Abstract: A system for managing graph data and methods for making and using same. A loading plan can be generated according to a loading job. Source data can be loaded into the graph model based on the loading plan. The loading job can be defined declaratively. An interpreter can encode the loading job to generate the loading plan having a tree structure. The loading plan can instruct a loading engine to load the source data. The loading engine can be compiled independent of the loading plan. Advantageously, the loading engine can be compiled no more than once. The compiled loading engine can interpret any loading plan. The compiled loading engine can filter or transform the source data at runtime of the loading. Advantageously, time for compiling the loading engine and reading source data can be saved. Graph data loading can have high flexibility and high performance.

Abstract: A system for managing graph data and methods for making and using same. A loading plan can be generated according to a loading job. Source data can be loaded into the graph model based on the loading plan. The loading job can be defined declaratively. An interpreter can encode the loading job to generate the loading plan having a tree structure. The loading plan can instruct a loading engine to load the source data. The loading engine can be compiled independent of the loading plan. Advantageously, the loading engine can be compiled no more than once. The compiled loading engine can interpret any loading plan. The compiled loading engine can filter or transform the source data at runtime of the loading. Advantageously, time for compiling the loading engine and reading source data can be saved. Graph data loading can have high flexibility and high performance.

Abstract: A database system may include a memory device configured to store a database dictionary and a processor in communication with the storage device. The processor may be configured to receive a temporal user-defined function (UDF) and to store, in the database dictionary, the temporal UDF and a period of time over which to apply the temporal UDF. A method may include receiving a temporal UDF and include determining a period of time over which to apply the temporal UDF. The method may further include storing the temporal UDF and the period of time over which to apply the temporal UDF. A computer-readable medium may include instructions to receive a temporal UDF and instructions to store the temporal UDF. The computer-readable medium may further include instructions to determine a period of applicability of the temporal UDF and instructions to store the period of applicability in a storage device.

Abstract: Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Abstract: Apparatus, systems, and methods may operate to receive requests to execute a plurality of compression and/or decompression mechanisms on one or more database objects; to execute each of the compression and/or decompression mechanisms, on a sampled basis, on the database objects; to determine comparative performance characteristics associated with each of the compression and/or decompression mechanisms; and to record at least some of the performance characteristics and/or derivative characteristics derived from the performance characteristics in a performance summary table. The table may be published to a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Abstract: A temporal target table stored on a computer is scanned using the computer. The temporal target table has a ValidTime dimension and a value dimension. A qualifying-row having a value dimension having a value equal to a value of a value dimension in a row-to-be-inserted is found during the scanning. The row-to-be-inserted has a ValidTime dimension and the value dimension. The qualifying-row is processed. The row-to-be-inserted is inserted in the temporal target table. A ValidTime dimension of the inserted row-to-be-inserted is set to be as the union of the ValidTime dimension of the row-to-be-inserted and the ValidTime dimension of the qualifying-row.

Abstract: Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Abstract: Apparatus, systems, and methods may operate to receive a set of ordered user-selected compression rules as a compression rule set comprising at least one compression threshold condition, to create or transform a database object with rows to be selectively compressed according to the compression rules in the compression rule set (providing a transformed object), and to publish at least a portion of the transformed object to one of a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Abstract: Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Abstract: Techniques for constructing and using run-time JAVA Archive (JAR) files for JAVA Stored Procedures (JSPs) are provided. JSP methods referenced by an application via a plurality of different JAR files are extracted and packed into a single packed JAR file. When the application tries to execute a JSP, the attempt to process the JSP methods is redirected to a new JSP that calls the single packed JAR file.

Abstract: Apparatus, systems, and methods may operate to receive a request to move at least a portion of a database table stored on a tangible medium from a current partition to a history partition, wherein the database table is partitioned into physical partitions according to a selected mapping update frequency. In response to receiving the request, activities may include modifying a logical partitioning of the database table by updating a mapping of the physical partitions to logical partitions. Other apparatus, systems, and methods are disclosed.

Abstract: Apparatus, systems, and methods may operate to receive requests to execute a plurality of compression and/or decompression mechanisms on one or more database objects; to execute each of the compression and/or decompression mechanisms, on a sampled basis, on the database objects; to determine comparative performance characteristics associated with each of the compression and/or decompression mechanisms; and to record at least some of the performance characteristics and/or derivative characteristics derived from the performance characteristics in a performance summary table. The table may be published to a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Abstract: Apparatus, systems, and methods may operate to receive a set of ordered user-selected compression rules as a compression rule set comprising at least one compression threshold condition, to create or transform a database object with rows to be selectively compressed according to the compression rules in the compression rule set (providing a transformed object), and to publish at least a portion of the transformed object to one of a storage medium or a display screen. Other apparatus, systems, and methods are disclosed.

Abstract: A learning-based method for estimating costs or statistics of an operator in a continuous query includes a cost estimation model learning procedure and a model applying procedure. The model learning procedure builds a cost estimation model from training data, and the applying procedure uses the model to estimate the cost associated with a given query. The learning procedure uses a feature extractor, a confidence adjustor and a cost estimator. The feature extractor collects relevant training data and obtains feature values. The extracted feature values are associated with costs and used to create the cost estimator. The extracted feature values, the associated costs, the cost estimator, and a user interface are used to create a confidence adjuster.

Abstract: Techniques for constructing and using run-time JAVA Archive (JAR) files for JAVA Stored Procedures (JSPs) are provided. JSP methods referenced by an application via a plurality of different JAR files are extracted and packed into a single packed JAR file. When the application tries to execute a JSP, the attempt to process the JSP methods is redirected to a new JSP that calls the single packed JAR file.

Abstract: A learning-based method for estimating costs or statistics of an operator in a continuous query includes a cost estimation model learning procedure and a model applying procedure. The model learning procedure builds a cost estimation model from training data, and the applying procedure uses the model to estimate the cost associated with a given query. The learning procedure uses a feature extractor and a cost estimator. The feature extractor collects relevant training data and obtains feature values. The extracted feature values are associated with costs and used to create the cost estimator. When applying the cost estimator to a continuous stream of data, the feature extractor extracts feature values from the data stream and uses the extracted feature values as inputs into the cost estimator to obtain the desired cost values.