Distributed Learning Platform for Large Scale Data Analytics
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Solution Overview
Problem
Current machine learning platforms face challenges in scaling to handle large datasets with trillions of data samples and billions of features, leading to inefficiencies in data processing and classification accuracy due to limitations in feature extraction and classifier generalization.
Innovation Solution
A distributed learning platform is developed that uses parallelizable algorithms to empirically estimate optimal feature vectors and discriminant functions, allowing for the creation and refinement of classifiers through distributed computing, enabling the processing of large-scale data analytics and improving classification accuracy by leveraging validated data and unsupervised learning mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning platforms are used to process large datasets, then processing capability is limited, but scaling to handle trillions of data samples and billions of features becomes impossible
Solution Approach 1:
The patent segments the learning platform into multiple distributed computing nodes that can independently process portions of the dataset. Each node performs feature extraction and classification on a subset of data, allowing the system to scale horizontally to handle trillions of samples by simply adding more nodes to the distributed cluster.
Solution Approach 2:
The patent transitions from single-machine processing to multi-dimensional distributed computing across a network of nodes. By organizing computation across spatial dimensions (multiple nodes, multiple processors per node), the system achieves scalability that cannot be obtained through vertical scaling alone.
2Measurement precision
If more features are extracted from data samples, then classification accuracy improves, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the feature extraction process into multiple stages and distributes it across different computing nodes. Instead of extracting all features for all samples sequentially, the system segments both the data samples and feature extraction tasks, allowing parallel processing that maintains accuracy while reducing overall computational burden.
Solution Approach 2:
The patent implements a two-stage approach where not all features are extracted for all samples simultaneously. Instead, the system extracts features in batches, uses preliminary filtering to identify promising samples, and then applies full feature extraction only where needed, reducing unnecessary computational complexity while preserving classification accuracy.
3Reliability
If more data samples are provided to the learning system, then classifier generalization improves, but processing time and resource requirements increase
Solution Approach 1:
The patent segments the training data into multiple partitions that can be processed independently and in parallel. Each computing node processes a portion of the training samples concurrently, allowing the system to ingest and learn from trillions of samples without linearly increasing total processing time, thus improving generalization while controlling time loss.
Solution Approach 2:
The patent implements continuous data ingestion and processing pipelines where new data samples are constantly being added to the training set without stopping the learning process. The distributed system continuously processes incoming data in real-time, allowing the classifier to improve generalization progressively without requiring batch retraining that would cause significant time loss.
Data Source
AI summary
To address problems that video imaging systems and platforms face when analyzing image and video content for detection and feature extraction, a solution is provided in which accumulating significant amounts of data suitable for training and learning analytics is leveraged to improve over time, the classifiers used to perform the detection and feature extraction, by employing a larger search space and generate additional and more complex classifiers through distributed processing. A distributed learning platform is therefore provided, which is configured for operating on large scale data, in a true big data paradigm. The learning platform is operable to empirically estimate a set of optimal feature vectors and a set of discriminant functions using a parallelizable learning algorithm. A method of adding new data into a database utilized by such a learning platform is also provided. The method comprises identifying an unrepresented sample space; determining new data samples associated with the unrepresented sample space; and adding the new data samples to the database.