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Relational database
technology (RDB) has revolutionized how
companies manage and mine for information
over the last 30 years. Experts estimate
that less than 50% of a corporation’s data
resides in this format. Interpreted data or
“value added information” is even less
likely to be in a RDB. It tends to reside in
Word documents, PowerPoint slides, PDF’s,
Excel spreadsheets, and a host of other
“unstructured” formats. |
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Keyword search
technology like Google has been one of the
great technology advances in the past
decade, empowering users by delivering
information from an infinite array of
external sources. |
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However, for
most oil and gas
companies,
valuable
“interpreted”
data usually
resides “buried”
on internal LANs
making it easier
to search the
Web than find
unstructured
data within the
organization. |
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As a result,
knowledge is often left undiscovered and
potential opportunities are lost. |
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petroWEB’s KeyWord Search Plugin is
built on the industry’s best Enterprise
Search platforms using either Microsoft
Search Server or Google’s Search Appliance,
and customized to meet the specific
requirements of the oil and gas industry. |
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Keyword Search can merge the results from
internal data sources with external sources.
This provides companies with a federated
keyword search structure. The external data
sources can include any number of Search
sites like Yahoo, news content and industry
specific sites, and data content
subscription sites such as AAPG’s DataPages. |
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These results can be
seamlessly integrated with the results of
one or more |
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internal search
engines (perhaps from remote business
units). Users can indicate which sources to include in the
search to support both broad based and
targeted, detailed searches. Results are
returned in a grid which can be filtered,
sorted or even pivoted for better analysis. |
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initiated from: |
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Simple Keyword Interface |
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Map Selection on a GIS |
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Hierarchical Navigation in a Tree
Structure |
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Traditional SQL type search |
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Results from the Keyword Search can also be
flashed or displayed by on the Map to
identify which documents are related to
certain spatial objects. |
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For example, a user
might identify a group of wells on a map
through a selection and then apply a keyword
search such as “logs” to narrow the search.
A list of documents will be returned for
each well which contain the keyword “log”.
As the user moves from well to well, the
well will flash on the map and the list of
documents associated with that particular
well will be returned providing the user
with both a list of relevant documents and a
spatial context of the results. |
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