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Our Technology

A3P Configurations

A3P was designed to be agile and adaptable to specific customer analytical requirements. This agility allows for capabilities to be removed or modified to fit very specific needs. Most modifications are made by Oracle at our manufacturing facilities .

A3P Expansion

As analysts start to realize the positive effect of the impact to mission from A3P, expansion might become necessary. Expansion requirements will also be driven by data growth, increasing user demands and future algorithm development.

Applications

Since integration, interoperability and avoidance of technology lock-in is always a prime objective, the Applications tier utilizes standards whenever possible. As you see in Figure 5 below, different data types have standards e.g., XML, SPARQL and OGC. There also exist protocol standards that the Applications tier should adhere to e.g., JDBC, REST, and SOA Web Services

Data Services

The data services depicted describe the general capabilities needed for many if not most data. The service titles are self-descriptive. Note however there is a critical reliance on the proven ability of the hardware to help efficiently support these capabilities.

Agile Analytic Sandbox

A3P embraces a concept called Sprint Analytics. Sprint Analytics supports the need for data scientists and analysts to try new tools, algorithms, and experimentation on data to improve prediction and mission results. In order to accomplish either success or retrial quickly, A3P includes an agile sandbox for creating development, test or an operational application hosting environment for the A3P users. This agility is delivered through the Oracle Virtual Compute Appliance

What We Do

Large Data Management

Data growth is a constant in today’s analytical environments. Evolving data storage requirements can expose your analytical mission to performance risk if your solution doesn’t give you the flexibility to keep up, no matter how fast data is growing. In some cases data sets in an analytical environment age, and are less useful over time. These data sets use precious disk storage resources but in many cases the data set can’t be thrown away.

High Performance Storage

Analytic workload variety necessitates the need for performance-oriented storage. Data sets such as streaming video, imagery, spatial data, social media data sets and large documents all need a location to reside for preprocessing, staging, transformation and archiving.

Hybrid Approach

There’s no reason to think the choice between on-prem and cloud is a mutually exclusive one. Customers can pick and choose what works best for them. Maybe you want to keep your email system on-prem, but leverage a cloud-based service for message hygiene, continuity or archiving. Or perhaps you will deploy your domain controllers and file and print servers on-prem, but build application and database servers in the cloud using an Infrastructure as a service provider who can provide you with computing resources for far less than you can do on your own.