1. Planning and defining a problem statement: Using business knowledge from multiple sources developing an understanding of the problem and solution. Identifying various entities relationships and behaviors in various tasks involved. Behavior relates to changes in the state of the system. 2. Defining Functional and non-functional requirements along with policies and constraints that manage deviations from expected behavior when they occur: Functional requirements define various entities their relationships and behaviors involved in executing the functional workflow. Non-functional requirements describe the structure of a computer network that provides the resources and the workflows to monitor and manage structure when deviations occur from the expected functional or structural behavior.

This phase focuses on an in-depth analysis of the application requirements for a video streaming platform adaptable to both human-like and alien digital intelligences. 1. Problem Statement Development & Entity Analysis: Understanding Problem Context: Conducting a thorough exploration of user expectations content types & interaction methods anticipating varied intelligences' perception & processing media. Entity & Relationship Identification: Defining the entities (e.g. user profiles media content types streaming sessions) along with relationships & interdependencies (e.g. user-to-content interactions session-to-network dependencies). Behavior Analysis & State Mapping: Mapping the expected behaviors of these entities understanding how the system's state fluctuates with actions like play pause buffer and adapt. 2. Requirements Definition: In this deliverable the focus is on detailed specification of Functional & Non-Functional Requirements (NFRs) shaping the platform's workflow & resilience under different operating conditions. Functional Requirements: Identifying & describing the primary actions of each entity such as content retrieval adaptive bitrate streaming user authentication & personalized content recommendations. Each functional workflow will cover the entity's role behavior & interaction sequences to ensure seamless content delivery. Non-Functional Requirements (NFRs): Sustain Structural Integrity. Policies & Constraints for Deviation Management.

$8,000 USD

3. Modeling the schema: Using a graph database defining the nodes representing various entities with the necessary attributes and the algorithms that change the state when events occur. In essence each attribute contains a name with value or a link to a process that provides the value using an algorithm. Each node called a knowledge structure is translated into its own containerized software service. Functional requirement processes are defined and executed by the algorithms. Non-functional requirements are implemented to manage the resources in the cloud environment and ensure a stable state of expected behavior. All connected nodes share knowledge through API to create a network of communication that reflects the vertex and edge relationship in our schema. 4. Each node is deployed as a service with inputs a process execution engine executing the workflow defined in the knowledge structure node and outputs that communicate with other knowledge structures using shared knowledge between the knowledge structures. A knowledge network thus comprises a hierarchical set of knowledge structures (nodes) executing various processes that are activated by inputs and communicating with other knowledge structures using their shared knowledge. Wired nodes fire together to perform the functional and non-functional requirements and policy constraints that keep the system steady safe and secure while fulfilling the mission without disruption.

1. Graph-Based Schema Modeling & Knowledge Structure Design: This phase focuses on constructing a dynamic schema using a graph database to represent various entities (e.g. users media content session states) as nodes each encapsulated within a self-sufficient knowledge structure. Defining Nodes & Attributes: For each entity we will create nodes with essential attributes defining values or processes for each attribute. Algorithms embedded in each node will dynamically manage the entity's state in response to events (e.g. play) Containerization of Knowledge Structures: Translating each node into a containerized microservice or "knowledge structure" ensuring that each functional unit operates independently yet cohesively within the larger system. Functional & Non-Functional Process Execution: Implementing functional processes through algorithms that handle primary streaming operations (content retrieval playback management) and non-functional requirements for resource allocation & error resilience in the cloud environment. Knowledge Network & API Communication: Defining the inter-node communication framework using API's to create a network mirroring the graph's vertex-edge relationships. 2. Service Deployment & Hierarchical Knowledge Network: In this deliverable each node's containerized service is activated processing inputs & executing workflows within the knowledge structure. Communication between nodes will ensure synchronization & stability across the system.

$10,000 USD

5. The policies are implemented using agents called “Cognizing Oracles” that monitor the system's structure and function as it evolves detect deviations from the expected behavior and take corrective actions. 6. As the system evolves the phase space (the system state and history) is captured in the graph database as associative memory and interaction history. These provide a single point of truth for the system to reason and act using the cognizing oracles. Knowledge about the phase space is captured and represented in the associative memory and interaction history.

1. Cognizing Oracle Policy Implementation for System Stability: This phase introduces a layer of intelligent agents referred to as "Cognizing Oracles" designed to continuously monitor and regulate system structure and function ensuring adaptive stability as the application evolves. These agents operate with real-time insights to detect and respond to deviations from expected behavior thus maintaining a seamless experience for users. Agent-Based Monitoring & Deviation Detection: Developing and deploying Cognizing Oracles each programmed to track critical operational metrics and structural integrity indicators. These agents use predefined policies to identify and analyze deviations in system behavior such as unexpected latency spikes resource anomalies or content buffering issues. Corrective Action & Adaptive Response Mechanisms: Implementing corrective algorithms within each Cognizing Oracle enabling them to autonomously initiate recovery actions. 2. Associative Memory & Interaction History Capture: This deliverable focuses on establishing a robust phase space which represents the system's evolving state and historical interactions. The phase space is recorded within the graph database as an associative memory which serves as a repository of knowledge on the system's operational history and decision-making logic.

$12,000 USD