The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio–Mouse–Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio–Mouse–Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors.

Retrieval-Augmented Generation (RAG) systems increasingly rely on vector databases to retrieve relevant context using multiple embedding criteria. In such workloads, query processing typically performs independent approximate nearest neighbor (ANN) retrieval per criterion followed by candidate fusion. A key challenge is determining how many candidates to retrieve per criterion: retrieving too many increases execution cost, while retrieving too few risks degrading result quality. In practice, the absence of a principled strategy forces systems to over-retrieve, inflating production costs, or under-retrieve, leading to missed relevant results and reduced user satisfaction. Existing systems address this trade-off using heuristic or fixed retrieval depths, lacking a principled mechanism to balance efficiency and effectiveness. In this paper, we propose an adaptive top-𝐾 pushdown vs pull-up optimization framework for multi- embedding query processing. We formalize query execution as a two-stage pipeline and identify per-criterion retrieval depths as key optimization variables governing both execution cost and retrieval quality. We further introduce an execution-driven cost–quality estimator and formulate a constrained optimization problem solved using linear programming to minimize execution cost while preserving retrieval quality guarantees. Experiments on the MovieLens dataset using pgvector show up to 97× speedup while preserving high quality (98% relevance) within the top 40% pushdown.