Abstract: A request is received at a virtual service simulating a particular data service. The request includes a uniform resource locator (URL) that includes a service root portion and a resource path portion identifying a particular resource of a data structure. Syntax of at least the resource path portion is verified based on a particular protocol. Consistency of the resource path portion with a structure of a data model corresponding to the particular data service is also verified. A query of a database is performed based on contents of at least the resource path portion and a simulated response of the particular data service to the request is generated using results of the query.

Abstract: Methods, media, and systems for managing access to shared content in a cloud-based service platform. Embodiments operate in a cloud-based environment that comprises storage devices that store content objects accessible by two or more users. The content objects have respective object path attributes and one or more object parent attributes. A server processes collaboration transactions to associate content object sharing between two or more users. Upon receipt of a collaboration transaction such as an invitation, a server generates a set of collaborator index attributes to relate the two or more users to share the selected content object, and the generated collaborator index attributes are added as rows in a collaborator index. Pre-existing rows in the collaborator index that pertain to the two or more users are not updated. Very large sets of collaboration objects and/or very large sets of users can be managed efficiently by re-indexing only the added rows.

Abstract: Methods, media, and systems for managing access to shared content in a cloud-based service platform. Embodiments operate in a cloud-based environment that comprises storage devices that store content objects accessible by two or more users. The content objects have respective object path attributes and one or more object parent attributes. A server processes collaboration transactions to associate content object sharing between two or more users. Upon receipt of a collaboration transaction such as an invitation, a server generates a set of collaborator index attributes to relate the two or more users to share the selected content object, and the generated collaborator index attributes are added as rows in a collaborator index. Pre-existing rows in the collaborator index that pertain to the two or more users are not updated. Very large sets of collaboration objects and/or very large sets of users can be managed efficiently by re-indexing only the added rows.

Abstract: A request is received at a virtual service simulating a particular data service. The request includes a uniform resource locator (URL) that includes a service root portion and a resource path portion identifying a particular resource of a data structure. Syntax of at least the resource path portion is verified based on a particular protocol. Consistency of the resource path portion with a structure of a data model corresponding to the particular data service is also verified. A query of a database is performed based on contents of at least the resource path portion and a simulated response of the particular data service to the request is generated using results of the query.

Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

Abstract: In a system and method for order-independent stream query processing, one or more input streams of data are received, and the one or more input streams are analyzed to determine data which is older than an already emitted progress indicator. The data which is older than the already emitted progress indicator is partitioned into one or more partitions, and each of the one or more partitions are independently processed using out-of-order processing techniques. A query is received, rewritten and decomposed into one or more sub-queries that produce partial results for each of the one or more partitions, where each of the one or more sub-queries correspond to a partition. A view is also produced that consolidates the partial results for each partition. The partial results are consolidated at a consolidation time specified by the query to produce final results, and the final results are provided.

Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

Abstract: In a system and method for order-independent stream query processing, one or more input streams of data are received, and the one or more input streams are analyzed to determine data which is older than an already emitted progress indicator. The data which is older than the already emitted progress indicator is partitioned into one or more partitions, and each of the one or more partitions are independently processed using out-of-order processing techniques. A query is received, rewritten and decomposed into one or more sub-queries that produce partial results for each of the one or more partitions, where each of the one or more sub-queries correspond to a partition. A view is also produced that consolidates the partial results for each partition. The partial results are consolidated at a consolidation time specified by the query to produce final results, and the final results are provided.

Abstract: In a system and method for order-independent stream query processing, one or more input streams of data are received, and the one or more input streams are analyzed to determine data which is older than an already emitted progress indicator. The data which is older than the already emitted progress indicator is partitioned into one or more partitions, and each of the one or more partitions are independently processed using out-of-order processing techniques. A query is received, rewritten and decomposed into one or more sub-queries that produce partial results for each of the one or more partitions, where each of the one or more sub-queries correspond to a partition. A view is also produced that consolidates the partial results for each partition. The partial results are consolidated at a consolidation time specified by the query to produce final results, and the final results are provided.

Abstract: In a system and method for order-independent stream query processing, one or more input streams of data are received, and the one or more input streams are analyzed to determine data which is older than an already emitted progress indicator. The data which is older than the already emitted progress indicator is partitioned into one or more partitions, and each of the one or more partitions are independently processed using out-of-order processing techniques. A query is received, rewritten and decomposed into one or more sub-queries that produce partial results for each of the one or more partitions, where each of the one or more sub-queries correspond to a partition. A view is also produced that consolidates the partial results for each partition. The partial results are consolidated at a consolidation time specified by the query to produce final results, and the final results are provided.