Ontological Policy Data Collection for Analytics
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Solution Overview
Problem
Cloud data collection has limited capabilities in identifying and selecting relevant data for analytics, leading to restricted data flow and inefficiencies in data selection and negotiation between providers and consumers.
Innovation Solution
Implementing a method for ontological policy-based data collection, processing, and negotiation that includes off-line auto-tagging, fine granularity data tagging, and hierarchical visualization to support custom data selection and preprocessing based on analytics requirements, allowing for evaluation of data quality and manual configuration of ontological policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud data collection systems use traditional data selection methods, then data flow is restricted and selection efficiency is low, but implementing ontological policy-based data collection with auto-tagging and fine granularity tagging increases system complexity
Solution Approach 1:
The system performs preliminary actions by automatically tagging data sources and data elements with ontological descriptors before data collection requests are made. This pre-tagging enables efficient data selection and negotiation without requiring complex real-time processing, thereby improving productivity while managing system complexity through advance preparation.
Solution Approach 2:
The patent introduces an intermediary layer consisting of ontological policies, descriptors, and auto-tagging mechanisms that mediate between data providers and data consumers. This intermediary structure standardizes data representation and selection criteria, improving data selection efficiency while providing a manageable framework that reduces the apparent complexity for end users.
2Measurement precision
If manual configuration of ontological policies is implemented, then data quality evaluation improves, but negotiation time and processing overhead increase
Solution Approach 1:
The system implements self-service through automatic tagging of data sources and elements using ontological descriptors. Data sources automatically generate and maintain their own metadata and quality descriptors, enabling accurate data quality evaluation without requiring extensive manual configuration or negotiation time. This self-tagging capability maintains measurement precision while significantly reducing the time loss associated with manual processes.
3Manufacturing precision
If fine granularity data tagging is applied to all data elements, then data selection precision improves, but data processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by implementing fine-granularity tagging selectively at the appropriate level of detail for each data element and context. Rather than uniformly applying maximum granularity to all data, the system tags data with the precise level of ontological detail needed for its specific domain and use case. This approach maintains data selection precision while avoiding the computational overhead of excessive granularity.
Solution Approach 2:
The system employs partial action by applying fine-granularity tagging only to data elements where such precision is actually required for the analytics task at hand. For data elements where coarse-granularity suffices, the system uses lighter tagging approaches. This selective application of tagging granularity maintains necessary data selection precision while optimizing data processing speed by avoiding unnecessary computational overhead.
Data Source
AI summary
An approach for ontological policy based data collection, processing, and negotiation for data in view of analytics is provided. The approach searches one or more data sources for data related to a data request. The approach collects data related to the data request from the one or more data sources. The approach determines whether one or more attributes generated from the data request match one or more descriptors associated with the data related to the data request. The approach creates one or more annotated ontologies for the data related to the data request. The approach displays a hierarchical visualization of the one or more annotated ontologies for the data related to the data request. The approach updates the one or more annotated ontologies for the data related to the data request based, at least in part, on an evaluation of the quality of the one or more data selections.