Model Driven Development Applied to Complex Event Processing for Near Real-Time Open Data

Nowadays, data are being produced like never before because the use of the Internet of Things, social networks, and communication in general are increasing exponentially. Many of these data, especially those from public administrations, are freely offered using the open data concept where data are published to improve their reutilisation and transparency. Initially, the data involved information that is not updated continuously such as budgets, tourist information, office information, pharmacy information, etc. This kind of information does not change during large periods of time, such as days, weeks or months. However, when open data are produced near to real-time such as air quality sensors or people counters, suitable methodologies and tools are lacking to identify, consume, and analyse them. This work presents a methodology to tackle the analysis of open data sources using Model-Driven Development (MDD) and Complex Event Processing (CEP), which help users to raise the abstraction level utilised to manage and analyse open data sources. That means that users can manage heterogeneous and complex technology by using domain concepts defined by a model that could be used to generate specific code. Thus, this methodology is supported by a domain-specific language (DSL) called OpenData2CEP, which includes a metamodel, a graphical concrete syntax, and a model-to-text transformation to specific platforms, such as complex event processing engines. Finally, the methodology and the DSL have been applied to two near real-time contexts: the analysis of air quality for citizens’ proposals and the analysis of earthquake data

Development of Semantics-Based Distributed Middleware for Heterogeneous Data Integration and its Application for Drought

ThesisDrought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. Also, since the dawn of modern science, different drought prediction and forecasting models/indices have been developed which usually incorporate data from sparsely located weather stations in their computation, producing less accurate results – due to lack of the desired scalability in the input datasets. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of ethnoecology, agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas of the KwaZulu-Natal province of South Africa and Mbeere District of Kenya. For the study areas, the local indigenous knowledge on drought gathered from the domain experts and local elderly farmers, is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases. The plethora of sub-systems in the semantic middleware produce a service(s) as a combined output – in the form of drought forecast advisory information (DFAI). The DFAI as an output of the semantic middleware is disseminated across multiple channels for utilisation by policy-makers to develop mitigation strategies to combat the effect of drought and their drought-related decision-making processes