B. Sc. Kiril Tonev

The key success factor for effective energy efficiency initiatives at community level is to involve all the stakeholders who have an active role in the decision making and provide them with the right information at the right time to take informed decisions; Stakeholders (i.e. citizens, household landlords, public spaces managers and urban planners) need tools and information to understand and assess alternatives to traditional consumption patterns and inertial behaviors leading to high consumption levels. The information used as the foundation of decision making comes from different data sources and systems. In order to provide each stakeholder with useful and consistent information an integration concept has to be developed.

Researchers have been developing ontologies to support semantic integration of different information systems, especially in companies. The challenge in integration is that usually the information provided by the data sources or existing IT systems does not meet the quality requirements of the tools which should work on it.

Objective:

The main objective of this diploma thesis is to develop a concept of a semantic data integration using ontologies for improving energy efficiency in a smart city environment and to evaluate whether the provided data fulfil the quality requirements of the tools that work on the integrated data.

Work content:

This thesis consists of the following work:

1. To analyze the requirements of the integration approach. This task examines the information infrastructure required to support the intelligent energy management in smart cities. In particular, it analyzes the relevant information domains of energy consumption and distribution, geography and demographics, their data modelling approaches, existing vocabularies and common exchange formats found in the literature and industry. The findings of this study serve to define the software requirements specification (SRS) which includes functional and non-functional requirements that establish the evaluation framework against which the methodology will be tested.
2. To analyze the state of the art in semantic integration, to identify the strengths and weaknesses of each approach with respect to the identified requirements. The analysis should cover the following:
   • Link discovery methods (e.g. similarity metrics, heuristics, classification algorithms)
   •  Automatic or semi-automatic techniques for semantic annotation and disambiguation
   • System architecture aspects, in particular data retrieval efficiency and fitness for timely data analysis. Scalability and resilience aspects should also be discussed.
3. Development of an OWL ontology to data source mapping using a suitable integration strategy. In order to take advantage of the formal semantics of the language, the student needs to have strong background in predicate logic and description logics to perform this task (cp. Diploma lecture “Formale Systeme”).
4. Development of integration architecture including their components and interface. The student needs to have knowledge in different software architectures and software engineering methods (cp. Diploma-Module “Softwaretechnik”).
5. Development of tools that automatically evaluate the achieved integration with respect to the consistency and completeness of the merged data. The tools should take into consideration the assertions in the ontology to test the data with.
6. Verification of the developed concept.
7. Documentation and presentation of the results.