Big Data Management in Education Sector: an Overview

The advancement in technological innovation has given rise to a new trend known as Big Data today. Given the soaring popularity of big data technology, organisations are profoundly attracted to and interested in it to transform their organisation by improving their businesses. Big data is enabling organisations to outpace their competitors and save cost. Similarly, the application of Big Data management in Universities is an essential aspect to institutions that have Big Data to manage; as the use of Big Data in the higher education sector is increasing day by day. Many studies have been carried out on big data and analytics with little interest in its management. Big Data management is a reality that represents a set of challenges involving Big Data modeling, storage, and retrieval, analysis, and visualization for several areas in organizations. This paper introduces and contributes to the conceptual and theoretical understanding of Big Data management within higher education as it outlines its relevance to higher education institutions. It describes the opportunities this growing research area brings to higher education as well as major challenges associated with it

The problems and challenges of managing crowd sourced audio-visual evidence

A number of recent incidents, such as the Stanley Cup Riots, the uprisings in the Middle East and the London riots have demonstrated the value of crowd sourced audio-visual evidence wherein citizens submit audio-visual footage captured on mobile phones and other devices to aid governmental institutions, responder agencies and law enforcement authorities to confirm the authenticity of incidents and, in the case of criminal activity, to identify perpetrators. The use of such evidence can present a significant logistical challenge to investigators, particularly because of the potential size of data gathered through such mechanisms and the added problems of time-lining disparate sources of evidence and, subsequently, investigating the incident(s). In this paper we explore this problem and, in particular, outline the pressure points for an investigator. We identify and explore a number of particular problems related to the secure receipt of the evidence, imaging, tagging and then time-lining the evidence, and the problem of identifying duplicate and near duplicate items of audio-visual evidence

Big Data Management in Education Sector: an Overview

The advancement in technological innovation has given rise to a new trend known as Big Data today. Given the soaring popularity of big data technology, organisations are profoundly attracted to and interested in it to transform their organisation by improving their businesses. Big data is enabling organisations to outpace their competitors and save cost. Similarly, the application of Big Data management in Universities is an essential aspect to institutions that have Big Data to manage; as the use of Big Data in the higher education sector is increasing day by day. Many studies have been carried out on big data and analytics with little interest in its management. Big Data management is a reality that represents a set of challenges involving Big Data modeling, storage, and retrieval, analysis, and visualization for several areas in organizations. This paper introduces and contributes to the conceptual and theoretical understanding of Big Data management within higher education as it outlines its relevance to higher education institutions. It describes the opportunities this growing research area brings to higher education as well as major challenges associated with it

Data compression for the First G-APD Cherenkov Telescope

The First Geiger-mode Avalanche photodiode (G-APD) Cherenkov Telescope (FACT) has been operating on the Canary island of La Palma since October 2011. Operations were automated so that the system can be operated remotely. Manual interaction is required only when the observation schedule is modified due to weather conditions or in case of unexpected events such as a mechanical failure. Automatic operations enabled high data taking efficiency, which resulted in up to two terabytes of FITS files being recorded nightly and transferred from La Palma to the FACT archive at ISDC in Switzerland. Since long term storage of hundreds of terabytes of observations data is costly, data compression is mandatory. This paper discusses the design choices that were made to increase the compression ratio and speed of writing of the data with respect to existing compression algorithms. Following a more detailed motivation, the FACT compression algorithm along with the associated I/O layer is discussed. Eventually, the performances of the algorithm is compared to other approaches.Comment: 17 pages, accepted to Astronomy and Computing special issue on astronomical file format