Aikaterini Tzompanaki

PANDORA: A Comprehensive Framework enabling the Delivery of Trustworthy Datasets for Efficient AIoT Operation (EU-HORIZON-CL4-2023-HUMAN-01-CNECT 2024-2027)

As Internet of Things (IoT) and IoT-Edge-Cloud continuum technologies advance, physical environments are becoming increasingly equipped with sensors, fuelling the development of smart space ecosystems. Massive quantities of data produced by IoT devices revolutionize the way such ecosystems operate via the exploitation of AI models/services. This has led to the emergence of the socalled Artificial Intelligence of Things (AIoT) systems. In general, designing techniques to promote robustness, efficiency and continual operation of AIoT systems requires realistic and trustworthy data at scale. However, such data is not always easy to obtain due to the cost of smart space construction, the inconvenience of long-term device tracking, the sensor/knowledge data gaps in diverse scenarios of a smart space, and the restrictions imposed on sensitive data sharing. Furthermore, an efficient AIoT system operation requires trustworthy AI services, as well as novel approaches for speeding up their inference across the IoT-Edge/Cloud continuum. PANDORA aims to devise and implement a comprehensive framework enabling the delivery of trustworthy datasets of smart space ecosystems, as well as the deployment and green operation of AIoT systems in such spaces. PANDORA spans two phases: (1) prior to AIoT system deployment; (2) post AIoT system deployment and operation. Phase 1 proposes and combines a series of novel techniques such as synthetic data generation, quantification of uncertainties, and data summarization for the delivery of trustworthy datasets, as well as explainable AI and domain-informed model training/testing in smart space ecosystems. Phase 2 defines novel AIaaS and CaaS techniques for the robust, explainable, green and continual operation of AIoT systems deployed in such spaces. The trustworthiness and applicability of the PANDORA framework will be tested through five pilot cases hosting AIoT applications in smart buildings, factories and critical infrastructures.

EXPIDA: EXplainable and parsimonious Preference models to get the most out of Inconsistent DAtabases (ANR-AAPG-PRC 2023-2027)

The project EXPIDA aims to develop a series of principled and powerful methods to better analyze and particularly to explain the actions that we can take over uncertain and inconsistent data to get the most out from these data. A prototype system based on symbolic AI to validate the effectiveness of the proposed approaches over the real-world database ASRS will be developped.

Nautilus

Developers often find themselves in front of unexpected results when they apply transformations to source data. In order to overcome this problem they get involved into analyze-fix-test cycles till they can generate the desired results. The goal of Nautilus is to support developers in this process, by providing a suite of algorithms and tools to accompany the process.

I was involved mainly on the Explanation Manager Component, that aims to support the Analyze phase. Particulary, I worked on the 'why-not provenance' which provides explanations about results that were expected but are missing from the result set.

3D-COFORM:Tools and Expertise for 3D Collection Formation

The 3D-COFORM consortium has one over-riding aim: to establish 3D documentation as an affordable, practical and effective mechanism for long term documentation of tangible cultural heritage. The project addresses all aspects of 3D-capture, 3D-processing, the semantics of shape, material properties, metadata and provenance, integration with other sources (textual and other media); search, research and dissemination to the public and professional alike.

I was practically involved in the design and documentation phase of the project, concerning the repositories (Object and Metadata Repositories). Later, I worked on the implementation of the Metadata Repository and the Repository Infrastructure (RI) as a software engineer. As a research assistant, I worked on the "Query Formulation Component" of the "Integrated Viewer and Browsing Component" building also complementary software.