公开(公告)号：US10558678B2

公开(公告)日：2020-02-11

申请号：US16124126

申请日：2018-09-06

Applicant: SAP SE

Inventor： Lars Dannecker ,  Gordon Gaumnitz ,  Boyi Ni ,  Yu Cheng

IPC: G06F16/00 ,  G06F16/248 ,  G06F16/22 ,  G06F3/06

Abstract: A flexible approach for storing time series data, utilizes multiple representations in order to achieve optimization amongst various dimensions such as covered time period, accuracy, compression model, and storage medium. A first aspect selectively provides different representations for various portions of a time series along the time axis (horizontal). In this horizontal aspect, a first compression model may be applied to store data occurring during one period in a time series, while a different compression model is applied to store data during another period. In a separate (vertical) aspect, time series data over a same time period may be saved in multiple representations using different compression models. While increasing demands on memory, such vertical storage can afford efficient access for specific purposes (i.e., analytical). Storing time series data according to horizontal and/or vertical distributions, may be useful for applications such as data aging and the optimization of operator execution patterns.

公开(公告)号：US10387419B2

公开(公告)日：2019-08-20

申请号：US14045656

申请日：2013-10-03

Applicant: SAP SE

Inventor： Wen-Syan Li ,  Yu Cheng

IPC: G06F16/2455 ,  G16H10/60

Abstract: The method includes selecting a target record from a dataset, the target record including a missing value, partitioning records of the dataset into at least two groups including co-related data, the partitioned records including records having a value for a same field as the missing value in the target record, predicting the missing value based on a relationship between fields in each of the at least two groups associated with the partitioned records, and setting the missing value of the target record to the predicted value.

公开(公告)号：US20190005102A1

公开(公告)日：2019-01-03

申请号：US16124126

申请日：2018-09-06

Applicant: SAP SE

Inventor： Lars Dannecker ,  Gordon Gaumnitz ,  Boyi Ni ,  Yu Cheng

IPC: G06F17/30 ,  G06F3/06

CPC classification number: G06F16/248 ,  G06F3/061 ,  G06F3/0661 ,  G06F3/0673 ,  G06F16/2291

Abstract: A flexible approach for storing time series data, utilizes multiple representations in order to achieve optimization amongst various dimensions such as covered time period, accuracy, compression model, and storage medium. A first aspect selectively provides different representations for various portions of a time series along the time axis (horizontal). In this horizontal aspect, a first compression model may be applied to store data occurring during one period in a time series, while a different compression model is applied to store data during another period. In a separate (vertical) aspect, time series data over a same time period may be saved in multiple representations using different compression models. While increasing demands on memory, such vertical storage can afford efficient access for specific purposes (i.e., analytical). Storing time series data according to horizontal and/or vertical distributions, may be useful for applications such as data aging and the optimization of operator execution patterns.

公开(公告)号：US10095757B2

公开(公告)日：2018-10-09

申请号：US14961502

申请日：2015-12-07

Applicant: SAP SE

Inventor： Lars Dannecker ,  Gordon Gaumnitz ,  Boyi Ni ,  Yu Cheng

IPC: G06F17/00 ,  G06F17/30 ,  G06F3/06

Abstract: A flexible approach for storing time series data, utilizes multiple representations in order to achieve optimization amongst various dimensions such as covered time period, accuracy, compression model, and storage medium. A first aspect selectively provides different representations for various portions of a time series along the time axis (horizontal). In this horizontal aspect, a first compression model may be applied to store data occurring during one period in a time series, while a different compression model is applied to store data during another period. In a separate (vertical) aspect, time series data over a same time period may be saved in multiple representations using different compression models. While increasing demands on memory, such vertical storage can afford efficient access for specific purposes (i.e., analytical). Storing time series data according to horizontal and/or vertical distributions, may be useful for applications such as data aging and the optimization of operator execution patterns.

公开(公告)号：US10896458B2

公开(公告)日：2021-01-19

申请号：US16371385

申请日：2019-04-01

Applicant: SAP SE

Inventor： Zhan Shi ,  Yu Cheng ,  Gufei Sun ,  Wen-Syan Li

IPC: G06Q30/00 ,  G06Q30/06 ,  G06F16/2457

Abstract: Disclosed herein are technologies for providing recommendations as to particular products and/or services that are customer specific and general, based on customer preference and inquiry. The recommendations are provided as part of an online shopping system. In accordance with one aspect, an item query is received from a customer, and analyzed by a query analyzer to determine if the query is a general item query or a specific item query. A search may be performed for items based on the item query in an items database listing items offered for purchase. If the query is the general item query, customer preference is determined from results of the search. If the query is the specific item query, the items from the results of the search are grouped based on cost performance. The items of the search result are ranked and provided to the customer.

