The Semantic COP Motivation

The number one challenge associated with the entire domain of Information Technology is data overload. This problem is not merely a matter of increasing complexity, because even as other areas within IT are becoming more integrated or manageable, the ability to effectively exploit the increasing quantity of available data is decreasing. There are a number of reasons why this is the case; those reasons include:

·       Traditional data technologies, standards and related solution architectures were simply not designed to handle this much data. Data sources were built to exist as silos and most of them have not changed in principle for about 30 years.

·       The requirements for sharing data across organizations or across the globe is a recent expectation and changes all previous expectations regarding data management, integration and discovery.

·       Unstructured data is now becoming as important as structured data. This adds a nearly infinite curve to potential data growth.  

Several key capabilities are needed to help get control of data overload

For those organizations or individuals charged with performing sophisticated analytics-based tasks, the future will be challenging. While data continues to grow at nearly an exponential rate, the tools and technologies dedicated to making sense of ever-larger data sets is not keeping pace. This is not merely a military or intelligence-focused problem; it affects many commercial domains as well. The fact that this is such a common problem makes the prospect of a solution architecture designed to address this challenge more appealing and commercially viable. Such a solution could be presented both as specific commercial products and a best practice architectural approaches (not unlike SOA or Cloud Computing). While this solution may take advantage of Big Data technologies such as Hadoop it is not primarily a "Big Data" approach.

This challenge requires both a new architectural approach but also requires a new way of managing IT lifecycles to better exploit the emerging technologies which comprise that architecture. Semantic Technology is the key to facilitating a new type of analytics, one that has the power to harness any type of data source or architecture.  The overall solution approach that we will be proposing is what we're referring to as a Semantic Operating Picture. The primary solution elements which build upon the S-COP principles we presented yesterday are as follows:

1. Development of a clear definition the Problem Space and related technical challenges.
2. Conducting applied or directed research into specific areas:
• Dynamic Tagging , Master Data & Metadata Management.
• Current Semantic Standards; focus on RDF, OWL, RIF.
• Semantic-based Visualization technologies; especially those using OWL, RDF.
• Semantic Search and Inference Engine technologies.
• Semantic Lifecycle Management capabilities
• Natural Language Processing (algorithms, strategies etc.)
• Translation of RDBMS or DW structures to Semantic Standards & DBs.
• Semantic-driven reporting capabilities; integration w/ visualization.
3. Identification of multi-modal datasets for use in the prototype solution architecture.

We will explore these in more detail in upcoming posts.

Copyright 2012  - Technovation Talks, Semantech Inc