This paper presents the scientific foundation of the ADBIS 2025 tutorial on graph analytics for estimating reciprocity in intellectual communities using property graphs. By integrating Natural Language Processing (NLP), graph analytics, and embeddings, we analyse culturally rich datasets—such as Wikipedia entries and literary corpora from Latin America and Eastern Europe—to uncover epistemic violence, highlight marginalized intellectual contributions, and advance decolonial approaches to information systems.
This paper summarises the fundamental background of the Doctoral Consortium titled “Ethical and Equitable Data Science”. It introduces Freda, a methodology for designing ethical, frugal, and equitable data and algorithm-driven science. It bridges technical innovation with social justice by integrating data sovereignty, fairness-aware analytics, and community-in-the-loop infrastructure. Rooted in decolonial and feminist perspectives, Freda addresses transparency, accountability, and epistemic diversity through policy-aware Spark pipelines, federated learning, and negotiated resource dispatching. A case study illustrates how sovereignty-aware pipelines enable community control, minimize extractivism, and embed plural, justice-centered values into AI systems.
Interactive time series visualization is essential in domains like IoT monitoring but is often constrained by latency and scalability challenges. Various methods have been proposed to address these issues, each with different trade-offs between efficiency, interactivity, and visualization accuracy, making systematic evaluation crucial. Traditional benchmarking approaches, however, fail to capture user-perceived responsiveness and accuracy in real-world exploration scenarios. To bridge this gap, we introduce TimeVizBench, an interactive evaluation platform for scalable time series visualization methods across performance and accuracy dimensions. TimeVizBench enables users to configure different methods, explore visual outputs interactively, and dynamically assess performance and accuracy. It also provides a standardized interface for integrating and comparing additional methods.
Retrieval-augmented generation (RAG) improves the performance of LLM-based applications by incorporating information from external knowledge bases. However, the introduction of search engines and knowledge sources can also introduce new biases and stereotypes into the system. Previous studies have shown that adjusting the bias of retrievers through fine-tuning can influence the overall bias of the RAG system, mitigating bias in RAG. In this work, we propose a re-ranking-based method, termed ReFaRAG, as an alternative to fine-tuning for controlling the bias in retrieval results. We further investigate how biased retrieval output affects different LLMs within the RAG framework.
