A query rewriting processor (processor) analyzes database semantic models (e.g., RDF knowledge graphs) that capture the interconnections (e.g., foreign and primary key links to other tables) present in a relational database. The processor generates an enriched model query given an initial model query (e.g., a SPARQL query) against the semantic model. The processor generates the enriched model query and translates the enriched model query into a relational database query (e.g., an SQL query). The processor may then pass the relational database query to another system or process (e.g., a data virtualization layer) for execution against the individual relational databases. In this manner, the processor automatically translates queries for information about the relational database structure to a corresponding or matching query for data from the relational database structure.

Methods and systems are provided for searching information in a distributed data processing system. A system for processing a semantic search query where the system may include a memory and a processor coupled to the memory being configured to, receive a structured search query, process the structured search query to deconstruct into query elements, identify a set of connected elements that define a data source associated with the received structured search query based on a processed query element, process the query elements to determine one or more command data element types associated with the received structured search query, and process data associated with the defined data source according to a command data element type to develop a semantic search query resultant data set.

A binary relational database model is described whereby application-layer object structures are easily inferred from database query templates. The object structures take the form of acyclic hypergraphs, which are induced from primal graphs representing query templates. Database applications may iterate through the collection of returned object structures, accessing the data in each structure. The returned object structures are not based on a fixed object model, thereby permitting rich structures with greater applicability than traditional ORM systems. A relationship between non-primitive entities may be directly expressed without the need for alternative join tables. Development and maintenance costs are thus substantially reduced, and data is more efficiently stored and manipulated for database applications.

Electronic communications between a client device and a server device are improved by providing a middleware component that incorporates electronic data read and/or written to a database in a hybrid data structure. The hybrid structure is further designed to allow for “NULL” or other pre-defined data values when one or more data fields are unavailable or erroneous. The client device, in turn, can be configured to check for the pre-defined data values in certain fields and to gracefully process such values. The hybrid structure with pre-defined error values therefore provides for very efficient data transmittal and processing, while retaining the ability to handle errors or other unusual situations relating to the data.

To effectively utilize knowledge of relationship information among material property parameters the users tangibly and intangibly have in a search system that generates a graph in which material property parameters are nodes and relationships of the material property parameters are edges from a database of material property parameter pairs whose relationships are already known, and conducts a path search in the generated graph. A search system, which includes the database, a graph generator that generates the graph, and a graph searcher searches the graph, further includes a user interface and a user information storage unit corresponding to each user. The user conducts a search unique to the user by inputting relationship information between the material property parameters that he has to the user information storage unit and integrating the relationship information into the above graph. Further, by accumulating a history of searches conducted by the user in the user information storage unit and analyzing the search history, the user can be provided with new knowledge.

One implementation of the present disclosure is a building system of a building including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive a query from a requesting system, the query including one or more query parameters and a context, the context indicating one or more purposes for the query including the one or more query parameters. The instructions cause the one or more processors to retrieve, based on the one or more query parameters, first data of the building system from a data storage system, identify, based on the context, one or more processing operations to perform to generate a processing result with the first data, perform the one or more processing operations with the first data to generate second data, and provide a response to the requesting system with the second data.

Methods and systems are provided for searching time series information in a distributed data processing system. A method of processing a semantic search query comprises receiving a structured search query, processing the structured search query to deconstruct into query elements, identifying a set of connected elements based on the query elements, processing a time series data structure of the identified set of connected elements to determine a command data element, utilizing the command data element to process the time series data structure of the identified set of connected elements, annotating the time series data structure of each of the identified set of connected elements to form a queried data set, and providing the queried data set.

In various examples, searching of data—including real-world data, simulation data, system under test (SUT) data, and/or map data—may be executed using a query engine configured to compile detailed binary code from high-level declarative queries for searching the data to identify scenarios or engineering artifacts of interest. A user may identify a behavior or scenario of interest, define the behavior or scenario in a descriptive and/or declarative manner—including implicit indications of temporal or spatial relationships—and the query engine may then compile an explicit procedural description that may be used to search the data for one or more instances and/or variations of the defined scenario or computational representation of an engineering artifact under investigation. Once the scenarios are identified, behaviors of the machine may be observed, criteria with respect to the machine performance may be evaluated, and/or test coverage with respect to the scenario type may be collected.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive an event from an event source, the event comprising data and a timestamp. The building system operates to identify first contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building, enrich the event with the first contextual data, and provide the enriched event to a consuming system, the consuming system generating an output event based on the enriched event. The building system operates to identify second contextual data describing the output event in the digital twin, enrich the output event with the second contextual data, and provide the enriched output event to the consuming system or another consuming system.

Methods, systems, and computer-readable media are disclosed herein that create inter-communication capabilities between servers that use technologically diverse and/or incompatible operating systems. Herein, a driver is leveraged to create these inter-communication capabilities by inserting a source server generated message into a table at the target server. The target server automatically executes code in the message and generates results therefrom. The target serer communicates the results to the source server, where the source server again leverages the driver to handle the results.