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Thomas Heinis

Researcher at Imperial College London

Publications -  85
Citations -  2132

Thomas Heinis is an academic researcher from Imperial College London. The author has contributed to research in topics: Computer science & Data management. The author has an hindex of 14, co-authored 69 publications receiving 1748 citations. Previous affiliations of Thomas Heinis include École Polytechnique Fédérale de Lausanne & MIND Institute.

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Journal ArticleDOI

Reconstruction and Simulation of Neocortical Microcircuitry

Henry Markram, +92 more
- 08 Oct 2015 - 
TL;DR: A first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat is presented, finding a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms.
Proceedings ArticleDOI

Efficient lineage tracking for scientific workflows

TL;DR: This paper proposes an alternative approach to storing lineage information captured as a workflow process using a space and query efficient interval representation for dependency graphs and shows how to transform arbitrary workflow processes into graphs that can be stored using such representation.
Proceedings ArticleDOI

Design and Evaluation of an Autonomic Workflow Engine

TL;DR: The design and performance of an autonomic workflow execution engine, which features self-configuration, self-tuning and self-healing properties, is presented and different autonomic control strategies are compared and discussed.
Journal ArticleDOI

Developing scientific workflows from heterogeneous services

TL;DR: The SODIUM platform is presented, consisting of a set of languages and tools as well as related middleware, for the development and execution of scientific workflows composed of heterogeneous services.
Proceedings Article

Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

TL;DR: ViDa is built, a system which reads data in its raw format and processes queries using adaptive, just-in-time operators, and features a language expressive enough to support heterogeneous data models, and to which existing languages can be translated.