Technology is disclosed for enabling storage service compatibility. The technology can enable sorting of data stored across partitions, and provide for key splitting, e.g., to respond to data updates and additions.

A method of managing search requests to a content recommendation engine (CRE) is provided. The CRE is adapted to receive search requests and provide one or more content recommendations based on the received search requests for a user of a content distribution system having a plurality of users. The method comprises receiving an inputted search term; setting one or more search parameters based on the search term; and generating a search request based on the inputted search term and the search parameters.

A system for support and management of search for documents is presented. The system includes a knowledge-database, query interface and communication to a database of documents to be searched. Information generated during a search session is collected by the system and is added to the knowledge-database. The information is ranked automatically according to the usage of that information by the user. During successive search session or during a search made by another user, the system uses the knowledge database to support the users with keywords, queries and reference documents.

In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing conditional statement execution within a multi-tenant aware structured query language within a computing environment. For instance, such an exemplary system having a processor and a memory therein includes means for operating a multi-tenant database system having customer data stored therein for a plurality of distinct customer organizations; receiving a database query specifying a command term to conditionally execute a sub-portion of the database query when a Boolean evaluation specified with the database query resolves to true and to negate execution of the sub-portion of the database query when the Boolean evaluation resolves to false; parsing the database query to identify the command term specified; parsing the database query to identify any part of the received database query corresponding to the sub-portion of the database query to be conditionally executed or negated pursuant to the Boolean evaluation; generating a new structured query language statement to execute against the multi-tenant database system in fulfillment of the database query received, in which the new structured query language statement includes a semantic structure to execute the sub-portion of the database query when the Boolean evaluation resolves to true and to omit from execution the sub-portion of the database query when the Boolean evaluation resolves to false; and executing the new structured query language statement against the multi-tenant database system. Other related embodiments are disclosed.

The disclosure generally provides for a method of solving a K-SAT problem. The method comprises programming one or more clauses of a Boolean expression for a K-SAT problem written in negated disjunctive normal form (DNF) to a ternary-CAM (TCAM) array comprising columns and rows of TCAM cells. The method further includes applying an interpretation comprising one or more binary variables expected to solve the Boolean expression as an input along the columns to the TCAM array, returning a binary value for each clause, randomly selecting one matched match line, determining a selected clause from one or more violated clause, and altering one or more literals within the interpretation using a break count for each variable of the selected clause.

System and method for training inductive logic programming enhanced deep belief network models for discrete optimization are disclosed. The system initializes (i) a dataset comprising values and (ii) a pre-defined threshold, partitions the values into a first set and a second set based on the pre-defined threshold. Using Inductive Logic Programming (ILP) engine and a domain knowledge associated with the dataset, a machine learning model is constructed on the first set and the second set to obtain Boolean features, and using the Boolean features that are being appended to the dataset, a deep belief network (DBN) model is trained to identify an optimal set of values between the first set and the second set. Using the trained DBN model, the optimal set of values are sampled to generate samples. The pre-defined threshold is adjusted based on the generated samples, and the steps are repeated to obtain optimal samples.

Provided is a process of refining Boolean queries, the process including: obtaining a query; searching a corpus of documents based on the query; selecting narrowing terms that pertain to respective subsets of the responsive documents; selecting broadening terms related to the query; instructing the user's computing device to present a graphical user interface comprising: graphical representations of the narrowing terms; graphical representations of the broadening terms; and one or more user inputs by which the user refines the query by adding a selected narrowing term or a selected broadening term; obtaining a user selection of a broadening term or a narrowing term; forming a refined query based on the user selection; searching at least part of the corpus based on the refined query to identify refined responsive documents; and instructing the user's computing device to present an updated graphical user interface with information about the refined responsive documents.

A computer implemented method of searching for records. The method comprises generating a relevant search string based on extracted text and related contextual information.

In an example, data from a source location is merged into a target location containing existing data, in which the source location and the target location contain tuples of data. For each tuple in the source location, during a single operation, a determination is made as to whether there is a matched tuple in the target location that satisfies a predetermined condition. For each matched tuple that satisfies the predetermined condition, the matched tuple in the target location is updated with a count value that is equal to a count of the matched tuple in the source location and the target location. In addition, for each tuple that does not have a matched tuple that satisfies the predetermined condition, the unmatched tuple is inserted into the target location.

A system for support and management of search for documents is presented. The system includes a knowledge-database, query interface and communication to a database of documents to be searched. Information generated during a search session is collected by the system and is added to the knowledge-database. The information is ranked automatically according to the usage of that information by the user. During successive search session or during a search made by another user, the system uses the knowledge-database to support the users with keywords, queries and reference documents.