公开(公告)号：US10803053B2

公开(公告)日：2020-10-13

申请号：US16073836

申请日：2015-12-03

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Hongwei Shang ,  Omar Aguilar

IPC: G06F16/00 ,  G06F16/23 ,  G06F16/901 ,  G06F17/10 ,  G06F16/2458 ,  G06F16/2455 ,  G06F16/248

Abstract: Automatic selection of neighbor lists to be incrementally updated is disclosed. One example is a system including an indexing module to receive an incoming data stream, and retrieve neighbor lists for received data objects. An evaluator determines similarity measures between pairs of the received data objects. A threshold determination module determines distributions of order statistics based on the determined similarity measures and retrieved neighbor lists, and a threshold based on the distributions of order statistics. 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：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：US20190042893A1

公开(公告)日：2019-02-07

申请号：US16073921

申请日：2015-12-04

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kyriaki Dimitriadou

IPC: G06K9/62 ,  G06F17/30

Abstract: Incremental clustering of a data stream via an orthogonal transform based indexing is disclosed. One example is a system including an indexing module that retrieves a ranked neighbor list for a data object in a data stream, where the ranked list is based on an orthogonal transform based indexing of an incrementally updated nearest neighbor graph. A reverse neighbor determination module identifies a reverse neighbor list for the data object, the reverse neighbor list comprising previously received data objects that include the data object in their respective ranked lists. An evaluator determines a hub measure for the data object, where the hub measure is a size of the reverse neighbor list. A hub identification module determines, based on the hub measure, if the data object is a hub, where the hub is representative of a cluster of similar data objects.

公开(公告)号：US20190034479A1

公开(公告)日：2019-01-31

申请号：US16073836

申请日：2015-12-03

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Hongwei Shang ,  Omar Aguilar

IPC: G06F17/30

CPC classification number: G06F16/2379 ,  G06F16/24568 ,  G06F16/2465 ,  G06F16/248 ,  G06F16/9024 ,  G06F17/10

Abstract: Automatic selection of neighbor lists to be incrementally updated is disclosed. One example is a system including an indexing module to receive an incoming data stream, and retrieve neighbor lists for received data objects. An evaluator determines similarity measures between pairs of the received data objects. A threshold determination module determines distributions of order statistics based on the determined similarity measures and retrieved neighbor lists, and a threshold based on the distributions of order statistics. 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：US20180114028A1

公开(公告)日：2018-04-26

申请号：US15567531

申请日：2015-05-01

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mehran Kafai ,  Hongwei Shang ,  April Slayden Mitchell

IPC: G06F21/60 ,  G06F21/62 ,  G06F17/30

CPC classification number: G06F21/602 ,  G06F16/24578 ,  G06F21/6227 ,  G06F21/6254 ,  G06F2221/2115 ,  H04L9/3239 ,  H04L2209/46

Abstract: Secure multi-party information retrieval is disclosed. One example is a system including a query processor to request secure retrieval of candidate terms similar to a query term. A collection of information processors, where a given information processor receives the request and generates a random permutation. A plurality of data processors, where a given data processor generates clusters of a plurality of terms in a given dataset, where the clusters are based on similarity scores for pairs of terms, and selects a representative term from each cluster. The given information processor determines similarity scores between a secured query term received from the query processor and secured representative terms received from the given data processor, where the secured terms are based on the permutation, and the given data processor filters, without knowledge of the query term, the candidate terms of the plurality of terms based on the determined similarity scores.

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：US20220027204A1

公开(公告)日：2022-01-27

申请号：US17498150

申请日：2021-10-11

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mehran Kafai ,  Wen Yao ,  April Slayden Mitchell

IPC: G06F9/50

Abstract: Low-level nodes (LLNs) that are communicatively connected to one another each have sensing capability and processing capability. High-level nodes (HLNs) that are communicatively connected to one another and to the LLNs each have processing capability more powerful than the processing capability of each LLN. The LLNs and the HLNs perform processing based on sensing events captured by the LLNs. The processing is performed by the LLNs and the HLNs to minimize data communication among the LLNs and the HLNs, and to provide for software-defined sensing.

公开(公告)号：US11144793B2

公开(公告)日：2021-10-12

申请号：US16073921

申请日：2015-12-04

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Kyriaki Dimitriadou

IPC: G06F16/22 ,  G06K9/62 ,  G06F16/2457 ,  G06F16/28 ,  G06F16/2455

Abstract: Incremental clustering of a data stream via an orthogonal transform based indexing is disclosed. One example is a system including an indexing module that retrieves a ranked neighbor list for a data object in a data stream, where the ranked list is based on an orthogonal transform based indexing of an incrementally updated nearest neighbor graph. A reverse neighbor determination module identifies a reverse neighbor list for the data object, the reverse neighbor list comprising previously received data objects that include the data object in their respective ranked lists. An evaluator determines a hub measure for the data object, where the hub measure is a size of the reverse neighbor list. A hub identification module determines, based on the hub measure, if the data object is a hub, where the hub is representative of a cluster of similar data objects.

公开(公告)号：US11080301B2

公开(公告)日：2021-08-03

申请号：US15278253

申请日：2016-09-28

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mehran Kafai ,  Manav Das

IPC: G06F16/28 ,  G06F21/60 ,  G06F21/78

Abstract: Storage allocation based on secure data comparisons is disclosed. One example is a system including a plurality of intermediaries, a data allocator and a plurality of storage containers. Each intermediary receives a request from the data allocator to identify a target storage container of the plurality of storage containers, for secure allocation of a data term. Each intermediary compares, for each storage container, the truncated data term with a collection of truncated candidate terms to select a representative term of the candidate terms, identifies the selected representative term to the storage container, receives a similarity profile from each storage container, where the similarity profile is representative of similarities between the truncated data term and terms in the storage container, and selects a candidate target storage container based on similarity profiles received from each storage container.