In South Africa, access to safe drinking water is a human right that is explicitly stated in the constitution. Most metro municipalities are meeting the drinking water quality targets, but the smaller rural environments are failing to provide water of acceptable drinking water quality. Reasons contributing to the high incidence of unacceptable water quality are the rural municipalities' inadequate institutional capacity and lack of management and monitoring of drinking water services. This study investigates the possibilities of supporting rural water service institutions to manage their remote water supply schemes better by addressing the challenge of distance monitoring. Through the creation of real-time information flow between the water service authorities and the water supply caretakers in remote villages, it is to be tested if better information can be received and the status of the rural water supply quality can be monitored. The improvement of information flow is based on introducing a mobile phone application. The hypothesis is that through improving the information flow, decisions on water supply management will be improved. Case study research was conducted in rural municipalities situated in the Northern Cape Province and Eastern Cape Province of South Africa. Four different municipalities were chosen to reveal the diverse municipal set-up and different challenges facing rural municipalities. Data was gathered through interviews conducted with the municipal mangers over a seven month period, as well as through field investigations. The findings reveal that the mobile reporting system has improved information flow from water supply caretakers to government service providers. The mobile application allowed for distance monitoring of rural water supply schemes. It has helped address the municipalities' institutional capacity problems by improving access to information relevant to decision making. Through the data records displayed on the mobile application, municipal mangers were able to track the supply caretakers' performance and subsequently hold them accountable. Through an increase in data availability, water quality failures were easily identified, resulting in improved confidence in the quality of rural water supply. The access to real-time information has improved the monitoring and communication of rural water quality. Early intervention and the management of non-compliance improved. The mobile technology provided the municipal managers with a tool to monitor their rural water supply schemes more regularly, but it also became apparent that the management of such schemes only improved if relevant action was taken based on the information received. Greater improvement was seen in municipalities where the tool was used consistently, where time was set aside to follow up on data warnings and protocols existed to follow up on non-compliance issues. Management of the resources did not improve in areas where management staff was severely overstretched and response strategies to problems were non-existent before the implementation of the tool.

Research data management (RDM) focuses on the organization and description of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. RDM entails storage, security, preservation, compliance, quality, sharing and jurisdiction. In the academic world, RDM can support the research process by searching for relevant data, storing data, describing data and advising researchers on good RDM practice. This study focused on developing RDM services. The aim of the study was to establish the role Makerere University Library could play in developing RDM Services. A number of questions were formulated to guide the researcher in finding answers to the research questions. A literature review, based on the research sub-questions, was carried out. The review covered the concept of RDM, academic libraries and their RDM practices, various RDM services in academic libraries, RDM services that require sustainability and how current researchers, in general, manage their data. The research undertaken took a qualitative approach with a case study design. This was due to the need to gather in-depth and comprehensive views and experiences regarding RDM at Makerere University. A purposive sampling technique was used to identify researchers who are actively involved in managing research data at Makerere University. Data were collected using semi structured interviews, from eight participants; one from each college. The participants were selected because of their knowledge about RDM and semi-structured interviews were preferred due to their flexibility. An interview schedule was used as the data collection instrument. Data was transcribed into Microsoft Word for easy analysis. Findings that addressed the research question and sub-questions were presented and interpreted in chapter four and conclusions as well as recommendations were discussed in detail in chapter five of this research report. In summary it is possible to say that although researchers, from across the entire university, generate big volumes of research data it appears that researchers themselves manage, control and store their data making use of different removable devices. This is risky. So there is a need to develop RDM skills for all stakeholders. It does appear though that the researchers at Makerere University would be willing the support of RDM services if these are developed by the library. / Mini Dissertation (MIT)--University of Pretoria, 2017. / Carnegie Corporation of New York / Information Science / MIT / Unrestricted

We recently developed and published a scaleable multi-user benchmark called XMach-1 (XML Data Management benchmark) for evaluating the performance of XML data management systems [1]. To our knowledge it is the first such benchmark. It aims at realistically evaluating the performance of individual systems as well as to allow for a performance comparison between different systems and architectures ranging from native XML data management systems to XML-enabled relational DBMS. Specifying and implementing the benchmark revealed a number of problems which are partly due to the lack of a standardized XML query language, the complexity of the XML format and the relative immaturity of current XML database software. After a brief review of XMach-1 we will discuss our experiences made so far.

Scientific datasets from global-scale scientific models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. These data are frequently gridded measurements spanning two or three spatial dimensions, the time dimension, and often several data dimensions which vary by the specific dataset. These data are useful in many modeling and analysis applications across the geosciences. Unlike vector spatial datasets, raster spatial datasets lack widely adopted conventions in file formats, data organization, and dissemination mechanisms. Raster datasets are often saved using the Network Common Data Format (NetCDF), Gridded Binary (GRIB), Hierarchical Data Format (HDF), or Geographic Tagged Image File Format (GeoTIFF) file formats. Several of these are entirely or partially incompatible with common GIS software which introduces additional complexity in extracting values from these datasets. We present a method and companion Python package as a general-purpose tool for extracting time series subsets from these files using various spatial geometries. This method and tool enable efficient access to multidimensional data regardless of the format of the data. This research builds on existing file formats and software rather than suggesting new alternatives. We also present an analysis of optimizations and performance.

Analysis of Inferred Social Networks While the social network analysis (SNA) is not a new science branch, thanks to the boom of social media platforms in recent years new methods and approaches appear with increasing frequency. However, not all datasets have network structure visible at first glance. We believe that every reasonable interconnected system of data hides a social network, which can be inferred using specific methods. In this thesis we examine such social network, inferred from the real-world data of a smaller bank. We also review some of the most commonly used methods in SNA and then apply them on our complex network, expecting to find structures typical for traditional social networks.

