18542267. Entity Snapshots Partitioning And Combining simplified abstract (Oracle International Corporation)

Entity Snapshots Partitioning And Combining

Organization Name

Oracle International Corporation

Inventor(s)

Sergey Troshin of Santa Clara CA (US)

Sachin Bhatkar of Sunnyvale CA (US)

Sunil Kunisetty of Fremont CA (US)

Shivakumar Subramanian Govindarajapuram of Dublin CA (US)

Entity Snapshots Partitioning And Combining - A simplified explanation of the abstract

This abstract first appeared for US patent application 18542267 titled 'Entity Snapshots Partitioning And Combining

Simplified Explanation

The patent application abstract describes a technique for improving the efficiency of data analytics on sets of entity data with different update frequencies by using partial historical snapshots of data and combining them on demand to execute analytical queries over business entities.

• The technique partitions entity properties into partial historical snapshots of data.
• Partial snapshots are combined on demand only as needed to execute analytical queries.
• Complete entity states with values for all properties are not required for most queries.
• Only partial snapshots containing values referenced by the query need to be combined.
• Minimizes data replication and efficiently combines snapshots into entity states for query execution.

Potential Applications

This technology could be applied in various industries such as finance, healthcare, retail, and telecommunications for efficient data analytics on entities with different update frequencies.

Problems Solved

1. Efficiently performing data analytics on sets of entity data with varying update frequencies. 2. Minimizing data replication and memory footprint while executing analytical queries over business entities.

Benefits

1. Improved efficiency in data analytics. 2. Reduced memory footprint and data replication. 3. On-demand combination of partial snapshots for query execution.

Potential Commercial Applications

Optimizing data analytics processes in industries such as finance, healthcare, retail, and telecommunications for better decision-making and operational efficiency.

Possible Prior Art

There may be prior art related to techniques for optimizing data analytics processes on entity data with different update frequencies, but specific examples are not provided in this context.

How does this technique handle real-time data updates?

The abstract does not mention how real-time data updates are incorporated into the partial historical snapshots and query execution process.

What are the potential limitations of this technique in handling large-scale datasets?

The abstract does not address the scalability of this technique when dealing with massive amounts of entity data and executing complex analytical queries.

Original Abstract Submitted

Embodiments relate to improving efficiency of data analytics performed on sets of entity data in which different entity properties having very different update frequencies. Time-based analytical queries track the entity states at each moment within a given time window. Analytical queries are executed over a massive number of entity states while using a reasonable memory footprint. The technique partitions the entity properties into partial historical snapshots of data and combines the partial snapshots on demand only as needed to execute analytical queries over business entities. A complete entity state having values for all entity properties is not required to execute most queries. Only partial snapshots including values referenced by the query need to be combined to satisfy the query. Using partial snapshots minimizes data replication, and the snapshots can be efficiently combined into entity states sufficient for query execution.