Cognitive Platform for Enterprise Data Integration

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Solution Overview

Problem

Existing 'big data' technologies face challenges in managing and integrating disparate data from various sources and formats across different infrastructures, leading to difficulties in extracting valuable information for business decision-making, as they are not scalable or sustainable in handling the exponential growth of data and knowledge within enterprises.

Innovation Solution

A cognitive platform that operates in a distributed parallel processing environment, converting source data into Resource Description Framework (RDF) triples using semantic transform rules, enabling efficient inference and creation of knowledge model RDF triples, which are then stored in a high-throughput file system, thereby optimizing query performance and providing a normalized, semantically consistent representation of data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If existing big data technologies are used to converge massive amounts of data from different sources and formats, then data volume can be handled, but the complexity of integrating disparate data formats and infrastructures increases significantly

Engineering Contradiction:

Improvedata volumeVSAvoidintegration complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary layer that translates and standardizes data from multiple sources and formats into a common representation. This intermediary mechanism handles the complexity of integration internally, allowing the system to manage massive data volumes without proportionally increasing integration complexity for users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the parameters of data representation by converting diverse data formats into a standardized schema. This parameter transformation allows heterogeneous data to be processed uniformly, reducing the effective complexity of handling multi-format data while maintaining the ability to ingest large volumes.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If data is integrated across multiple systems with system-level optimizations, then each system can achieve its goals, but the time and complexity required for data integration and schema manipulation increases

Engineering Contradiction:

Improvesystem functionalityVSAvoidintegration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-defining data schemas, validation rules, and transformation logic before data integration occurs. This upfront preparation enables systems to integrate data more quickly without sacrificing reliability, as the integration framework is already configured to handle expected data patterns.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system employs a universal data integration framework that can handle multiple data types, formats, and sources through a single standardized interface. This multi-functional approach reduces integration time by eliminating the need for separate integration processes for different data categories while maintaining system-specific reliability requirements.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Quantity of substance

If massive amounts of data are stored and analyzed, then more information is available, but the ability to discover valuable information efficiently decreases

Engineering Contradiction:

Improvedata availabilityVSAvoidinformation discovery efficiency

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The patent extracts valuable information from massive data sets by applying targeted queries, filters, and analysis algorithms that focus on specific patterns and relationships. This extraction mechanism allows the system to maintain large data repositories while efficiently discovering valuable insights by pulling out only the relevant information needed for decision-making.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system replaces manual or simple mechanical information search methods with automated intelligent algorithms that can efficiently navigate and analyze massive data sets. This substitution enables rapid information discovery by using computational intelligence to identify patterns and extract value from large volumes of data without proportional increases in processing time.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10430712B1Cognitive platform for using knowledge to create information from data

Publication Date: 2019.10.01 GOLDMAN SACHS & CO LLC

AI summary

A cognitive platform, systems and methods for using knowledge to create information from data are disclosed. A cognitive stack supports the separation of enterprise knowledge, information and data into three distinct layers. The cognitive stack provides a curated representation of knowledge as an authoritative enterprise system of truth, which can be applied to enterprise relevant data to create meaningful enterprise information in a timely, scalable and sustainable fashion. In an embodiment, the system implements a transmission methodology capable of providing a knowledge contract to independent information creation agents, a horizontally scalable data transformation methodology for creating raw semantically normalized information from disparate data sources and a materialization methodology for creating flexible representations of addressable information structures from a single enterprise information store to support multiple enterprise cognitive use cases.