Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.

Methods and systems are for using machine learning models to locate information in an organizational graph. A search system may use techniques described herein to determine relevant data (e.g., organizational knowledge) to retrieve from a knowledge graph for input to a machine learning model. The search system may retrieve more relevant data from the knowledge graph through the use of time data that may enable the search system to avoid outdated information. The search system may also limit the data that may be used in determining an answer to a query. By doing so, the search system may be able to answer queries more efficiently (e.g., using less computing resources, less processing power, etc.).

Systems and methods are disclosed for supporting transformations of a graph generated from a query to event data. The event data may be unstructured event data, from which instances of a journey can be identified that represent sequences of related events describing actions performed in a computing environment. When evaluating journey instances, it can be helpful to visualize the instances as a graph. Depending on the instances viewed, a user may desire different modifications to the graph. While such modifications can be made when initially building instances from the unstructured event data, this can limit reuse of the resulting instances (since the modification would also be present when evaluating other subsets). To address this, embodiments of the present disclosure enable graph modifications to be applied to subsets of journey instances after building those instances from unstructured event data, increasing reuse of instances built from a query against the unstructured data.

The technology disclosed teaches protecting sensitive data in the cloud via indexable databases. The method includes identifying sensitive fields of metadata for encryption and for hashing. The method also includes hashing at least partial values in the indexable sensitive fields to non-reversible hash values, concatenating the non-reversible hash values with the metadata for the network events, and encrypting the sensitive fields of metadata. Also included is sending the metadata for the network events, with the non-reversible hash values and the encrypted sensitive fields, to a remote database server that does not have a decryption key for the encrypted sensitive fields and that indexes the non-reversible hash values for indexed retrieval against the indexable sensitive fields. The disclosed technology also teaches retrieving sensitive information that is secured at rest: receiving a sensitive field query, hashing the query, querying and receiving network event metadata responsive to the query, and decrypting the metadata.

In an embodiment, each of a set of subgraphs associating an entity from an entity graph with an item is extracted from a graph database. A label score, which is an importance of an item to a respective entity is computed for each subgraph. A training dataset including the set of subgraphs and the label score for each subgraph is generated. A set of ML regression models is trained on respective entity-specific subsets of the training dataset. An ML regression model associated with a second entity generates a prediction score for an unseen graph. From the set of subgraphs, one or more subgraphs associated with the second entity are determined based on the prediction score. A recommendation for one or more items is determined, based on the one or more subgraphs. The recommendation is displayed on a user device of the first entity.

Systems and methods for performing Document Visual Question Answering tasks are described. A document and query are received. The document encodes document tokens and the query encodes query tokens. The document is segmented into nested document sections, lines, and tokens. A nested structure of tokens is generated based on the segmented document. A feature vector for each token is generated. A graph structure is generated based on the nested structure of tokens. Each graph node corresponds to the query, a document section, a line, or a token. The node connections correspond to the nested structure. Each node is associated with the feature vector for the corresponding object. A graph attention network is employed to generate another embedding for each node. These embeddings are employed to identify a portion of the document that includes a response to the query. An indication of the identified portion of the document is be provided.

A building system of a building including one or more memory devices having instructions thereon, that, when executed by one or more processors, cause the one or more processors to identify a capability of an entity in a building graph, the building graph including a plurality of nodes and a plurality of edges, the plurality of nodes representing entities of the building including the entity and the capability, the plurality of edges representing relationships between the entities of the building and the capability. The instructions cause the one or more processors to receive a command to perform an operation associated with the entity, the operation associated with the capability of the entity and provide the command to perform the operation associated with the entity to an operating system.

Techniques are described for enabling in-memory execution of any-sized graph data query by utilizing both depth first search (DFS) principles and breadth first search (BFS) principles to control the amount of memory used during query execution. Specifically, threads implementing a graph DBMS switch between a BFS mode of data traversal and a DFS mode of data traversal. For example, when a thread detects that there are less than a configurable threshold number of intermediate results in memory, the thread enters BFS-based traversal techniques to increase the number of intermediate results in memory. When the thread detects that there are at least the configurable threshold number of intermediate results in memory, the thread enters DFS mode to produce final results, which generally works to move the intermediate results that are currently available in memory to final query results, thereby reducing the number of intermediate results in memory.

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Methods and systems are provided for ranking search results and generating a presentation. In some implementations, a search system generates a presentation based on a search query. In some implementations, a search system ranks search results based on data stored in a knowledge graph. In some implementations, a search system identifies a modifying concept such as a superlative in a received search query, and determines ranking properties based on the modifying concept.