SEARCH
Jumper allows you to ask complex, ad-hoc, cross-domain questions of databases,
spanning multiple data domains, and even organizations. Jumper empowers researchers to query or view thousands of databases
simultaneously from a single interface regardless of data format, schema, or location. With Jumper you can analyze and
filter information in real-time and adapt queries on the fly. Jumper does not search the data records directly, but instead
searches an abstract representation of the data. It allows you to search for the knowledge contained in the database, not
just the records. For instance, you would not search for a specific ‘test ID’ record, but instead would search for ‘glucose
tests’ and would receive a list of all database tables containing glucose test results. As a result queries can be tightly
focused and return very targeted results.
ACCESS
Jumper provides an automated method of extracting the table names, column names, and
profile metadata from any database. You simply point the Jumper Index at the database and it extracts the relevant metadata
and captures it in the JUMP-OWL format. Access is via JDBC and both the metadata and the actual records can be extracted
using the Jumper tools. It connects to any relational database using a standard JDBC-Connection and mediates between the
relational and the semantic context. It converts a database schema automatically into a JUMP-OWL ontology and presents a
holistic representation of both the column names as well as the physical and technical metadata that define the columns.
INTERPRET
Jumper delivers any structured data for qualitative and quantitative analysis. Each
database table is categorized and advertised using a classification system that leverages your master data efforts, and a
set of keyword descriptors to provide focused search capabilities. A search returns a JUMP-OWL file that describes the data
in the table. The JUMP-OWL file captures both a full data profile of each table and a knowledge description of the data
contained in each table so the data can be easily interpreted. This file allows you to understand the exact context and
definition of the data contained in the table so you can quickly and easily understand what the data means, how it is
defined, and how it can be used.
INTEGRATE
Jumper also provides innovative capabilities to ensure transactional synchronization
of data across data domains. It allows you to share and reuse existing data by establishing mappings among different
ontological entities. JUMP-OWL identifies equivalent metadata tags between different schemas. It does this by providing a
higher-layer set of semantic objects. JUMP-OWL defines two uses of OWL equivalence to express these relationships. The
first is the ‘sameValueAs’ predicate which identifies equivalence between the column name and master data or industry
standards. The second is the ‘sameAs’ predicate which defines a mapping between source and target metadata. The Semantic
Model (smodel) shows how the components of data are related to one another in the context of metadata equivalence allowing
you to automate data conversion.
| 
