Multilevel Hash Tree Index for Distributed Data Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing algorithms for clustering time series data are less efficient in distributed storage environments, requiring significant data shuffling across networks, which hampers performance in scalable storage systems.
Innovation Solution
A multilevel hash tree (MLHT)-based index is used to cluster data at the point of ingestion, mapping attribute hierarchies onto continuous ranges, allowing efficient storage and querying by localizing data elements with similar attributes to specific storage partitions, thereby reducing data filtering and improving query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional clustering algorithms are used in distributed storage environments, then data can be clustered by attribute values, but significant data shuffling across the network is required, reducing efficiency
Solution Approach 1:
The patent applies preliminary action by performing clustering at the point of data ingestion rather than after data is already stored in a distributed environment. The MLHT-based index is built incrementally as data arrives, assigning each data element to an appropriate storage partition immediately upon ingestion. This eliminates the need for subsequent data shuffling and re-clustering operations, thereby maintaining clustering accuracy while significantly improving processing efficiency.
Solution Approach 2:
The patent segments the data storage space into multiple partitions organized as a multi-level hash tree structure. Each partition corresponds to specific attribute value ranges, allowing data to be distributed across multiple storage nodes without requiring centralized data shuffling. This segmentation enables parallel processing and eliminates network traffic bottlenecks while preserving the ability to cluster data by attribute values.
2Productivity
If data is stored in a single location for clustering, then clustering algorithms can operate efficiently, but the system lacks scalability for large distributed datasets
Solution Approach 1:
The patent transitions from a single-location storage model to a multi-dimensional distributed storage model using a multi-level hash tree structure. Data is organized across multiple dimensions (hash levels, partitions, storage nodes) rather than in a single location. This dimensional transformation allows clustering algorithms to operate efficiently on each partition locally while the system scales to handle large distributed datasets across multiple nodes.
Solution Approach 2:
The MLHT-based index structure serves multiple functions simultaneously: it enables efficient local clustering within each partition, supports scalable distribution across multiple storage nodes, and provides a unified interface for querying distributed data. This multi-functionality allows the system to maintain clustering efficiency while achieving scalability that single-location storage cannot provide.
3Adaptability or versatility
If data elements are distributed across multiple storage partitions, then storage scalability is improved, but query performance degrades due to increased data filtering requirements
Solution Approach 1:
The patent introduces the MLHT-based index as an intermediary structure between the distributed storage partitions and query operations. The index maintains mapping relationships between attribute value ranges and storage partition locations, allowing queries to be routed directly to relevant partitions without scanning all distributed data. This intermediary enables the system to achieve both storage scalability and efficient query performance by eliminating unnecessary data filtering across partitions.
Data Source
AI summary
Techniques are described for clustering data at the point of ingestion for storage using scalable storage resources. To cluster data at the point of ingestion, a data ingestion and query service uses a multilevel hash tree (MLHT)-based index to map a hierarchy of attribute values associated with each data element onto a point of a MLHT (which itself conceptually maps onto a continuous range of values). The total range of the MLHT is divided into one or more data partitions, each of which is mapped to one or more physical storage resources. A mapping algorithm uses the hierarchy of attribute fields to calculate the position of each data element ingested and, consequently, a physical storage resource at which to store the data element.