Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER) storing planning and execution information for QEs from previous queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous queries. Additionally, dynamic join indexes representing QE results are created for high-value QEs selected from the QER and maintained within a DJI repository. During query plan creation for a current or subsequent query, the optimizer searches the QER and DJI repository for DJIs created for high-value QEs corresponding to QEs contained in the current or subsequent query. DJIs corresponding to the matching QEs are used in the query planning phase to rewrite the current or subsequent user query so that stored QE results are used to answer QEs contained in the current or subsequent query.

Abstract: A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database comprised of one or more tables storing data. The RDBMS executes a query with a semi-join operation comprising an inclusion join and/or an exclusion join performed against at least an outer table and an inner table, wherein the inclusion join returns a row from the outer table when there is a match with a row in the inner table, and the exclusion join returns a row from the outer table when there is no match with a row in the inner table. The RDBMS performs a rewrite of the query to avoid spooling and/or sorting of the inner table, when the inner table is larger than the outer table and a cost after the rewrite is lower than before the rewrite.

Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER) storing planning and execution information for QEs from previous queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous queries. Additionally, dynamic join indexes representing QE results are created for high-value QEs selected from the QER and maintained within a DJI repository. During query plan creation for a current or subsequent query, the optimizer searches the QER and DJI repository for DJIs created for high-value QEs corresponding to QEs contained in the current or subsequent query. DJIs corresponding to the matching QEs are used in the query planning phase to rewrite the current or subsequent user query so that stored QE results are used to answer QEs contained in the current or subsequent query.

Abstract: A compression system trains a machine-learned compression model that includes components for an encoder and decoder. In one embodiment, the compression model is trained to receive parameter information on how a target frame should be encoded with respect to one or more encoding parameters, and encodes the target frame according to the respective values of the encoding parameters for the target frame. In particular, the encoder of the compression model includes at least an encoding system configured to encode a target frame and generate compressed code that can be transmitted by, for example, a sender system to a receiver system. The decoder of the compression model includes a decoding system trained in conjunction with the encoding system. The decoding system is configured to receive the compressed code for the target frame and reconstruct the target frame for the receiver system.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: A compression system trains a machine-learned compression model that includes components for an encoder and decoder. In one embodiment, the compression model is trained to receive parameter information on how a target frame should be encoded with respect to one or more encoding parameters, and encodes the target frame according to the respective values of the encoding parameters for the target frame. In particular, the encoder of the compression model includes at least an encoding system configured to encode a target frame and generate compressed code that can be transmitted by, for example, a sender system to a receiver system. The decoder of the compression model includes a decoding system trained in conjunction with the encoding system. The decoding system is configured to receive the compressed code for the target frame and reconstruct the target frame for the receiver system.

Abstract: A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

Abstract: A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

Abstract: A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database comprised of one or more tables storing data. The RDBMS executes a query with a semi-join operation comprising an inclusion join and/or an exclusion join performed against at least an outer table and an inner table, wherein the inclusion join returns a row from the outer table when there is a match with a row in the inner table, and the exclusion join returns a row from the outer table when there is no match with a row in the inner table. The RDBMS performs a rewrite of the query to avoid spooling and/or sorting of the inner table, when the inner table is larger than the outer table and a cost after the rewrite is lower than before the rewrite.

Abstract: An apparatus, method and computer program product for physical database design and tuning in relational database management systems. A relational database management system executes in a computer system, wherein the relational database management system manages a relational database comprised of one or more tables storing data. A Deep Reinforcement Learning based feedback loop process also executes in the computer system for recommending one or more tuning actions for the physical database design and tuning of the relational database management system, wherein the Deep Reinforcement Learning based feedback loop process uses a neural network framework to select the tuning actions based on one or more query workloads performed by the relational database management system.

Abstract: A compression system trains a compression model for an encoder and decoder. In one embodiment, the compression model includes a machine-learned in-loop flow predictor that generates a flow prediction from previously reconstructed frames. The machine-learned flow predictor is coupled to receive a set of previously reconstructed frames and output a flow prediction for a target frame that is an estimation of the flow for the target frame. In particular, since the flow prediction can be generated by the decoder using the set of previously reconstructed frames, the encoder may transmit a flow delta that indicates a difference between the flow prediction and the actual flow for the target frame, instead of transmitting the flow itself. In this manner, the encoder can transmit a significantly smaller number of bits to the receiver, improving computational efficiency.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: A plurality of triggers may be presented and a selection of a predefined trigger may be accepted. Corresponding actions for the selected trigger may then be presented and an assignment of building control components may be accepted for the corresponding actions. During subsequent operation of a building automation system, the selected trigger may be detected and the corresponding actions may then be performed on the assigned building control components.

Abstract: In some examples, a system learns properties of an analytical function based on information of queries invoking the analytical function that have been previously executed, creates a function descriptor for the analytical function based on the learning, and provides the function descriptor for use by an optimizer in generating an execution plan for a received database query that includes the analytical function.

Abstract: A compression system trains a machine-learned compression model that includes components for an encoder and decoder. In one embodiment, the compression model is trained to receive parameter information on how a target frame should be encoded with respect to one or more encoding parameters, and encodes the target frame according to the respective values of the encoding parameters for the target frame. In particular, the encoder of the compression model includes at least an encoding system configured to encode a target frame and generate compressed code that can be transmitted by, for example, a sender system to a receiver system. The decoder of the compression model includes a decoding system trained in conjunction with the encoding system. The decoding system is configured to receive the compressed code for the target frame and reconstruct the target frame for the receiver system.

Abstract: A compression system trains a compression model for an encoder and decoder. In one embodiment, the compression model includes a machine-learned in-loop flow predictor that generates a flow prediction from previously reconstructed frames. The machine-learned flow predictor is coupled to receive a set of previously reconstructed frames and output a flow prediction for a target frame that is an estimation of the flow for the target frame. In particular, since the flow prediction can be generated by the decoder using the set of previously reconstructed frames, the encoder may transmit a flow delta that indicates a difference between the flow prediction and the actual flow for the target frame, instead of transmitting the flow itself. In this manner, the encoder can transmit a significantly smaller number of bits to the receiver, improving computational efficiency.

Abstract: A method and apparatus for optimizing a query in a relational database management system (RDBMS) when a predicate on a data column in the query has a correlation to a partitioning attribute of a partitioning column in data retrieved from a cloud-based store, wherein the optimizing uses the correlation between the data column in the query to the partitioning column in the data retrieved from the cloud-based store for data elimination when processing the query. The correlation is defined in a formula or lookup data structure that maps or range-maps from the data column to the partitioning column.

Abstract: The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.

Abstract: A system and method for loading data into column-partitioned database tables. The system and method incorporate a mechanism for buffering data extracted from the rows of a source table in column-oriented fashion within an in-buffer memory, enabling an efficient bulk-write of large arrays of values from the buffer into column-partitioned database tables. The system and method may also include optimizations for grouping columns according to data types and altering the order in which columns are inserted into the database tables.