Abstract: Improved techniques for selecting a handover target cell are described herein, for example in connection with a 5G disaggregated radio network. For example, in addition to evaluating signal quality metrics that are reported by the user equipment subject to the handover, the disclosed techniques can also evaluate additional metrics. For example, a load associated with potential target cells can be considered as neighbor load can cause handover issues. As another example, a history of success rates associated with a handover to the target cell and/or from the serving cell can be considered as well since handover failures can sometimes result due to issues that can be surfaced by the success rate history.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Generally provided is a system for determining unknown, new and/or changed radio system architecture/topology, including available system connections between nodes, NFs, CU-UPs, DUs and/or CU-CPs. An example system can comprise a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a request from a user equipment (UE) for network access, and selecting a central unit user plane (CU-UP) to which to assign the UE based on a known aspect of a network topology of a communication network comprising the CU-UP. The system can provide a learned and dynamic approach to network topology analysis, and can allow for determining a CU-UP based on network topology, connection availability, loading, downtime, traffic connections, bandwidth and/or one or more other KPIs.

Abstract: Technology described herein can employ dynamically changing network variables and/or context, in realtime, to determine one or more possible contexts that can lead to network function (NF) misconfiguration, NF mismatch and/or key performance indicator (KPI) degradation. In an embodiment, an example system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a working configuration of a network function, and detecting a misconfiguration between a specified configuration and the working configuration of the network function, wherein the detecting is based on a context known to be associated with the misconfiguration. Analysis of context can comprise analyzing, based on an artificial intelligence model, the contexts of the network function as compared to known and/or stored contexts.

Abstract: The described technology is generally directed towards scheduling, by a distributed unit, the injection of custom traffic (signals/data) into a radio unit communications path. The distributed unit coordinates with the radio unit to schedule and synchronize such custom traffic in unscheduled (physical resource block), such as interleaved with to live-air and non-live-air traffic. The radio unit can request the unscheduled physical resource blocks for custom traffic to be inserted by the radio unit. Alternatively, the distributed unit can inject the custom traffic in otherwise unscheduled physical resource blocks for sending to the radio unit. The custom traffic is configured to perform some action by the radio unit, such as to perform antenna calibration, to perform test and measurement operations to obtain performance data, and the like. Performance data can be used, for example, to modify operating parameters of the radio unit to improve performance of the radio unit.

Abstract: The described technology is generally directed towards scheduling, by a distributed unit, the injection of custom traffic (signals/data) into a radio unit communications path. The custom traffic can be used to improve the performance of the radio unit, such as to perform antenna calibration, to perform test and measurement operations to obtain performance data, which can be used, for example, to modify operating parameters of the radio unit to improve performance of the radio unit. The distributed unit coordinates with the radio unit to schedule and synchronize such custom traffic in unscheduled (physical resource block), such as interleaved with to live-air and non-live-air traffic. Further, the distributed unit, which has scheduling knowledge of upcoming traffic of the radio unit, can communicate such knowledge to the radio unit for the radio unit to change its operating state based on the upcoming traffic knowledge.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Described is a system, method, and computer program product to perform bi-directional mapping of hierarchical data (e.g. JSON, XML) to database object types (e.g., user defined database object types).

Abstract: Techniques are introduced herein for maintaining geometry-type data on persistent storage and in memory. Specifically, a DBMS that maintains a database table, which includes at least one column storing spatial data objects (SDOs), also maintains metadata for the database table that includes definition data for one or more virtual columns of the table. According to an embodiment, the definition data includes one or more expressions that calculate minimum bounding box values for SDOs stored in the geometry-type column in the table. The one or more expressions in the metadata maintained for the table are used to create one or more in-memory columns that materialize the bounding box data for the represented SDOs. When a query that uses spatial-type operators to perform spatial filtering over data in the geometry-type column is received, the DBMS replaces the spatial-type operators with operators that operate over the scalar bounding box information materialized in memory.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Functionalities of new data types are enabled for data stored using native data types (“recognized data types”) supported by DBMSs. Values having a latent data type may be stored using a recognized data type of a DBMS. That is, a value stored in a database table may be recognized by a DBMS as having a recognized data type (e.g., character string) but the value may also include information that indicates the value conforms to a particular latent data type. DBMSs compile database statements. The database statement can contain multiple expressions that may specify a latent data type or that may have a recognized data type but that also imply latent data types. By determining that various expressions have latent data types, operations that rely on the expressions as having a latent data type may be validated in a query and executed accordingly.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: A “hybrid derived cache” stores semi-structured data or unstructured text data in an in-memory mirrored form and columns in another form, such as column-major format. The hybrid derived cache may cache scalar type columns in column-major format. The structure of the in-memory mirrored form of semi-structured data or unstructured text data enables and/or enhances access to perform path-based and/or text based query operations. A hybrid derived cache improves cache containment for executing query operations. The in-memory mirrored form is used to compute queries in a transactionally consistent manner through the use of an invalid vector that used to determine when to retrieve the transactionally consistent persistent form of semi-structured data or unstructured text data in lieu of the in-memory form.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: The present invention relates to autonomous tuning of a data grid of documents in a database. Herein are techniques for storage cells to autonomously maintain local indices and other optimization metadata and algorithms to accelerate selective access into a distributed collection of documents. In an embodiment, each storage cell persists a respective subset of documents. Each storage cell stores, in memory, respective index(s) that map each item to location(s), in one or more documents of the respective subset of documents, where the item occurs. One or more computers execute, based on at least a subset of the indices of the storage cells, a data access request from a database management system. In an embodiment, a cloud of JSON document services provides an easy-to-use, fully autonomous JSON document database that horizontally and elastically scales to deliver fast execution of document transactions and queries without needing tuning by a database administrator.

Abstract: Techniques are introduced herein for maintaining geometry-type data on persistent storage and in memory. Specifically, a DBMS that maintains a database table, which includes at least one column storing spatial data objects (SDOs), also maintains metadata for the database table that includes definition data for one or more virtual columns of the table. According to an embodiment, the definition data includes one or more expressions that calculate minimum bounding box values for SDOs stored in the geometry-type column in the table. The one or more expressions in the metadata maintained for the table are used to create one or more in-memory columns that materialize the bounding box data for the represented SDOs. When a query that uses spatial-type operators to perform spatial filtering over data in the geometry-type column is received, the DBMS replaces the spatial-type operators with operators that operate over the scalar bounding box information materialized in memory.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.