A database system receives a query and determines that the query includes an inner join between a parent table and a child table. The database system determines that the following relationships exists between the parent table and the child table: referential integrity (“RI”) between a primary key attribute (pk) in the parent table and a foreign key attribute (fk) in the child table and a temporal relationship constraint (“TRC”) between a period attribute in the parent table and a TRC-attribute in the child table. The database system determines that the query satisfies non-temporal join elimination conditions and temporal join elimination conditions and that the query contains no other qualification conditions on the parent table's period attribute and eliminates the inner join when planning execution of the query.

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

One example method includes receiving data, partitioning the data according to their respective similarity groups, and the similarity groups collectively define a range of similarity groups, deduplicating the data after the partitioning, packing unique data segments remaining after deduplicating into one or more compression regions, compressing the compression regions, and writing an object, that includes the compression regions, to a durable log. The deduplicating and compressing for a similarity group may be performed by a dedup-compression instances uniquely assigned to that similarity group.

Diffusing a root identity of an entity among association and event covenants in a multi-dimensional computing security system involves generating a first generation of diffusion of identities of entities participating in mediated association and generating a second generation of diffusion of identities of the entities through recombinant mediated association of the entities and at least one other entity. The second generation of diffusion of identities facilitates securely constraining a computing system action associated with one of the entities.

Embodiments for enabling granular migration of data with high efficiency. A defined metadata element, a tag, is assigned to each file, and then tag filtering is used to direct the data to the proper location. Files with different tags can be selected for transfer, and such a group of tags is referred to as a tag set. Embodiments can be used with a defined backup system file migration process, such as present in the Data Domain File System. By using snapshots, incoming new data (ingested file) is allowed to continue while the migration is in process and maintaining data consistency at the same time. This is achieved by performing operations on B+ Tree snapshots in conjunction with tag filtering on keys present in the leaf pages of these structures. This method is efficient became it makes a single pass walk of a B+ Tree in contrast with previous methods that look up files one-by-one via their pathname.

A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

A data linking system is described herein that links data records corresponding to a particular real estate property even if there are inconsistencies in the data records, the physical presence of the real estate property has changed over time, and/or the data records use different terminology. In some cases the data records are matched using a trained machine learning model. The data linking system can optionally generate a visualization of the data record linkage via interactive user interfaces. By linking data records despite the issues described above, the data linking system reduces the number of navigational steps a user performs to obtain data associated with a property and/or reduces data processing times. The disclosed system may be used to generate and maintain a comprehensive database of substantially all properties within a jurisdiction, in which a unique identifier is assigned to each property.

A digital security system can store data associated with entities in resolver trees. If the digital security system determines that two resolver trees are likely representing the same entity, the digital security system can use a merge operation to merge the resolver trees into a single resolver tree that represents the entity. The single resolver tree can include a merge node indicating a merge identifier of the merge operation. Nodes containing information merged into the resolver tree from another resolver tree during the merge operation can be tagged with the corresponding merge identifier. Accordingly, if the merge operation is to be undone, for instance if subsequent information indicates that the entries are likely separate entities, the resolver tree can be unmerged and the nodes tagged with the merge identifier can be restored to a separate resolver tree.

The present disclosure is generally directed to the generation of domain-specific, voice-activated systems in interconnected networks. The system can receive input signals that are detected at a client device. The input signals can be voice-based input signals, text-based input signals, image-based input signals, or other type of input signals. Based on the input signals, the system can select domain-specific knowledge graphs and generate responses based on the selected knowledge graph.

Methods and apparatus for executing a data operation are described herein. The methods and systems may include determining at least one subdivision of at least one logical hierarchical data space. The at least one logical hierarchical data space may have a plurality of subdivisions. The method may further include determining at least one file corresponding to the at least one subdivision of the at least one logical hierarchical data space. The method may further include reading at least one tuple from the at least one file.