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A System and Method for Industry-Based Software App Recipe Generation Disclosure Number: IPCOM000244902D
Publication Date: 2016-Jan-27
Document File: 2 page(s) / 29K

Publishing Venue

The Prior Art Database


Disclosed is a method to extract information of how services are used in combination to solve business problems and identify common patterns per industry.

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A System and Method for Industry -

-Based Software App Recipe Generation

Based Software App Recipe Generation

The problem addressed in this disclosure is that of identifying how microservices are used to build apps and solutions specific to different industries. This knowledge can be used to generate app recipes that invoke most popular services in the most used manner by different industries. This can be useful for various purposes including benchmark testing, microservice provisioning optimization, and feedback to product and development teams allowing for consolidation/integration of services. Furthermore, this information is all industry specific so this method allows you to judge the most relevant microservices and how they're integrated to build applications in different industries.

    The idea of this disclosure is to parse log files generated by the different services and create service usage graph to describe how the services are used in building applications and solutions specific to various industries. This graph is then leveraged to build most commonly used app/solution recipes which could then be used for modifying product offerings or optimizing the microservice provisioning. Platform as a Service (PaaS) providers enable the offering of multiple services in various domains which empowers app developers to integrate these services to build powerful apps. After building these apps, the developers host them on Bluemix* which handles the provisioning, scaling, and metering of these apps. As each app A gets executed, it calls several services and each of these services creates a log file identifying the app and associated metadata such as the app id, org id, timestamp when service was called, the data used in calling the service, and so on.

    The first step in this method involves identifying a set of industries of interest such as Retail, Healthcare, Automotive, Financial Services, Marketing, etc. Given information about partners/clients from registration data, one can identify which industry each partner/client is best associated with. Note that in some scenarios a few partners/clients may bridge a couple of industries. In what follows, one assumes they have K classes of industries: {I1, I2, … IK}.

Assuming there are N services available, this idea is to create a directed graph where each node corresponds to one service. An edge from node i to node j indicates that there is at least one app that calls service i and service j in sequence. The weight of the edge would be computed based on how frequently service i and service j are called in sequence. For example, if service i and service

j are called in sequence 10 times, then the weight of the...