Service Network Ranking Clustering via Unified Framework
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Solution Overview
Problem
Existing web service ranking and clustering approaches often lead to biased results due to their independent nature, failing to consider the complex relationships and attributes within heterogeneous service networks, which can result in incorrect clustering and ranking.
Innovation Solution
A unified neighborhood random walk distance measure is integrated with local optimal weight assignment to enhance both ranking and clustering, using a probabilistic clustering method that considers service network structure and attributes, and a greedy strategy for cluster matching to align clusters efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If independent web service ranking and clustering approaches are used, then the processes are simpler to implement, but the results become biased and less accurate due to failure to consider complex relationships in heterogeneous service networks
Solution Approach 1:
The patent merges independent ranking and clustering approaches into a unified framework that simultaneously considers service network structure and heterogeneous attributes. This integration allows both processes to benefit from shared computational structures and mutual reinforcement, improving accuracy while managing complexity through unified design.
Solution Approach 2:
The patent develops a universal framework that handles both ranking and clustering tasks within a single system. This multi-functional approach enables the system to address multiple objectives (accurate ranking and meaningful clustering) simultaneously, rather than requiring separate specialized systems for each task.
2Reliability
If independent web service ranking and clustering approaches are used, then the implementation is simpler, but the clustering and ranking results become incorrect due to ignoring complex relationships and attributes
Solution Approach 1:
The patent combines ranking and clustering into an integrated framework where both operations consider the same service network structure and heterogeneous attributes. This merging ensures consistency between ranking and clustering results, improving reliability by eliminating the biases that arise from independent processing.
Solution Approach 2:
The patent transforms the service network into a unified representation that captures both structural relationships and heterogeneous attributes. By changing the parameters considered in both ranking and clustering to include the same comprehensive set of features, the framework ensures consistent and reliable results across both tasks.
3Measurement precision
If a unified framework integrating ranking and clustering is used, then the quality and accuracy of results improve, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary processing of the service network structure and attributes before executing ranking and clustering operations. By pre-computing shared representations and relationships, the framework reduces the computational burden during the actual ranking and clustering tasks, making the integrated approach more efficient despite its enhanced capabilities.
Data Source
AI summary
Offline functionality-based co-ranking and clustering is carried out on a knowledge base that characterizes a heterogeneous information technology services network including a plurality of services, a plurality of providers, and a plurality of attributes. Results of the functionality-based co-ranking and clustering are stored as annotations of the services and the providers in the knowledge base, to obtain an annotated knowledge base. A service requirement is obtained from a customer requiring information technology services. The annotated knowledge base is queried, based on the service requirement; and an ordered list of at least given ones of the services, based on the querying, is returned to the customer.