Publications de Farhan Tauheed

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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 ...

Scientists in many disciplines have progressively been using simulations to better understand the natural systems they study. Faster hardware, as well as increasingly precise instruments, allow the construction and simulation of progressively advanced mod ...

Researchers in several scientific disciplines are struggling to cope with the masses of data resulting from either increasingly precise instruments or from simulation runs on ever more powerful supercomputers. Efficiently managing this deluge of data has b ...

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse ex ...

It is increasingly common for domain scientists to use computational tools to build and simulate spatial models of the phenomena they are studying. The spatial models they build are more and more detailed as well as dense and are consequently difficult to ...

Efficient spatial joins are pivotal for many applications and particularly important for geographical information systems or for the simulation sciences where scientists work with spatial models. Past research has primarily focused on disk-based spatial jo ...

Many scientific and geographical applications rely on the efficient execution of spatial joins. Past research has produced several efficient spatial join approaches and while each of them can join two datasets, the problem of efficiently joining two datase ...

Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simu ...

The performance of spatial joins is becoming increasingly important in many applications, particularly in the scientific domain. Several approaches have been proposed for joining spatial datasets on disk and few in main memory. Recent results show that in ...

Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to ...

Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio amo ...

Advances in observation instruments and abundance of computational power for simulations encourage scientists to gather and produce unprecedented amounts of increasingly complex data. Organizing data automatically to enable efficient and unobstructed acces ...

Scientists in many disciplines use spatial mesh models to study physical phenomena. Simulating natural phenomena by changing meshes over time helps to understand and predict future behavior of the phenomena. The higher the precision of the mesh models, the ...

Neuroscientists increasingly use computational tools to build and simulate models of the brain. The amounts of data involved in these simulations are immense and the importance of their efficient management is primordial. One particular problem in analyzin ...

Scientists in all disciplines increasingly rely on simulations to develop a better understanding of the subject they are studying. For example the neuroscientists we collaborate with in the Blue Brain project have started to simulate the brain on a superco ...

Farhan Tauheed

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RUBIK: Efficient Threshold Queries on Massive Time Series

Spatial Data Management Challenges in the Simulation Sciences

Enabling Scientific Discovery Via Innovative Spatial Data Management

Reconstruction and Simulation of Neocortical Microcircuitry

Accelerating Spatial Range Queries

TOUCH: In-Memory Spatial Join by Hierarchical Data-Oriented Partitioning

GIPSY: Joining Spatial Datasets with Contrasting Density

SCOUT: Prefetching for Latent Structure Following Queries

Configuring Spatial Grids for Efficient Main Memory Joins

Computational Neuroscience Breakthroughs Through Innovative Data Management

TRANSFORMERS: Robust Spatial Joins on Non-Uniform Data Distributions

Challenges and Opportunities in Self-Managing Scientific Databases

OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets

Scalable Exploration of Spatial Data in Large-Scale Scientific Simulations

Accelerating Range Queries For Brain Simulations

Data-driven Neuroscience: Enabling Breakthroughs Via Innovative Data Management

THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads