Disclosed implementations provide a streamlined, assisted search process for surfacing items from a database that enables guided exploratory searching. For example, a system may receive selected content from a client device and determine a first object and a second object for the selected content using a search converter. The system may generate first results by performing a first search of items in a database using the first object as a query, and generate second results by performing a second search of the items in the database using the second object as a query. The system may select a first set of the first results based on relevance of the first object to the selected content and a second set of the second results based on relevance of the second object to the selected content. The system may provide a combined search result including the first set and the second set.

Disclosed implementations provide a streamlined, assisted search process for surfacing items from a database that enables guided exploratory searching. For example, a system may receive selected content from a client device and determine a first object and a second object for the selected content using a search converter. The system may generate first results by performing a first search of items in a database using the first object as a query, and generate second results by performing a second search of the items in the database using the second object as a query. The system may select a first set of the first results based on relevance of the first object to the selected content and a second set of the second results based on relevance of the second object to the selected content. The system may provide a combined search result including the first set and the second set.

The present concepts relate to an improved personalized search engine that can generate personalized rankings of search results in view of individual user's personal preferences and interests. Information about a segment of online content is collected. Certain activities by a user are tracked, including search queries submitted by the user, search results clicked on by the user, and/or web pages browsed by the user. From these activities, the user's preferences relating the segment are inferred using the collected segment information. When the user conducts a search directed to the segment, certain search results that the user is more likely to be interested in, based on the user's preferences, are ranked higher to generate the personalized rankings.

The present concepts relate to an improved personalized search engine that can generate personalized rankings of search results in view of individual user's personal preferences and interests. Information about a segment of online content is collected. Certain activities by a user are tracked, including search queries submitted by the user, search results clicked on by the user, and/or web pages browsed by the user. From these activities, the user's preferences relating the segment are inferred using the collected segment information. When the user conducts a search directed to the segment, certain search results that the user is more likely to be interested in, based on the user's preferences, are ranked higher to generate the personalized rankings.

A method is provided for automatic purchase of digital media and digital assets. The method may include asking at least one user to provide a preselected list of products. The method may also include generating a recommended list of additional products using a machine-learning algorithm, where the machine-learning algorithm includes one of collaborative filtering algorithm, content-based filtering, desirable content model, personalized video game ranker, or knowledge based recommendation systems. The method may also include adding the recommended list of additional products to the preselected list of the at least one user to provide an augmented interest list to the at least one user. The method may also include asking the at least one user to establish parameters for automatic purchases based upon the augmented interest list. The method may further include automatically purchasing one or more products based upon the prices of the one or more products meeting the parameters.

A method is provided for automatic purchase of digital media and digital assets. The method may include asking at least one user to provide a preselected list of products. The method may also include generating a recommended list of additional products using a machine-learning algorithm, where the machine-learning algorithm includes one of collaborative filtering algorithm, content-based filtering, desirable content model, personalized video game ranker, or knowledge based recommendation systems. The method may also include adding the recommended list of additional products to the preselected list of the at least one user to provide an augmented interest list to the at least one user. The method may also include asking the at least one user to establish parameters for automatic purchases based upon the augmented interest list. The method may further include automatically purchasing one or more products based upon the prices of the one or more products meeting the parameters.

A system and method is described for database split generation in a massively parallel or distributed database environment including a plurality of databases and a data warehouse layer providing data summarization and querying functionality. A database table accessor of the system obtains, from an associated client application, a query for data in a table of the data warehouse layer, wherein the query includes a user preference. The system obtains table data representative of properties of the table, and determines a splits generator in accordance with one or more of the user preference or the properties of the table. The system generates, by the selected splits generator, table splits dividing the user query into a plurality of query splits, and outputs the plurality of query splits to an associated plurality of mappers for execution by the associated plurality of mappers of each of the plurality of query splits against the table.

One example method includes crawling data included in a dataset, based on the crawling of the data, creating and/or obtaining metadata concerning the data, crawling the metadata and obtaining information about a data change event involving the data, based on the crawling of the metadata, creating a meta label that documents occurrence of the data change event, and associating the meta label with the metadata. An analysis of the data, metadata, and meta label, may be performed and a trigger generated based on the analysis.

One example method includes crawling data included in a dataset, based on the crawling of the data, creating and/or obtaining metadata concerning the data, crawling the metadata and obtaining information about a data change event involving the data, based on the crawling of the metadata, creating a meta label that documents occurrence of the data change event, and associating the meta label with the metadata. An analysis of the data, metadata, and meta label, may be performed and a trigger generated based on the analysis.

A method, computer system, and a computer program product for topic monitoring in web conferencing is provided. The present invention may include determining a topic profile for each of the plurality of virtual rooms. The present invention may include displaying, in a web conference user interface, the determined topic profile for each of the plurality of virtual rooms to a user. The present invention may include identifying one or more topics of interest of the user. The present invention may include recommending one or more virtual rooms to the user based on a commonality score between the one or more topics of interest of the user and the topic profile for each of the plurality of rooms.