Continuous Event Processing Platform for Asset Data Correlation
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Solution Overview
Problem
Real-time asset monitoring and management in vertical markets face challenges in correlating static and dynamic asset properties, as well as formulating general, reusable queries over a dynamically changing set of single property values from separate data stores, making it difficult to operate effectively across heterogeneous data sources.
Innovation Solution
The architecture leverages continuous event processing (CEP) to model assets as real-time event types, using a general input interface framework and a declarative query model to import dynamic sets of event types, enabling the formulation of standing, declarative queries that correlate data from different data sources with different dynamic properties, thus synchronizing and merging real-time and static asset data into a unified event stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate data stores are used for static and dynamic asset properties, then data storage and retrieval can be optimized for each type, but correlating data from multiple sources and formulating general queries becomes difficult
Solution Approach 1:
The patent merges static and dynamic asset property data into a unified event stream using a common event schema. The system combines data from multiple heterogeneous sources (time-series databases for dynamic properties and relational stores for static properties) into a single standardized event format, enabling consistent querying and correlation across all asset data regardless of its origin or type.
Solution Approach 2:
The system creates a universal event schema that can represent both static and dynamic asset properties through a single data structure. This universal schema serves multiple functions: it standardizes data from different sources, enables unified querying operations, and facilitates complex correlations between asset properties that would otherwise require separate query formulations for each data source.
2Ease of operation
If asset schema and hierarchy are stored in a metadata store, then asset information can be centrally managed, but retrieving actual property values from separate repositories adds complexity to monitoring tasks
Solution Approach 1:
The system performs preliminary action by pre-loading both static and dynamic property values into the unified event stream before monitoring queries are executed. The metadata store provides asset schema and hierarchy information, while actual property values are retrieved and synchronized in advance, so that when monitoring tasks run, all necessary data is already available in the standardized event format without requiring complex multi-source retrieval operations.
3Productivity
If monitoring tasks operate on dynamically changing single property values from separate stores, then real-time updates can be captured, but formulating general reusable queries becomes difficult or impossible
Solution Approach 1:
The system handles parameter changes by maintaining a unified event schema that can accommodate varying property types and values. The schema allows dynamic property values to be represented consistently regardless of their source or type, enabling monitoring queries to operate on standardized parameters rather than source-specific fields. This standardization maintains real-time monitoring capability while enabling general reusable queries that work across all asset types and data sources.
Data Source
AI summary
The disclosed architecture leverages realtime continuous event processing (CEP) to address using a general input interface framework to import a dynamic set of event types (e.g., assets), and using a declarative, expressive query model to implement monitoring and management tasks on an asset level. This is in contrast to looking separately at single values from static databases and/or realtime streams as is common conventionally. The architecture uses the CEP data model to model assets as realtime event types. Thus, queries can be formulated per asset and not just per single stream. The architecture uses the query capabilities of CEP to formulate asset management and monitoring tasks as standing, declarative queries, and uses the input interface of a CEP platform to correlate data from different data sources with different dynamic properties.