A method, computer program and apparatus is disclosed. The method, performed by one or more processors, may comprise receiving, from one or more predetermined organizations, datasets representing entities and datasets representing one or more tasks for those entities and storing in a database, in accordance with an ontology which is common to the organizations, the received one or more datasets as data objects, the ontology defining properties of data objects and relationships between the data objects. The method may also comprise mapping the data objects stored in the database to the organization from which the one or more datasets were received and receiving, through a querying application, a query from a user of one of the predetermined organizations to view one or more data objects relating to a task. The method may also comprise identifying the organization to which the user is associated, generating, based on the mapping, a view including at least the one or more task data objects associated with the identified organization and not data objects associated with other organizations and displaying the view on a user interface.

A method includes a digital subscription management system (DSMS) receiving from a source system a request to perform an edit of at least one data object, the DSMS sending a request for response (RFR) to subscribing systems having a copy of the at least one data object, the DSMS receiving a response from the subscribing systems, and the DSMS performing the edit.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

Systems and methods for identifying content for an input query are presented. A mapping model is trained to map elements of an input query embedding vector for a received query into one or more elements of a destination embedding vector. In response to receiving an input query, an input query embedding vector is generated that projects into an input query embedding space. The input query embedding vector is processed by the mapping model to map the input query embedding vector into one or more elements of a destination embedding vector in a destination embedding space, resulting in a partial destination embedding vector. Items of a corpus of content are projected into the destination embedding space and the partial destination embedding vector is also projected into the destination embedding space. A similarity measure determines the most-similar items to the partial destination embedding vector and at least some of the most-similar items are returned in response to the input query.

Systems and methods for identifying content for an input query are presented. A mapping model is trained to map elements of an input query embedding vector for a received query into one or more elements of a destination embedding vector. In response to receiving an input query, an input query embedding vector is generated that projects into an input query embedding space. The input query embedding vector is processed by the mapping model to map the input query embedding vector into one or more elements of a destination embedding vector in a destination embedding space, resulting in a partial destination embedding vector. Items of a corpus of content are projected into the destination embedding space and the partial destination embedding vector is also projected into the destination embedding space. A similarity measure determines the most-similar items to the partial destination embedding vector and at least some of the most-similar items are returned in response to the input query.

The present disclosure relates generally to Lightweight Directory Access Protocol (LDAP), and more particularly, to techniques for improving query performance on an LDAP server. One particular technique includes receiving a LDAP query having search criteria, identifying one or more search filters within the search criteria, determining candidate indices based on the identified one or more search filters, evaluating the candidate indices based on statistics collected for the candidate indices, selecting one or more indices from the candidate indices based on the evaluating, and executing the LDAP query on an LDAP directory using the selected one or more indices.

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. An improved approach is provided for identifying log files that have undergone a change in status that would require retrieve of its log data, by including a module directly into the operating system that allows the log collection component to be reactively notified of any changes to pertinent log files.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing semi-structured data. One of the methods includes maintaining a plurality of schemas; receiving a first semi-structured data item; determining that the first semi-structured data item does not match any of the schemas in the plurality of schemas; and in response to determining that the first semi-structured data item does not match any of the schemas in the plurality of schemas: generating a new schema, encoding the first semi-structured data item in the first data format to generate the first new encoded data item in accordance with the new schema, storing the first new encoded data item in the data item repository, and associating the first new encoded data item with the new schema.

A method includes identifying an independent data object of a plurality of independent data objects for retrieval from dispersed storage network (DSN) memory. The method further includes determining a mapping of the plurality of independent data objects into a data matrix, wherein the mapping is in accordance with the dispersed storage error encoding function. The method further includes identifying, based on the mapping, an encoded data slice of the set of encoded data slices corresponding to the independent data object. The method further includes sending a retrieval request to a storage unit of the DSN memory regarding the encoded data slice. When the encoded data slice is received, the method further includes decoding the encoding data slice in accordance with the dispersed storage error encoding function and the mapping to reproduce the independent data object.