A Hierarchical Aggregation Framework for Efficient Multilevel Visual Exploration and Analysis
Nikos Bikakis, George Papastefanatos, Melina Skourla, Timos Sellis

TL;DR
This paper introduces a hierarchical aggregation framework for scalable, multilevel visual exploration of large, dynamic datasets, enabling efficient, personalized analysis through a lightweight tree structure and real-time adaptation.
Contribution
It presents a novel on-the-fly hierarchical model for multilevel data exploration, incorporating dynamic hierarchy adaptation and efficient aggregation for big data visualization.
Findings
Efficient on-the-fly construction of hierarchical models.
Supports personalized, multilevel exploration with dynamic hierarchy adaptation.
Demonstrated effectiveness through a web-based prototype tool.
Abstract
Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information…
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