Event Prediction System Using Multidimensional Histograms
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Solution Overview
Problem
Current technologies are limited in processing large volumes of data for event prediction, particularly in correlating data and learning causality in a scalable manner, which hinders real-time prediction of significant societal events.
Innovation Solution
A system and method that continuously processes data streams to detect significant events, leveraging statistical methods to discover relationships between event types, populations, time, and locations, using a multidimensional histogram for clustering and anomaly detection, and constructing belief networks for future event prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital computers are used to process large volumes of data for event prediction, then data processing speed is improved, but the ability to correlate data and learn causality deteriorates due to lack of scalable learning methods
Solution Approach 1:
The patent introduces an intermediary layer of event extraction frameworks and causality models that bridge raw data and predictive analytics. These intermediaries (event schemas, causal graphs, knowledge bases) enable computers to learn and represent causal relationships systematically, resolving the contradiction between processing speed and causality learning capability
Solution Approach 2:
The system transforms raw data into structured event representations with specific parameters (event types, entities, timestamps, locations). This parameterization enables scalable processing while preserving causal relationships, allowing both high-speed processing and meaningful causality learning to coexist
2Measurement precision
If the volume of data processed for event prediction is increased, then prediction accuracy is improved, but the complexity of processing and filtering meaningful signals deteriorates
Solution Approach 1:
The patent segments the overwhelming data stream into discrete, structured events with defined attributes and categories. By dividing data into manageable event units (e.g., political events, economic events, social events), the system can process large volumes without proportional increases in complexity, maintaining prediction accuracy through systematic event-based analysis
Solution Approach 2:
The system extracts only the essential and meaningful features from vast amounts of data, discarding redundant information. Event extraction frameworks identify and isolate key entities, relationships, and temporal patterns, reducing processing complexity while preserving the signals necessary for accurate prediction
3Speed
If real-time event prediction is implemented, then responsiveness is improved, but the computational resources required deteriorate due to continuous processing demands
Solution Approach 1:
The system employs periodic event extraction and updating cycles rather than continuous processing. Events are detected and processed at meaningful intervals based on data arrival patterns and event significance, enabling real-time responsiveness while reducing computational resource consumption through rhythmic, batch-oriented processing
Solution Approach 2:
The patent implements preliminary event extraction, filtering, and structuring before full predictive analysis. By pre-processing data into organized event formats and maintaining updated event databases, the system reduces the computational burden during real-time prediction, having already performed heavy lifting in earlier processing stages
Data Source
AI summary
A method and apparatus for predicting significant future events based on previous events. Plural messages representing events are received. Attributes of the messages are mapped to respective feature dimensions to define a multidimensional histogram. Co-occurrence of at least two event types are determined based on queries of the multidimensional histogram. Anomalous event types are detected from the messages by comparing feature dimensions of incoming messages to probability density functions of the cluster corresponding to the event type and highly anomalous event types are determined. Causal relationships between each pair of event types are determined and a Bayesian belief network of the pairs of event types is created and used to predict future events based on occurrence of additional events.