Uses Datacutter framework (written in c++, tested on unix platform)

Useful Grid/Database concepts presented in the journal articles.

From Ohio state uni.

The objective of STORM is to enable execution of SQL-like SELECT queries (And provides filters) on datasets stored in files distributed across a network. STORM provides support for:

Selection of the data of interest. The data of interest is selected based on either the values of particular attributes or ranges of attribute values (i.e., range queries). The selection operation can also involve user-defined filtering operations.

Transfer of data from storage nodes to compute nodes for processing. If the client program runs on a parallel machine, STORM supports application-specific partitioning and parallel transfer of data elements to the destination processors.

Data Virtualization: STORM

Large data querying capabilities, layered on DataCutter (Example uses TB SAN(Storage area networks)

Distributed data virtualization.

Indexing, Data Cluster/Decluster, Parallel Data Transfer.

Support a basic SQL Select query with a virtual relational table view or a virtual XML database view.

A lightweight layer on top of datasets.

STORM  runtime middleware STORM carries out query execution, query planning.

Compiler front end customizes runtime support.

Automatic customization and configuration of runtime query support middleware.

Diagrams:

Diagram representing an abstraction of a STORM execution

Storm extractor

STORM defines a default binary extractor. The default extractor will handle the extraction of binary data from an uncompressed file in which data is arranged in record-style, sequential blocks.

Disadvantages:

Designed for Unix environment.

No specific web support.

Designed for datasets.