Binary Data Encoding for Storage Reduction and Real-Time Analytics
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Solution Overview
Problem
Current systems face challenges in efficiently processing and analyzing large volumes of structured and unstructured data to derive insights and present actionable information, particularly in real-time and across distributed platforms.
Innovation Solution
The IDAP platform employs a distributed, in-memory computing system with map/reduce operations and faceted search, converting data into a compact binary format for efficient storage and querying, allowing for fast ad-hoc querying and insight discovery across social media and other data sources without requiring exemplars or templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in traditional formats, then storage capacity is sufficient, but storage needs increase significantly
Solution Approach 1:
The patent transforms data from traditional text/format representations into binary representations, fundamentally changing the parameter of data encoding. This conversion reduces the space required to represent the same information, directly addressing the contradiction between maintaining storage capacity and reducing storage needs.
Solution Approach 2:
The system creates compact binary copies of the original data that preserve all necessary information while occupying significantly less storage space. These binary copies serve as efficient representations that can be stored and processed with reduced resource requirements.
2Speed
If data is processed in real-time, then analytics speed improves, but processing complexity increases
Solution Approach 1:
The system performs preliminary conversion of data into binary format and pre-processes data into structured representations before real-time analysis is needed. This advance preparation reduces the complexity of real-time processing while maintaining high analytics speed.
Solution Approach 2:
The patent replaces traditional text-based data processing mechanisms with binary data processing mechanisms. This substitution enables faster computational operations and reduces processing complexity while maintaining real-time analytics capability.
3Ease of operation
If distributed data querying is implemented, then data accessibility improves, but system complexity increases
Solution Approach 1:
The system segments distributed data into standardized binary formats across multiple locations, enabling independent processing and retrieval. This segmentation improves data accessibility while the standardized format reduces overall system complexity by providing a uniform interface for data operations.
Data Source
AI summary
The APPARATUSES, METHODS AND SYSTEMS FOR INSIGHT DISCOVERY AND PRESENTATION FROM STRUCTURED AND UNSTRUCTURED DATA (“IDAP”) provides a platform that, in various embodiments, is configurable to identify, display, and act upon insights derived from large volumes of data. In one embodiment, the IDAP is configurable to determine values and relationships for primal data. Identified relationships may be leveraged to build queries optimized for efficient data access across data volumes. The IDAP may also be configured to identify drivers of global metrics of interest, leverage those drivers to discern the efficacy of media and/or advertising campaigns, and provide recommendations to improve and/or optimize campaign efficacy.