Abstract: Embodiments are directed towards managing data. Attributes of model fields of a plurality of model objects may be analyzed. If the analysis of the attributes discovers primary key fields in the model objects, the characteristics of the primary keys may be compared with the characteristics of other model objects. If affirmative results of the comparison indicate that one or more foreign key fields may be in the other model objects, one or more relationships that associate the primary key fields with the foreign key fields may be provided. And, a system model may be provided based on the relationships and the model objects that include the primary key fields and the model objects that include the foreign key fields.

Abstract: Embodiments are directed towards managing data. Attributes of model fields of a plurality of model objects may be analyzed. If the analysis of the attributes discovers primary key fields in the model objects, the characteristics of the primary keys may be compared with the characteristics of other model objects. If affirmative results of the comparison indicate that one or more foreign key fields may be in the other model objects, one or more relationships that associate the primary key fields with the foreign key fields may be provided. And, a system model may be provided based on the relationships and the model objects that include the primary key fields and the model objects that include the foreign key fields.

Abstract: Embodiments are directed towards dynamic semantic models having multiple indices. Source data may be provided to a network computer from at least one separate data source. A raw data graph may be generated from the source data such that the structure of the raw data graph may be based on the structure of the source data. Elements of the raw data graph may be mapped to a concept graph. Concept instances may be generated based on the concept graph, the raw data graph, and the source data. Model-identifiers (MIDs) that correspond to the concept instances may be generated to include at least a path in the concept graph. The MID values may be indexed into a plurality of indices based on a content-type of the data associated with the MIDs. In response to a query, a result set may be generated that includes result MIDs.

Abstract: Embodiments are directed towards dynamic semantic models having multiple indices. Source data may be provided to a network computer from at least one separate data source. A raw data graph may be generated from the source data such that the structure of the raw data graph may be based on the structure of the source data. Elements of the raw data graph may be mapped to a concept graph. Concept instances may be generated based on the concept graph, the raw data graph, and the source data. Model-identifiers (MIDs) that correspond to the concept instances may be generated to include at least a path in the concept graph The MID values may be indexed into a plurality of indices based on a content-type of the data associated with the MIDs. In response to a query, a result set may be generated that includes result MIDs.

Abstract: Embodiments are directed towards dynamic semantic models having multiple indices. Source data may be provided to a network computer from at least one separate data source. A raw data graph may be generated from the source data such that the structure of the raw data graph may be based on the structure of the source data. Elements of the raw data graph may be mapped to a concept graph. Concept instances may be generated based on the concept graph, the raw data graph, and the source data. Model-identifiers (MIDs) that correspond to the concept instances may be generated to include at least a path in the concept graph The MID values may be indexed into a plurality of indices based on a content-type of the data associated with the MIDs. In response to a query, a result set may be generated that includes result MIDs.

Abstract: Embodiments are directed towards dynamic semantic models having multiple indices. Source data may be provided to a network computer from at least one separate data source. A raw data graph may be generated from the source data such that the structure of the raw data graph may be based on the structure of the source data. Elements of the raw data graph may be mapped to a concept graph. Concept instances may be generated based on the concept graph, the raw data graph, and the source data. Model-identifiers (MIDs) that correspond to the concept instances may be generated to include at least a path in the concept graph The MID values may be indexed into a plurality of indices based on a content-type of the data associated with the MIDs. In response to a query, a result set may be generated that includes result MIDs.

Abstract: An interactive recommendation system generates one or more recommendations (e.g., recommended products, travel destinations, etc.) for a user based on a recommendation model. The recommendation model includes one or more criteria that are used to analyze a datastore of user characteristics (e.g., a user's age, location, past online behavior, etc.) and generate one or more recommendations based thereon. The interactive recommendation system further presents a user interface that allows the user to interactively modify the criteria of the recommendation model and to apply the modified recommendation model to the datastore in order to generate one or more modified recommendations. In this manner, for example, the user can customize the recommendations he or she receives by interacting with the recommendation system to modify the recommendation model used to generate such recommendations.

Abstract: An interactive recommendation system generates one or more recommendations (e.g., recommended products, travel destinations, etc.) for a user based on a recommendation model. The recommendation model includes one or more criteria that are used to analyze a datastore of user characteristics (e.g., a user's age, location, past online behavior, etc.) and generate one or more recommendations based thereon. The interactive recommendation system further presents a user interface that allows the user to interactively modify the criteria of the recommendation model and to apply the modified recommendation model to the datastore in order to generate one or more modified recommendations. In this manner, for example, the user can customize the recommendations he or she receives by interacting with the recommendation system to modify the recommendation model used to generate such recommendations.

Abstract: A re-closable tamper-proof closure assembly for containers comprises a cap with a tamper-proof strip attached to the skirt of the cap by a breakable web but the strip is free of any tabs graspable by the user's hand, and is broken away from the cap simply by the act of removing the cap by pushing upwards on a stout lug. There may be a gap in the web already in the region of the lug, and at the opposite side of the cap the skirt is extended downwards to form a tongue between the ends of the strip. There can be a bead on the container neck for snap engagement of the cap on re-use, in addition to the bead engaged by the tamper-proof strip.