The development of T-Storm was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
T-Storm is a platform for supporting scalable real-time analytics of massive sets of voluminous time-series. The platform is constructed over the Storm parallel dataflow engine, and supports both vertical scalability (fully utilizing high-end servers and multi-core systems) and horizontal scalability (scaling across a cluster of physical machines or even incorporating virtual cloud resources). The current version enables efficient maintenance of the highly correlated time-series in linear space and near-linear computational complexity (in practice, computational complexity depends on the input time-series). This functionality is, for example, useful to identify the pairs of neurons that fire in a correlated manner.
T-Storm is distributed as a prepared virtual machine. To use the platform, the user needs to 1) deploy and configure the required number of virtual machines, depending on the number of time series to monitor, and their velocity; 2) configure the virtual machines so that they have network access and can talk to each other; 3) provide the input in the documented format. Full instructions are provided with the virtual machine.
|Date of release||April 2015|
|Version of software||0.1|
|Version of documentation||0.1|
|Responsible||Minos Garofalakis (firstname.lastname@example.org)|
|Requirements & dependencies||Java (JDK>=7) (https://www.oracle.com/java/index.html)|
Storm parallel dataflow engine (https://storm.apache.org)