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

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Ontology-based context management for mobile devices

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Contribution to the elaboration of a decision support system based on modular ontologies for ecological labelling

With the rising concern of sustainability and environmental performance, eco-labeled products and services are becoming more and more popular. In addition to the financial costs, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. In this research work, we propose a decision support process and implement a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels. The decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to official eco-label criteria documentation. Traditional knowledge base in relational data model is low interoperable, lack of inference support and difficult to be reused. In our research, the knowledge base composed of interconnected ontologies modules covers various products and services, and allows reasoning and semantic querying. A domain-centric modularization scheme about EU Eco-label laundry detergent product criteria is introduced as an application case. This modularization scheme separates the entity knowledge and rule knowledge so that the ontology modules can be reused easily in other domains. We explore a reasoning methodology based on inference with SWRL (Semantic Web Rule Language) rules which allows decision making with explanation. Through standard RDF (Resource Description Framework) and OWL (Web Ontology Language) ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other application. In order to foster the reuse of ontology modules, we also proposed a usercentric approach for federate contextual ontologies (mapping and integration). This approach will create an ontology federation by a contextual configuration that avoid the “OWL:imports” disadvantages. Instead of putting mapping or new semantics in ontology modules, our approach will conserve the extra contextual information separately without impacting original ontologies or without importing all ontologies’ concepts. By introducing this contextualization, it becomes easier to support more expressive semantics in term of ontology integration itself, then it will also facilitate application agents to access and reuse ontologies. To realize this approach, we elaborate a new plug-in for the Protégé ontology editor

Mechanical design automation: a case study on plastic extrusion die tooling

The Skills Gap in Mechanical Engineering (ME) Design has been widening with the increasing number of baby boomers retiring (Silver Tsunami) and the lack of a new generation to acquire, practice and perfect their knowledge base. This growing problem has been addressed with several initiatives focused on attracting and retaining young talent; however, these types of initiatives may not be timely for this new group to be trained by an established Subject Matter Expert (SME) group. Automated Engineering Design provides a potential pathway to address not only the Skills Gap but also the transfer of information from SMEs to a new generation of engineers. Automation has been at the heart of the Advanced Manufacturing Industry, and has been successful at accomplishing repetitive tasks with processes, software and equipment. The next stage in Advanced Manufacturing is further integrating Machine Learning techniques (Artificial Intelligence (AI)) in order to mimic human decision making. These initiatives are clear for the type of mechanized systems and repetitive processes present in the manufacturing world, but the question remains if they can be effectively applied to the decision heavy area of ME Design. A collaboration with an industry partner New Jersey Precision Technologies (NJPT) was established in order to address this question. This thesis presents an ME Design Automation process involving a multi-stage approach: Design Definition, Task Differentiation, Workflow Generation and Expert System Development. This process was executed on plastic extrusion tooling design. A Computer Aided Design (CAD) based Expert System was developed for the Automation of design, and the generation of a database towards future Machine Learning work. This system was run on 6 extrusion product examples previously designed by NJPT through traditional methods. The time needed to generate the design was reduced by 95-98%. This thesis demonstrates the capability of automating ME design, the potential impact in industry and next steps towards the application of AI

Third Conference on Artificial Intelligence for Space Applications, part 1

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed

Design of a Rule Based System to Assign Components to Drive Maps

Questo lavoro di tesi s’inserisce all’interno del macroprogetto Leanergie portato avanti dall’università di Hannover nel campo delle macchine utensili, con lo scopo di prevedere già durante la fase di progettazione il consumo di energia della macchina a seconda dello scenario applicativo, in modo da poter pianificare la miglior combinazione dei diversi componenti in termini di consumo di energia. La previsione del consumo di energia, a differenza degli altri metodi basati su modelli matematici e simulativi, è basata su informazioni empiriche acquisite durante il funzionamento della macchina. Questo lavoro si è occupato in particolare di permettere una previsione del consumo di energia tutte le volte in cui non è possibile avere informazioni empiriche su un dato componente, ad esempio quando non è possibile estrarre dati operativi o quando sono presenti nuovi componenti che non hanno mai avuto un applicazione industriale. Per ottenere tali risultati, è stato sviluppato un concetto basato sulla Fuzzy Logic che assegni ad ogni componente privo di informazioni empiriche un diagramma che approssimi il suo funzionamento e il suo consumo di energia nella maniera più precisa possibile