公开(公告)号：US11709798B2

公开(公告)日：2023-07-25

申请号：US17500843

申请日：2021-10-13

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mehran Kafai ,  Kave Eshghi ,  Omar Aguilar Macedo

IPC: G06F7/00 ,  G06F16/13

CPC classification number: G06F16/137

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

公开(公告)号：US11599561B2

公开(公告)日：2023-03-07

申请号：US15142504

申请日：2016-04-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  April Slayden Mitchell ,  Kave Eshghi ,  Omar Aguilar ,  Hongwei Shang

IPC: G06F16/28 ,  G06F16/22 ,  G06F16/2455

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

公开(公告)号：US10810458B2

公开(公告)日：2020-10-20

申请号：US16073891

申请日：2015-12-03

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Hongwei Shang ,  Mehran Kafai ,  Kave Eshghi

IPC: G06F16/00 ,  G06K9/62 ,  G06F17/10 ,  H03M7/30 ,  G06F16/901 ,  G06F16/904

Abstract: Incremental automatic update of ranked neighbor lists based on k-th nearest neighbors is disclosed. One example is a system including an indexing module to retrieve an incoming data stream, and retrieve ranked neighbor lists for received data objects. An evaluator determines similarity measures between the received data objects and their respective k-th nearest neighbors. A threshold determination module determines a statistical distribution based on the determined similarity measures, and a threshold based on the statistical distribution. The evaluator determines additional similarity measures between a new data object in the data stream and the received data objects. A neighbor update module automatically selects a sub-plurality of the received data objects by comparing the additional similarity measures to the threshold, and determines, for each selected data object, if the respective retrieved neighbor list is to be incrementally updated based on neighborhood comparisons for the new data object and the selected data object.

公开(公告)号：US20170316338A1

公开(公告)日：2017-11-02

申请号：US15142357

申请日：2016-04-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Kave Eshghi ,  Mehran Kafai ,  Omar Aguilar Macedo

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06N20/00 ,  G06F16/285

Abstract: In some examples, a method includes accessing input vectors in an input space, wherein the input vectors characterize elements of a physical system. The method may also include generating feature vectors from the input vectors, and the feature vectors are generated without any vector product operations between performed between any of the input vectors. An inner product of a pair of the feature vectors may correlate to an implicit kernel for the pair of feature vectors, and the implicit kernel may approximate a Gaussian kernel within a difference threshold. The method may further include providing the feature vectors to an application engine for use in analyzing the elements of the physical system, other elements in the physical system, or a combination of both.

公开(公告)号：US10326585B2

公开(公告)日：2019-06-18

申请号：US15185727

申请日：2016-06-17

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kave Eshghi

IPC: H04L9/00 ,  G06F17/10

Abstract: A system may include an access engine to access an input vector as well as a projection matrix. The projection matrix may include a number of rows equal to a number of hash values to generate from the input vector multiplied by the square root of an inverted sparsity parameter specifying a ratio of the hash universe size from which the hash values are generated to the number of hash values to generate. The projection matrix may include a number of columns equal to the dimensionality of the input vector. The system may also include a hash computation engine to determine a projection vector from the projection matrix and the input vector, split the projection vector into a number of sub-vectors equal to the number of hash values to generate, and generate a hash value from each of the sub-vectors.

公开(公告)号：US20170316081A1

公开(公告)日：2017-11-02

申请号：US15142504

申请日：2016-04-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  April Slayden Mitchell ,  Kave Eshghi ,  Omar Aguilar ,  Hongwei Shang

IPC: G06F17/30

CPC classification number: G06F16/285 ,  G06F16/2228 ,  G06F16/24568 ,  G06F16/289

Abstract: Examples disclosed herein involve data stream analytics. In examples herein, a data stream may be analyzed by computing a set of hashes of a real-valued vector, the real-valued vector corresponding to a sample data object of a data stream; generating a list of data objects from a database corresponding to the sample data object based on the set of hashes, the list of data objects ordered based on similarity of the data objects to the sample data object of the data stream; and updating a data structure representative of activity of the sample data object in the data stream based on the list of data objects, the data structure to provide incremental analysis corresponding to the sample data object.

公开(公告)号：US20220066988A1

公开(公告)日：2022-03-03

申请号：US17500843

申请日：2021-10-13

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mehran Kafai ,  Kave Eshghi ,  Omar Aguilar Macedo

IPC: G06F16/13

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

公开(公告)号：US20170364517A1

公开(公告)日：2017-12-21

申请号：US15185727

申请日：2016-06-17

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kave Eshghi

IPC: G06F17/30 ,  G06F17/16

CPC classification number: G06F17/10 ,  H04L9/0866

Abstract: A system may include an access engine to access an input vector as well as a projection matrix. The projection matrix may include a number of rows equal to a number of hash values to generate from the input vector multiplied by the square root of an inverted sparsity parameter specifying a ratio of the hash universe size from which the hash values are generated to the number of hash values to generate. The projection matrix may include a number of columns equal to the dimensionality of the input vector. The system may also include a hash computation engine to determine a projection vector from the projection matrix and the input vector, split the projection vector into a number of sub-vectors equal to the number of hash values to generate, and generate a hash value from each of the sub-vectors.

公开(公告)号：US20200167312A1

公开(公告)日：2020-05-28

申请号：US16073961

申请日：2015-12-11

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kave Eshghi ,  Omar Aguilar Macedo

IPC: G06F16/13

Abstract: An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

公开(公告)号：US20170344589A1

公开(公告)日：2017-11-30

申请号：US15166026

申请日：2016-05-26

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kave Eshghi

IPC: G06F17/30

CPC classification number: G06F16/2237 ,  G06F16/2264

Abstract: A system may include an access engine and a projection engine. The access engine may access a feature vector with an initial dimensionality that represents a data object of a physical system. The projection engine may generate an extended vector with an extended dimensionality from the feature vector. The projection engine may also apply an orthogonal transformation to the extended vector to obtain an intermediate vector with the extended dimensionality, as well as compute the inner products of the intermediate vector and sparse binary vectors of a sparse binary vector set. In doing so, the projection engine may obtain a randomly projected vector with an output dimensionality that is greater than the extended dimensionality of the intermediate vector. Then, the projection engine may output the randomly projected vector as an output vector that is a random projection of the feature vector with the output dimensionality.