  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 abstraction and summarization. In this work,
we present a generic model for personalized multilevel exploration and analysis
over large dynamic sets of numeric and temporal data. Our model is built on top
of a lightweight tree-based structure which can be efficiently constructed
on-the-fly for a given set of data. This tree structure aggregates input
objects into a hierarchical multiscale model. Considering different exploration
scenarios over large datasets, the proposed model enables efficient multilevel
exploration, offering incremental construction and prefetching via user
interaction, and dynamic adaptation of the hierarchies based on user
preferences. A thorough theoretical analysis is presented, illustrating the
efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and
analysis over Linked Data datasets.
