What is Jumper?
Jumper is a set of semantic tools. It can be implemented alongside traditional storage engines including Oracle, DB2, MSSQL, mySQL, and PostgreSQL to help relieve the strain caused by trying to integrate data derived from multiple models and to enable more complex querying. The Jumper solution is based W3C Semantic Web standards that have been specifically adapted for structured data. Our semantic technology enables life science organizations to become more streamlined and effective in their R&D, allowing scientists to quickly make the right decision about targets, discover the knowledge about those targets, the linked associations between those targets, and to increase the volumes of data they can effectively process.

What does Jumper do?
Our innovative semantic technology delivers:

• Knowledge Base - capture accumulated knowledge about the data including annotations, controlled vocabularies, and technical metadata.
• Semantic Integration - flexible semantic models allow rapid integration of fragmented public biological data into any target system.
• Linked Data - link protein-to-protein dependencies or independencies between columns, tables and databases.
• Query Routing - ask complex, ad-hoc queries of your data, across multiple datasets and systems without common models.
• Auditing & Tracking - specify source, track changes, define workflows for determining data quality, validity, and freshness.

How to deploy Jumper?
Jumper is fully integrated with the mySQL open-source database for an enterpise-level data warehouse solution.
Traditional data warehouses and ETL tools are poorly equipped to deal with multiple models, or dynamic models that change rapidly over time. The Jumper solution was developed for a project that was specifically tasked with meeting this common challenge faced when aggregating online biological data. The mySQL database is an enterprise class batch transaction platform and the Jumper semantic technology easily manages the multiple different models of each data source.

Jumper can also be deployed to quickly federate a set of distributed project-level, single-study, or bench-side databases. Jumper provides an innovative method of federating databases. With Jumper you can federate literally thousands of databases. Jumper does not rely on a common model to federate databases, but instead utilizes a common language. This OWL based language captures the knowledge about each database table so that a search delivers very targeted results.

How to use Jumper?
Powerful Jumper semantic technology allows you to aggregate and interpret any structured data and rapidly combine this data into new target systems. Bioinformatics data is currently spread across the Internet and throughout organizations in a wide variety of formats. Current solutions in the life sciences for aggregating all this research data available from publicly sources is to build internal data warehouses. Traditional data warehouses significantly limit your flexibility and productivity. A warehouse is not ideally suited for demand-driven, often highly-fragmented, scientific data.

• A typical research warehouse can integrate only a subset of the massive amount of publicly available data that is deemed to be of greatest interest.
• Warehouse models are very static and it has proven difficult to add new data sources to the warehouse at a later point.
• Further, advances in scientific knowledge require regular changes to be made to the underlying data models, and this is not straightforward with a relational model.
• Organizations that use this approach also typically face challenges with representing data that is at different levels of abstraction, and that includes data of very different quality.

How to Benefit from Jumper?
Fast, efficient, and successful data integration is one of the keys to improved productivity in biopharmaceutical R&D. Success in most bioinformatics-related activities requires rapid integration of all relevant data often from publicly available databases.

• With Jumper you easily manage informatics workflows; combine data from multiple columns located in different tables, or combine multiple tables located in different databases, or manage multiple conversions of the same data as it is processed by different systems using flexible semantic models.
• With Jumper you can also build an extensive knowledge base, including detailed information about what the data means, how it is structured, where and how it was derived, and what changes have been made to the data using semantic web profiles.
• With Jumper you can ask complex queries of the data, including similarity searches, association searches, procedural searches, and conditional searches all enabled on the same database using semantic methodologies and novel indexes.