Complex service network ranking and clustering

• US 9,576,048 B2
• Filed: 06/26/2014
• Issued: 02/21/2017
• Est. Priority Date: 06/26/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Akolkar, Rahul P., Chefalas, Thomas E., Jiang, Yexi, Laredo, Jim A., Perng, Chang-Shing, Sailer, Anca, Schaffa, Frank A., Segal, Alla, Silva-Lepe, Ignacio, Tao, Tao, Zhou, Yang
• Primary Examiner(s): PHAM, KHANH B
• Application Number:
  US14/315,370
• Publication Number:
  US 20150379121A1
• Time in Patent Office: 971 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06Q 30/0631 Item recommendations

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.

Claims

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 step of carrying out offline functionality-based co-ranking and clustering on said knowledge base comprises;

calculating an initial service clustering for said heterogeneous information technology services network;

classifying said providers into a plurality of service clusters;

within each of said service clusters, ranking each of said providers;

integrating ranking and clustering distances between said providers into a uniform provider distance to obtain updated provider similarity;

classifying said services into a plurality of provider clusters;

within each of said provider clusters, ranking each of said services; and

integrating ranking and clustering distances between said services into a uniform service distance to obtain updated service similarity.

2. The method of claim 1, wherein:

said clustering comprises service influence based probabilistic clustering based on a uniform neighborhood random walk distance measure; and

said co-ranking comprises service influence based ranking.

3. The method of claim 2, wherein said service influence based probabilistic clustering is implemented by analogy to heat diffusion.

4. The method of claim 1, further comprising:

repeating said steps of classifying said providers, ranking each of said providers;

integrating said ranking and clustering distances between said providers, classifying said services, ranking each of said services, and integrating ranking and clustering distances between said services, until convergence is achieved;

wherein, in said step of storing said results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain said annotated knowledge base, said results comprise results of said convergence.

5. The method of claim 1, further comprising providing a system, wherein the system comprises distinct software modules, each of the distinct software modules being embodied on a computer-readable storage medium, and wherein the distinct software modules comprise a knowledge base querying module and a population agent module;

wherein;

said offline functionality-based co-ranking and clustering on said knowledge base is carried out by said population agent module executing on at least one hardware processor;

said storing of said results of said functionality-based co-ranking and clustering is carried out by said population agent module executing on said at least one hardware processor;

said obtaining of said service requirement from said customer requiring said information technology services is carried out by said knowledge base querying module executing on said at least one hardware processor;

said querying of said annotated knowledge base is carried out by said knowledge base querying module executing on said at least one hardware processor; and

said returning to said customer of said ordered list is carried out by said knowledge base querying module executing on said at least one hardware processor.

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:

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 program instructions executable by said processor to cause the processor to carry out offline functionality-based co-ranking and clustering on said knowledge base comprise program instructions executable by said processor to cause the processor to;

calculate an initial service clustering for said heterogeneous information technology services network;

classify said providers into a plurality of service clusters;

within each of said service clusters, rank each of said providers;

integrate ranking and clustering distances between said providers into a uniform provider distance to obtain updated provider similarity;

classify said services into a plurality of provider clusters;

within each of said provider clusters, rank each of said services; and

integrate ranking and clustering distances between said services into a uniform service distance to obtain updated service similarity.

7. The computer program product of claim 6, wherein:

said clustering comprises service influence based probabilistic clustering based on a uniform neighborhood random walk distance measure; and

said co-ranking comprises service influence based ranking.

8. The computer program product of claim 7, wherein said service influence based probabilistic clustering is implemented by analogy to heat diffusion.

9. The computer program product of claim 6, wherein said program instructions further comprise program instructions executable by said processor to cause the processor to:

repeat said steps of classifying said providers, ranking each of said providers;

integrating said ranking and clustering distances between said providers, classifying said services, ranking each of said services, and integrating ranking and clustering distances between said services, until convergence is achieved;

wherein, in said storing of said results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain said annotated knowledge base, said results comprise results of said convergence.

10. An apparatus comprising:

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 at least one processor is operative to carry out said offline functionality-based co-ranking and clustering on said knowledge base by;

calculating an initial service clustering for said heterogeneous information technology services network;

classifying said providers into a plurality of service clusters;

within each of said service clusters, ranking each of said providers;

integrating ranking and clustering distances between said providers into a uniform provider distance to obtain updated provider similarity;

classifying said services into a plurality of provider clusters;

within each of said provider clusters, ranking each of said services; and

integrating ranking and clustering distances between said services into a uniform service distance to obtain updated service similarity.

11. The apparatus of claim 10, wherein:

said clustering comprises service influence based probabilistic clustering based on a uniform neighborhood random walk distance measure; and

said co-ranking comprises service influence based ranking.

12. The apparatus of claim 11, wherein said service influence based probabilistic clustering is implemented by analogy to heat diffusion.

13. The apparatus of claim 10, wherein said at least one processor is further operative to:

repeat said steps of classifying said providers, ranking each of said providers;

integrating said ranking and clustering distances between said providers, classifying said services, ranking each of said services, and integrating ranking and clustering distances between said services, until convergence is achieved;

wherein, in said storing of said results of said functionality-based co-ranking and clustering as annotations of said services and said providers in said knowledge base, to obtain said annotated knowledge base, said results comprise results of said convergence.

14. The apparatus of claim 10, further comprising a plurality of distinct software modules, each of the distinct software modules being embodied on a computer-readable storage medium, and wherein the distinct software modules comprise a knowledge base querying module and a population agent module;

wherein;

said at least one processor is operative to carry out said offline functionality-based co-ranking and clustering by executing said population agent module on said at least one hardware processor;

said at least one processor is operative to store said results of said functionality-based co-ranking and clustering by executing said population agent module;

said at least one processor is operative to obtain said service requirement from said customer by executing said knowledge base querying module on said at least one hardware processor;

said at least one processor is operative to query said annotated knowledge base by executing said knowledge base querying module on said at least one hardware processor; and

said at least one processor is operative to return to said customer said ordered list by executing said knowledge base querying module on said at least one hardware processor.