Unified Data Representation for Structured and Semi-Structured Queries
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Solution Overview
Problem
Traditional database management systems require significant capital investment in hardware and infrastructure, are susceptible to data loss during disasters, and have limited scalability and efficiency in data retrieval and processing.
Innovation Solution
A network-based database system utilizing compute service managers, execution platforms, and metadata-driven micro-partitioning to optimize query execution and resource management, enabling efficient data storage and retrieval across distributed systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional relational database management systems are used, then data can be stored and accessed, but significant capital investment in hardware and infrastructure is required
Solution Approach 1:
The patent replaces traditional mechanical database infrastructure with a cloud-based system that uses virtualized computing resources. Instead of requiring physical hardware investment, the system leverages cloud providers' infrastructure to store and manage data, substituting mechanical hardware management with software-based virtualization and remote access mechanisms.
Solution Approach 2:
The patent introduces a cloud platform as an intermediary between the user and the database system. This intermediary layer provides database services without requiring direct hardware investment or management, allowing users to access data through web browsers or APIs while the cloud provider manages the underlying infrastructure complexity.
2Reliability
If traditional database systems are deployed on-premises, then data can be stored locally, but physical space is required for storing database infrastructure
Solution Approach 1:
The patent substitutes physical storage infrastructure with virtualized cloud storage systems. Data is stored on remote servers accessed through network protocols rather than requiring local physical space, enabling scalable storage capacity without proportional increases in physical footprint.
3Reliability
If traditional database systems are used, then data can be stored, but significant resources are required for capital investment in hardware
Solution Approach 1:
The patent replaces energy-intensive physical hardware maintenance with software-based cloud systems that consume significantly less energy. The cloud provider's optimized infrastructure and virtualization technologies reduce the energy required for data storage and processing compared to traditional on-premises hardware systems.
4Productivity
If traditional relational database management systems are used, then data can be accessed, but limited scalability is achieved
Solution Approach 1:
The patent implements dynamic scalability through cloud-based resource allocation. The system can automatically adjust computing and storage resources based on demand, allowing the database to scale up during peak loads and scale down during quiet periods, providing both high productivity and adaptability.
Solution Approach 2:
The patent creates a universal cloud database platform that can handle diverse data types and workloads through a single system. The cloud infrastructure provides multi-functional capabilities including relational data storage, cloud object storage, and various access methods (web browser, API, mobile applications), enabling the system to adapt to different scalability requirements.
5Reliability
If traditional database systems are deployed, then data can be stored, but highly susceptible to data loss during power outages or disasters
Solution Approach 1:
The patent uses the cloud platform as a protective intermediary that isolates user data from physical disasters. The cloud provider's distributed infrastructure across multiple geographic regions ensures that data is replicated and stored safely, protecting against local power outages, natural disasters, or hardware failures that could affect on-premises systems.
Solution Approach 2:
The patent implements beforehand cushioning through automated backup and disaster recovery mechanisms provided by the cloud platform. Data is continuously replicated across multiple locations and versions are maintained, providing a cushion against data loss before disasters occur and enabling rapid recovery when failures happen.
Data Source
AI summary
The subject technology receives a first semi-structured object. The subject technology iterates through a list of fields specified by a target object type. The subject technology, for each field, determines whether a field with a same name is present in the first semi-structured object. The subject technology, in response to the field being found in the first semi-structured object, converts a value of the field to a target field type according to defined type conversion rules. The subject technology stores the converted value in a unified representation comprising a data structure that stores both structured and semi-structured data types. The subject technology processes a query using the unified representation.