Abstract: A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Abstract: A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Abstract: A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Abstract: An apparatus comprises a multi-cluster distributed data processing platform. Each of the clusters of the platform is configured to perform processing operations utilizing local data resources accessible within a corresponding data zone. A first application is initiated in one of the clusters, and data resources to be utilized by the application are determined. For one or more of the data resources identified as local data resources for the associated cluster, processing operations are performed utilizing those local data resources in that cluster in accordance with the first application. For one or more of the data resources identified as remote data resources for the associated cluster, one or more additional applications are initiated in one or more additional ones of the clusters. This repeats recursively for each additional application until all processing required by the first application is complete. Processing results from the clusters are aggregated and provided to a client.

Abstract: A hybrid recommender system, in which the initial stereotype is manually defined by an expert and an affinity vector of stereotypes relating to each specific user who registers onto the system, is created to define a specific profile for each user. Recommendations for a specific user are generated according to the initial stereotype and the affinity vector of stereotypes. A binary feedback, from user regarding specific items picked by him is received (e.g., while of the item), which can be either positive or negative. Then the affinity vector of stereotypes is updated.

Abstract: A hybrid recommender system, in which the initial stereotype is manually defined by an expert and an affinity vector of stereotypes relating to each specific user who registers onto the system, is created to define a specific profile for each user. Recommendations for a specific user are generated according to the initial stereotype and the affinity vector of stereotypes. A binary feedback, from user regarding specific items picked by him is received (e.g., while of the item), which can be either positive or negative. Then the affinity vector of stereotypes is updated.