Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Leland Stanford Junior University

Ebay, Incorporated

Micro Focus Lcc

Sandisk Corporation

Entit Software LLC

Hewlett-packard Company

The Board of Trustees of the Leland

Jun Li

Yi Cui

Ram Swaminathan

Sharad Singhal

Mijung Kim

Keith E Moore

Bryan Stephenson

Alan Hersh Karp

Richard M Myers

Huda Akil

Stanley Watson

Edward G Jones

Prabhakara V Choudary

Hiroaki Tomita

Simon J Evans

Juan F Lopez

Robert C Thompson

Marquis P Vawter

Manish Marwah

Gary Lee Vondran, Jr

Mehrdad Mofidi

Riddhiman Ghosh

Krishnamurthy Viswanathan

Kave Eshghi

Pratyusa Kumar Manadhata

Steven Chu

Thomas Rathbone

Ismail Ari

Prajit Nandi

William E Bunney

Viswa Vutharkar

Alexander Ulanov

Mohamed E Dekhil

Alan F Schatzberg

Haris Volos

Richard A Stein

Hernan Laffitte

Jhilmil Jain

Stanley J Watson

Xinglang Wang

Cortney Turner

Siddharth Mark Joseph

Caio Northfleet

Eric Wu

Menaka Indrani

Margherita Molnar

Donald E Bollinger

Margit Burmeister

Ajay Dhar

David M Lyons

Mohammad Roohitavaf

Alexander Kozlov

Elsa Durante

Nelson Souto Rosa

Fernando Antonio Aires Lins

Robson Wagner Albuquerque De Medeiros

Pradeep Fernando

Paulo Romero Martins Maciel

Julio Cesar Damasceno

Bruno Leonardo Barros Silva

Alvin Cheng-Tser Chen

Andre Ricardo Da Silva Souza

Hamid Reza Motahari-Nezhad

Hamid Reza Motahari Nezhand

Mary Atz

William E Bunney, Jr

William Byerley

Kathleen Casey

Matthew P Houseman

Brandi Rollins

Colin Heng Fai Tsui

Ilan Kerman

Rene Bernard

Michael Boehnke

Laura J Scott

Fan Meng

Cortney A Turner

David Michael Lyons

Devin Absher

René Bernard

William E Bunney Jr

Described are aspects of a sharded monitoring system that collects metrics data from a plurality of targets within a monitored system. The monitoring system includes multiple monitoring-server instances each collecting metrics data from a respective subset of the targets, as well as a federation server that collects aggregated metrics data from the other monitoring-server instances. Mappings between monitoring targets and monitoring-server instances collecting their metrics data may be stored in a temporal routing map, e.g., on the federation server, which may build the map based on time-series routing metrics received from the other monitoring-server instances. The routing map may be used to direct queries for metrics data from a client to the appropriate monitoring-server instance.

Time-series data monitoring with sharded server

The approaches presented herein may include loading a plurality of metadata instances for a plurality of data blocks stored in a database, each metadata instance of the plurality of metadata instances indicating a respective data block structure applied to aggregate a set of data messages. The approaches may include processing a first metadata instance corresponding to a first set of one or more data blocks of the plurality of data blocks and a second metadata instance corresponding to a second set of one or more data blocks of the plurality of data blocks to perform an anomaly storage check between the first set of one or more data blocks of the plurality of data blocks and the second set of one or more data blocks of the plurality of data blocks. The approaches may include generating an output based at least in part on the anomaly storage check.

Block aggregation for shared streams

A method performed by a processing system includes identifying a first node in a metadata tree of a patient that corresponds to an encrypted electronic health record in an encrypted data store and preventing a portion of the first node from being decrypted with a node key of a first healthcare participant in response to a second node of the metadata tree including key rotation information that indicates that the node key has been revoked by a second healthcare participant.

Metadata tree with key rotation information

Examples herein involve processing data in a distributed data processing system using an off-heap memory store. An example involves allocating a shared memory region of a shared memory to store attributes corresponding to a first partition of a distributed data system, and updating, in the shared memory region, the attributes corresponding to updates to the local data from process iterations of the first partition, such that a second partition of the distributed data system has access to the updated attributes.

