Abstract: This disclosure relates to a method and system for identifying products using digital fingerprints. The method includes receiving, by a product tracking system, a set of images corresponding to a product, wherein the set of images are received along with associated context information. The method generates a set of standardized images by pre-processing the set of images. Each standardized image of the set of standardized images are further segmented into a plurality of zones. A zone wise fingerprint is generated for each zone of the plurality of zones. A product digital fingerprint is generated for the product by combining zone wise fingerprints associated with the set of standardized images, wherein the product digital fingerprint identifies the product.

Abstract: This disclosure relates to method and system for identifying products using digital fingerprints. The method includes receiving, by a product tracking system, a set of images corresponding to a product, wherein the set of images are received along with associated context information. The method generates a set of standardized images by pre-processing the set of images. Each standardized image of the set of standardized images are further segmented into a plurality of zones. A zone wise fingerprint is generated for each zone of the plurality of zones. A product digital fingerprint is generated for the product by combining zone wise fingerprints associated with the set of standardized images, wherein the product digital fingerprint identifies the product.

Abstract: A device may receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

Abstract: In some examples, incident prediction and prevention may include ascertaining a plurality of past incidents, clustering the plurality of past incidents to generate a plurality of incidents clusters, and identifying, for each past incident of the plurality of past incidents that is in a respective incidents cluster of the plurality of incidents clusters, a time of occurrence. Incident prediction and prevention may include ascertaining a new incident, assigning the new incident to an incidents cluster of the plurality of incidents clusters, and determining, for the assigned incidents cluster, at least one further predicted incident associated with at least one further corresponding incidents cluster. Further, incident prediction and prevention may include determining a resolution to the at least one further predicted incident, and preventing occurrence of the at least one further predicted incident by executing the determined resolution to the at least one further predicted incident.

Abstract: In some examples, incident prediction and prevention may include ascertaining a plurality of past incidents, clustering the plurality of past incidents to generate a plurality of incidents clusters, and identifying, for each past incident of the plurality of past incidents that is in a respective incidents cluster of the plurality of incidents clusters, a time of occurrence. Incident prediction and prevention may include ascertaining a new incident, assigning the new incident to an incidents cluster of the plurality of incidents clusters, and determining, for the assigned incidents cluster, at least one further predicted incident associated with at least one further corresponding incidents cluster. Further, incident prediction and prevention may include determining a resolution to the at least one further predicted incident, and preventing occurrence of the at least one further predicted incident by executing the determined resolution to the at least one further predicted incident.

Abstract: A system may include one or more server devices. The system may provide a set of questions, to a user of a user device, to characterize an Internet of things system. The system may obtain responses from the user of the user device associated with each of the set of questions. The system may automatically create an Internet of things system architecture that defines the Internet of things system by applying associated Internet of things system architecture rules to the responses.

Abstract: A device may receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

Abstract: This disclosure relates to assessing database migrations to cloud computing systems. On example method includes determining, by a migration server including one or more hardware processors, a set of possible transactions associated with a database application based at least in part on a set of application attributes associated with the database application; generating, by the migration server, a set of application requirements associated with the set of possible transactions; and creating, by the migration server, a set of migration recommendations associated with the database application based on the set of application requirements, the set of migration recommendations configured to allow the database application to be migrated to a cloud computing system and to allow the database application to comply with the set of application requirements when executed in the cloud computing system.

Abstract: A system may include one or more server devices. The system may provide a set of questions, to a user of a user device, to characterize an Internet of things system. The system may obtain responses from the user of the user device associated with each of the set of questions. The system may automatically create an Internet of things system architecture that defines the Internet of things system by applying associated Internet of things system architecture rules to the responses.

Abstract: This disclosure relates to assessing database migrations to cloud computing systems. On example method includes determining, by a migration server including one or more hardware processors, a set of possible transactions associated with a database application based at least in part on a set of application attributes associated with the database application; generating, by the migration server, a set of application requirements associated with the set of possible transactions; and creating, by the migration server, a set of migration recommendations associated with the database application based on the set of application requirements, the set of migration recommendations configured to allow the database application to be migrated to a cloud computing system and to allow the database application to comply with the set of application requirements when executed in the cloud computing system.