Eleni Tzirita Zacharatou

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Core to many scientific and analytics applications are spatial data capturing the position or shape of objects in space, and time series recording the values of a process over time. Effective analysis of such data requires a shift from confirmatory pipelin ...

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, wher ...

The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower d ...

The majority of spatial processing techniques rely heavily on the idea of approximating each group of spatial objects by their minimum bounding box (MBB). As each MBB is compact to store (requiring only two multi-dimensional points) and intersection tests ...

Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...

Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-in-polygon tests that associate data points to polygonal regio ...

Advances in data acquisition—through more powerful supercomputers for simulation or sensors with better resolution—help scientists tremendously to understand natural phenomena. At the same time, however, it leaves them with a plethora of data and the chall ...

Eleni Tzirita Zacharatou

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Efficient Query Processing for Spatial and Temporal Data Exploration

RUBIK: Efficient Threshold Queries on Massive Time Series

Interactive Visual Exploration of Spatio-Temporal Urban Data Sets using Urbane

Improving Spatial Data Processing by Clipping Minimum Bounding Boxes

Efficient Bundled Spatial Range Queries

GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons

Space Odyssey - Efficient Exploration of Scientific Data