Micro finance organizations play an important role since they facilitate integration of all social classes to sustained economic growth. Against this background, exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. Data Governance provides a different approach to data management, as seen from the perspective of business assets. In this regard, it is necessary that the organization have the ability to assess the extent to which that management is correct or is generating expected results. This paper proposes a data governance maturity model for micro finance organizations, which frames a series of formal requirements and criteria providing an objective diagnosis. This model was implemented based on the information of a Peruvian micro finance organization. Four domains, out of the seven listed in the model, were evaluated. Finally, after validation of the proposed model, it was evidenced that it serves as a means for identifying the gap between data management and objectives set.

This study investigated the role of academic libraries in implementing research data services, UKZN being the case study. The objectives of the study were to identify the need for research data services among UKZN researchers, to identify the major challenges associated with introducing research data services at UKZN, and to determine the possibility of implementing research data services at UKZN Libraries. The Data Curation Centre Lifecycle model was adopted as a framework for the study as it manages to connect the different stages of research data management. The study took a mixed methods approach of which interviews and a survey were used. A purposive sample was used to select library staff and random sample was drawn from 1341 UKZN academics. From a sample of 1341, 299 was the minimum size recommended by the Raosoft sample size calculator for a 5% margin of error and 95% confidence level. For quantitative analysis, an online questionnaire was administered using Google Forms. A series of questions were formulated for guidance in obtaining answers to the study objectives. Google Forms was used for the analysis while figures and tables were created using Microsoft Excel. Interviews from the library staff were recorded and data from interviews was transcribed into Microsoft Word. The study revealed that UKZN Libraries are still struggling with RDM policy development. The findings of the study revealed that researchers who responded to the study showed a lack of RDM awareness while library staff showed a moderate level of awareness. The study revealed that researchers at UKZN work with different types of data and they use different storage options such as removable storage devices, computer hard drives and cloud services. Although a few researchers have developed data management plans at UKZN, they have not done so because they were mandated by the institution - UKZN has not yet developed DMPs and library staff are not aware which funders require DMPs. The researchers who responded to the study showed interest in different trainings such as, training on data storage, development of DMPs and metadata creation. The library staff were more eager to provide data storage, data archiving and sharing mainly because of the existence of the UKZN data repository (Yabelana). Study recommendations are based on the analysed data. One of the recommendations was that UKZN Libraries should assume a role of being an advisor and trainer for research data services at UKZN.

The emerging concept of vehicle cloudification is a promising solution to deal with ever-growing computational and communication demands of connected vehicles. A key idea is to have connected vehicles in the vicinity form a cluster which is called vehicular micro cloud. Vehicles in this micro cloud collaborate with other cluster members over vehicle-to-vehicle (V2V) networks for collective data processing, shared data storage, collaborative sensing and communication services. A typical use case of vehicular micro cloud is creation of a regional distributed data storage service, where member vehicles of the cloud collaboratively keep data contents in their on-board data storage devices. This allows vehicles in and around the vehicular micro cloud to request the contents from the micro cloud member(s) over vehicle-to-vehicle networks, or even update the data on the spot. In this thesis, we will discuss the need for vehicular micro clouds, followed by the architecture, formation of the micro clouds, and feasibility of micro clouds. Furthermore, we will cover aspects of efficient data transmission between vehicles, how to increase the scalability and to make it time efficient and cost efficient on practical road conditions for moving vehicles by encouraging coordination between neighboring micro cloud to help transfer data .

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Recently it is obvious that climate change has became a critical topic for human society. As climate change becomes more severe, natural disasters caused by climate change have increasingly impacted humans. Most recently, Hurricane Ida killed 43 people across four states. Hurricane Ida's damage could top $95 billion, and many meteorologists predict that climate change is making storms wetter and wider. Thus, there is an urgent need to predict how much damage the flood will cause and prepare for possible destruction. Most current flood damage estimation system did not apply social media data. The theme of this thesis was to evaluate the feasibility of using machine learning models to predict hurricane damage and the input data are social media and satellite imagery. This work involves developing Data Mining approach and a couple of different Machine Learning models that further extract the feature from the data. Satellite imagery is used to identify changes in building structures as well as landscapes, and Twitter data is used to identify damaged locations and the severity of the damage. The features of Twitter posts and satellite imagery were extracted through pre-trained GloVe, ResNet, and VGG models separately. The embedding features were then fed to MLP models for damage level estimation. The models were trained and evaluated on the data. Finally, a case study was performed on the test dataset for hints on improving the models. / Master of Science / Natural disasters affect Millions of people's lives each year and it is becoming even more severe because of global warming. To make rescue more efficient when the roads and bridges are cut, social media and satellite imagery are effective data sources to help estimating flood damage. With the growth of social media, it is obvious that the post and information from people on the Internet are powerful. Also, with image processing technology improves, the information extracted from satellite images is crucial. In this work we have developed a data mining approach along with different combinations of pre-trained models using neural networks, satellite imagery and archived data from Twitter to estimate flood damage. The data mining approach leverages keywords to identify the event in the history posts in the Twitter, more specifically, we attain the geo-location, time, language information from Twitter, also using pre-event and post-event images which satellite took to generate vectors and thus effectively acquire very useful embedding features. With vectored information from Twitter and satellite imagery, we use pre-trained models and generate damage level prediction. The final results suggest that the proposed approach has potential to create more accurate prediction by using multiple data as input. Furthermore, the estimate result by using only satellite images even outperformed the result using Twitter information, which is an unexpected result comparing to previous studies.