systems developed over the years. In the past decade owing to breakthrough advancements in cloud and mobile computing technologies, unprecedented volumes of hitherto inaccessible data, referred to as big data, has become available to technology companies and business organizations farsighted and discerning enough to use it to create new products, and services generating astounding profits. The advent of big data and software systems utilizing big data has presented a new sphere of growth for the software engineering discipline. Researchers, entrepreneurs and major corporations are all looking into big data systems to extract the maximum value from data available to them. Software engineering for big data systems is an emergent field that is starting to witness a lot of important research activity. This thesis investigates the application of software engineering knowledge areas and standard practices, established over the years by the software engineering research community, into developing big data systems by: - surveying the existing software engineering literature on applying software engineering principles into developing and supporting big data systems; - identifying the fields of application for big data systems; - investigating the software engineering knowledge areas that have seen research related to big data systems; - revealing the gaps in the knowledge areas that require more focus for big data systems development; and - determining the open research challenges in each software engineering knowledge area that need to be met. The analysis and results obtained from this thesis reveal that recent advances made in distributed computing, non-relational databases, and machine learning applications have lured the software engineering research and business communities primarily into focusing on system design and architecture of big data systems. Despite the instrumental role played by big data systems in the success of several businesses organizations and technology companies by transforming them into market leaders, developing and maintaining stable, robust, and scalable big data systems is still a distant milestone. This can be attributed to the paucity of much deserved research attention into more fundamental and equally important software engineering activities like requirements engineering, testing, and creating good quality assurance practices for big data systems