Complex service network ranking and clustering
First Claim
1. A method comprising:
- carrying out offline functionality-based co-ranking and clustering on a knowledge base, said knowledge base characterizing a heterogeneous information technology services network comprising a plurality of services, a plurality of providers, and a plurality of attributes;
storing results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain an annotated knowledge base;
obtaining a service requirement from a customer requiring information technology services;
querying said annotated knowledge base, based on said service requirement; and
returning to said customer an ordered list of at least given ones of said services, based on said querying,wherein;
said clustering comprises service influence based probabilistic clustering using a unified neighborhood random walk distance measure, wherein service influence accounts for social interactions between object vertices and social activities between object vertices and associated attribute vertices, wherein object vertices represent services or providers and attribute vertices denote their properties; and
said co-ranking comprises service influence based ranking.
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Abstract
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.
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Citations
14 Claims
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1. A method comprising:
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carrying out offline functionality-based co-ranking and clustering on a knowledge base, said knowledge base characterizing a heterogeneous information technology services network comprising a plurality of services, a plurality of providers, and a plurality of attributes; storing results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain an annotated knowledge base; obtaining a service requirement from a customer requiring information technology services; querying said annotated knowledge base, based on said service requirement; and returning to said customer an ordered list of at least given ones of said services, based on said querying, wherein; said clustering comprises service influence based probabilistic clustering using a unified neighborhood random walk distance measure, wherein service influence accounts for social interactions between object vertices and social activities between object vertices and associated attribute vertices, wherein object vertices represent services or providers and attribute vertices denote their properties; and said co-ranking comprises service influence based ranking. - View Dependent Claims (2, 3, 4, 5)
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6. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by a processor to cause the processor to:
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carry out offline functionality-based co-ranking and clustering on a knowledge base, said knowledge base characterizing a heterogeneous information technology services network comprising a plurality of services, a plurality of providers, and a plurality of attributes; store results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain an annotated knowledge base; obtain a service requirement from a customer requiring information technology services; query said annotated knowledge base, based on said service requirement; and return to said customer an ordered list of at least given ones of said services, based on said querying, wherein; said clustering comprises service influence based probabilistic clustering using a unified neighborhood random walk distance measure, wherein service influence accounts for social interactions between object vertices and social activities between object vertices and associated attribute vertices, wherein object vertices represent services or providers and attribute vertices denote their properties; and said co-ranking comprises service influence based ranking. - View Dependent Claims (7, 8, 9)
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10. An apparatus comprising:
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a memory; and at least one processor, coupled to said memory, and operative to; carry out offline functionality-based co-ranking and clustering on a knowledge base, said knowledge base characterizing a heterogeneous information technology services network comprising a plurality of services, a plurality of providers, and a plurality of attributes; store results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain an annotated knowledge base; obtain a service requirement from a customer requiring information technology services; query said annotated knowledge base, based on said service requirement; and return to said customer an ordered list of at least given ones of said services, based on said querying, wherein; said clustering comprises service influence based probabilistic clustering using a unified neighborhood random walk distance measure; and
said co-ranking comprises service influence based ranking, wherein service influence accounts for social interactions between object vertices and social activities between object vertices and associated attribute vertices, wherein object vertices represent services or providers and attribute vertices denote their properties. - View Dependent Claims (11, 12, 13, 14)
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Specification