-
公开(公告)号：US10984029B2
公开(公告)日：2021-04-20
申请号：US15380970
申请日：2016-12-15
Applicant: SAP SE
Inventor： Daniela Maftuleac , Alejandro Lopez-Ortiz , Jeffrey Pound , Alejandro Salinger
Abstract: A bit vector having a bit vector length is accessed. A select operator directory tree can be generated using the bit vector. The select operator directory tree includes a first level of superblocks including large superblocks and small superblocks, a second level of blocks including large blocks and small blocks, each block associated with one of the superblocks, and a third level of sub-blocks, each sub-block associated with a block. The large superblocks each have, a length greater than a first constant that is independent of the bit vector length and the large blocks each have a length greater than a second constant that is independent of the bit vector length. The select operator directory tree can be stored. Related apparatus, systems, techniques and articles are also described.
-
公开(公告)号：US20230030608A1
公开(公告)日：2023-02-02
申请号：US17964490
申请日：2022-10-12
Applicant: SAP SE
Inventor： Marco Antonio Carniel Furlanetto , Alessandro Parolin , Cristiano Ruschel Marques Dias , Alejandro Salinger
Abstract: Techniques for implementing cross in-database machine learning are disclosed. In some example embodiments, a computer-implemented method comprises training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
-
公开(公告)号：US11494672B2
公开(公告)日：2022-11-08
申请号：US16870672
申请日：2020-05-08
Applicant: SAP SE
Inventor： Marco Antonio Carniel Furlanetto , Alessandro Parolin , Cristiano Ruschel Marques Dias , Alejandro Salinger
IPC: G06N5/04 , G06N20/00 , G06K9/62 , G06F16/2458
Abstract: In some example embodiments, a computer-implemented method may include training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
-
公开(公告)号：US11995535B2
公开(公告)日：2024-05-28
申请号：US18213071
申请日：2023-06-22
Applicant: SAP SE
Inventor： Marco Antonio Carniel Furlanetto , Alessandro Parolin , Cristiano Ruschel Marques Dias , Alejandro Salinger
IPC: G06N5/04 , G06F18/214 , G06F18/243 , G06N3/063 , G06N20/00 , G06F16/2458
CPC classification number: G06N3/063 , G06F18/214 , G06F18/24323 , G06N5/04 , G06N20/00 , G06F16/2458
Abstract: In some example embodiments, a computer-implemented method may include training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
-
公开(公告)号：US20210350254A1
公开(公告)日：2021-11-11
申请号：US16870672
申请日：2020-05-08
Applicant: SAP SE
Inventor： Marco Antonio Carniel Furlanetto , Alessandro Parolin , Cristiano Ruschel Marques Dias , Alejandro Salinger
Abstract: Techniques for implementing cross in-database machine learning are disclosed. In some example embodiments, a computer-implemented method comprises training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
-
Patent Agency Ranking
公开(公告)号：US10417208B2
公开(公告)日：2019-09-17
申请号：US15380985
申请日：2016-12-15
Applicant: SAP SE
Inventor： Alejandro Lopez-Ortiz , Daniela Maftuleac , Alejandro Salinger , Jeffrey Pound
IPC: G06F16/22 , G06F16/2455
Abstract: A plus-minus-one array in which adjacent entries vary by no more than positive one and no less than negative one is accessed. A range minimum query directory tree including blocks and subblocks of the plus-minus-one array is determined. Blocks are contained in the plus-minus-one array and subblocks are contained in the blocks. A data structure characterizing positions of minimum elements within the range minimum query directory tree is generated. The characterization includes positions of minimums within each subblock, between subblocks in a respective block, within each block, and between blocks. The data structure is stored. Related apparatus, systems, techniques and articles are also described.
-
公开(公告)号：US11755896B2
公开(公告)日：2023-09-12
申请号：US17964490
申请日：2022-10-12
Applicant: SAP SE
Inventor： Marco Antonio Carniel Furlanetto , Alessandro Parolin , Cristiano Ruschel Marques Dias , Alejandro Salinger
IPC: G06N5/04 , G06N20/00 , G06N3/063 , G06F18/214 , G06F18/243 , G06F16/2458
CPC classification number: G06N3/063 , G06F18/214 , G06F18/24323 , G06N5/04 , G06N20/00 , G06F16/2458
Abstract: In some example embodiments, a computer-implemented method may include training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
-
公开(公告)号：US20230334303A1
公开(公告)日：2023-10-19
申请号：US18213071
申请日：2023-06-22
Applicant: SAP SE
Inventor： Marco Antonio Carniel Furlanetto , Alessandro Parolin , Cristiano Ruschel Marques Dias , Alejandro Salinger
IPC: G06F18/214 , G06N5/04 , G06F18/243 , G06N3/063 , G06N20/00
CPC classification number: G06N3/063 , G06F18/214 , G06F18/24323 , G06N5/04 , G06N20/00 , G06F16/2458
Abstract: Techniques for implementing cross in-database machine learning are disclosed. In some example embodiments, a computer-implemented method comprises training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
-
公开(公告)号：US20180173738A1
公开(公告)日：2018-06-21
申请号：US15380985
申请日：2016-12-15
Applicant: SAP SE
Inventor： Alejandro Lopez-Ortiz , Daniela Maftuleac , Alejandro Salinger , Jeffrey Pound
IPC: G06F17/30
CPC classification number: G06F16/2237 , G06F16/2246 , G06F16/2455
Abstract: A plus-minus-one array in which adjacent entries vary by no more than positive one and no less than negative one is accessed. A range minimum query directory tree including blocks and subblocks of the plus-minus-one array is determined. Blocks are contained in the plus-minus-one array and subblocks are contained in the blocks. A data structure characterizing positions of minimum elements within the range minimum query-directory tree is generated. The characterization includes positions of minimums within each subblock, between subblocks in a respective block, within each block, and between blocks. The data structure is stored. Related apparatus, systems, techniques and articles are also described.
-
10.发明申请公开(公告)号：US20180173710A1
公开(公告)日：2018-06-21
申请号：US15380970
申请日：2016-12-15
Applicant: SAP SE
Inventor： Daniela Maftuleac , Alejandro Lopez-Ortiz , Jeffrey Pound , Alejandro Salinger
IPC: G06F17/30
CPC classification number: G06F16/3347 , G06F16/2237 , G06F16/322
Abstract: A bit vector having a bit vector length is accessed. A select operator directory tree can be generated using the bit vector. The select operator directory tree includes a first level of superblocks including large superblocks and small superblocks, a second level of blocks including large blocks and small blocks, each block associated with one of the superblocks, and a third level of sub-blocks, each sub-block associated with a block. The large superblocks each have, a length greater than a first constant that is independent of the bit vector length and the large blocks each have a length greater than a second constant that is independent of the bit vector length. The select operator directory tree can be stored. Related apparatus, systems, techniques and articles are also described.
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.032 seconds)