公开(公告)号：US10223649B2

公开(公告)日：2019-03-05

申请号：US14884773

申请日：2015-10-16

Applicant: SAP SE

Inventor： Yu Cheng ,  Wen-Syan Li

IPC: G06Q10/04

Abstract: A technology for multi-objective optimization is provided. In accordance with one aspect, historical data and real-time data of a network of interest are provided in an integrated database. User input containing a problem to be solved and user preference for solving the problem is received. An optimization function is identified for generating at least one solution to the problem. The optimization function is identified based on the user preference. In response to identifying a single objective optimization function, the optimization function is initiated from a single objective optimization function library. In response to identifying a multi-objective optimization function, the optimization function is initiated from a multi-objective optimization function library. The optimization function retrieves the historical and real-time data based on the user preference for solving the problem. A result comprising at least one solution to the problem is provided.

公开(公告)号：US09877169B1

公开(公告)日：2018-01-23

申请号：US15383628

申请日：2016-12-19

Applicant: SAP SE

Inventor： Mengjiao Wang ,  Yu Cheng ,  Wen-Syan Li

IPC: H04B5/00 ,  H04W4/08 ,  H04W4/10

CPC classification number: H04W4/08 ,  H04W4/10

Abstract: Software techniques are described for optimizing resource assignments among assignees of those resources, by using dynamic priority updating over a plurality of rounds of resource assignment. In particular, in example implementations, a greedy algorithm is used to optimize the resource assignments over multiple rounds based on ranked selections within each selection set of each assignee, including dynamically updating the priority of the assignees at each round, based on assignment results from one or more preceding assignment rounds.

公开(公告)号：US10685306B2

公开(公告)日：2020-06-16

申请号：US14961456

申请日：2015-12-07

Applicant: SAP SE

Inventor： Lars Dannecker ,  Gordon Gaumnitz ,  Boyi Ni ,  Yu Cheng

IPC: G06Q10/00 ,  G06F16/00 ,  G06Q10/06 ,  G06F3/06 ,  G06F16/2453 ,  G06F16/2458 ,  G06Q30/02

Abstract: An advisor creates configurations for generating multi-representations of time series data based upon detected characteristics such as length, interval, minimums, data types, etc., as well as configurations provided by a user. In an offline mode the advisor may further consider a previous time series workload. In an on-line mode the advisor may adapt multi-representation configurations with respect to ongoing changes in a current time series workload. The advisor may reference a cost model including values quantifying various dimensions (e.g., compression technique, accuracy, covered time period, storage medium, memory consumption, speed) of the multi-representations for optimization purposes. Configurations created by the advisor may be input to a storage engine to generate and store the multi-representations according to goals for data aging, operation execution pattern optimization, and ease of access to time series data located in hot zones. The advisor may be implemented with an engine of an in-memory database.

公开(公告)号：US20190228451A1

公开(公告)日：2019-07-25

申请号：US16371385

申请日：2019-04-01

Applicant: SAP SE

Inventor： Zhan Shi ,  Yu Cheng ,  Gufei Sun ,  Wen-Syan Li

IPC: G06Q30/06 ,  G06F16/2457

Abstract: Disclosed herein are technologies for providing recommendations as to particular products and/or services that are customer specific and general, based on customer preference and inquiry. The recommendations are provided as part of an online shopping system. In accordance with one aspect, an item query is received from a customer, and analyzed by a query analyzer to determine if the query is a general item query or a specific item query. A search may be performed for items based on the item query in an items database listing items offered for purchase. If the query is the general item query, customer preference is determined from results of the search. If the query is the specific item query, the items from the results of the search are grouped based on cost performance. The items of the search result are ranked and provided to the customer.

公开(公告)号：US20170161340A1

公开(公告)日：2017-06-08

申请号：US14961502

申请日：2015-12-07

Applicant: SAP SE

Inventor： Lars Dannecker ,  Gordon Gaumnitz ,  Boyi Ni ,  Yu Cheng

IPC: G06F17/30

CPC classification number: G06F17/30554 ,  G06F3/061 ,  G06F3/0661 ,  G06F3/0673 ,  G06F17/30342

Abstract: A flexible approach for storing time series data, utilizes multiple representations in order to achieve optimization amongst various dimensions such as covered time period, accuracy, compression model, and storage medium. A first aspect selectively provides different representations for various portions of a time series along the time axis (horizontal). In this horizontal aspect, a first compression model may be applied to store data occurring during one period in a time series, while a different compression model is applied to store data during another period. In a separate (vertical) aspect, time series data over a same time period may be saved in multiple representations using different compression models. While increasing demands on memory, such vertical storage can afford efficient access for specific purposes (i.e., analytical). Storing time series data according to horizontal and/or vertical distributions, may be useful for applications such as data aging and the optimization of operator execution patterns.