Shared memory for distributed data

Described are aspects of a sharded monitoring system that collects metrics data from a plurality of targets within a monitored system. In various embodiments, the monitoring system includes multiple monitoring-server instances each collecting metrics data from a respective subset of the targets, as well as a federation server that collects aggregated metrics data from the other monitoring-server instances. Mappings between monitoring targets and monitoring-server instances collecting their metrics data may be stored in a temporal routing map, e.g., on the federation server, which may build the map, in accordance with some embodiments, based on time-series routing metrics received from the other monitoring-serve instances. The routing map may be used to direct queries for metrics data from a client to the appropriate monitoring-server instance. Further aspects and embodiments are disclosed.

Examples relate to deploying distributed applications using virtual nodes. In some examples, virtual nodes are created and are each assigned a core subset of a number of processing cores, an Internet protocol (IP) address, and an in-memory file system configured to provide access to a portion of physically shared memory. At this stage, a distributed application that is configured to be deployed to a plurality of machine nodes is deployed to the plurality of virtual nodes. On a first virtual node, a reference to a first dataset stored in physically shared memory is sent to a second virtual node, where the physically shared memory is accessible to each of the plurality of virtual nodes. Next, on the second virtual node, the first dataset is accessed through the in-memory file system of the first virtual node.

Virtual node deployments of cluster-based applications modified to exchange reference to file systems

In some examples, a system extracts features from event data representing events in a computing environment, trains ensembles of machine-learning models for respective analytics modules of a plurality of different types of analytics modules, and detects, by the different types of analytics modules using the respective trained ensembles of machine-learning models, an anomalous entity in response to further event data.

Detecting anomalous entities

Storing time series data for a search query includes identifying a time series whose representation is to be pre-computed based on available memory storage, pre-computing at least one representation of the identified time series, and storing the at least one representation in the memory storage.

Storing time series data for a search query

An example technique includes assigning partitions of a dataset of multidimensional points to a plurality of local memory nodes of a multicore machine and using the local memory nodes for a search query to determine similarity matches in the dataset for a given multidimensional point. The using includes parallel searching with the local memory nodes in the assigned partitions to identify candidate similarity matches to the given multidimensional point using indexes derived from the multidimensional points, the parallel searching for each node progressing through a sequence of search distances and providing an ongoing search result for each search distance from the given multidimensional point and regulating an extent of the parallel searching based on the ongoing search results.

Using local memory nodes of a multicore machine to process a search query

Examples herein involve fault tolerance in a shared memory. In examples herein, a metadata store of a shared memory indicating versions of data partitions of a resilient distributed dataset and a valid flag for the partitions of the resilient distributed dataset are used to achieve fault tolerance and/or recover from faults in the share memory.

Fault tolerance in shared memory

A method performed by a processing system includes reconstructing a metadata tree of a patient from a metadata tree journal, the metadata tree including a plurality of references to a corresponding plurality of encrypted electronic health records of the patient in an encrypted data store, and validating the metadata tree by comparing first integrity information of the metadata tree to second integrity information corresponding to the metadata tree journal provided by a metadata integrity validator.

Validating a metadata tree using a metadata integrity validator

A method performed by a processing system includes determining a location in a metadata tree of a patient for an electronic health record, generating a record key for the electronic health record based on the location and a provider key corresponding to a provider, the provider key generated from a patient key corresponding to the patient, encrypting the electronic health record using the record key to generate a encrypted record, and providing the encrypted record to an encrypted data store.

Encrypted data store for records

A method for secure distribution of data in an interchange network, comprises having a network in which data records stored on at least one computer; and including an authorization service, where the authorization service grants a contractor access to at least a portion of the data records; and a watermarking module that adds one or more artificial records to said portion. The artificial records cannot be distinguished from the data records by the contractor and are valid for processing in the same way as the data records.

Methods for secure data distribution

An encryption key fragment is divided into a number of encryption key fragments. Requests to store different ones of the encryption key fragments are transmitted to different computer memory storage systems. An individual request to store an encryption key fragment includes one of the encryption key fragments and bears an access control hallmark for regulating access to the encryption key fragment.

Encryption key management using distributed storage of encryption-key fragments

Scalable structured data store operations can include logging a number of detected corrupted records within a scalable structured data store, repairing the number of corrupted records within a number of nodes of the scalable structured data store, and predicting hardware failure based on the logged number of corrupted records.

Scalable structured data store operations

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Mountain View, CA, United States of America

Jun Li