Hewlett-packard Development Company, L.p.

Gaurav Sujit Roy

Amit Kumar Singh

Kevin Williams

Ronnie Glenn Blewer

Shakti Ashirvad

Juan M Venegas, Iii

Binh T Truong

Nileshkumar Gawali

Mengqi Hei

Sanket Anavkar

Martin Schwarz

Prateek Tiwari

Adnan Mirza

Francisco Beschoren Da Costa

Shiva Bansal

Madhurya Sarma

Aravind Iyengar

Siddhesh Arolkar

Chetan Satpute

Sumit Raut

Sakshi Kandya

Example implementations relate to determining router and computing device performance. An example non-transitory machine-readable medium can include instructions executable by a processor to capture, from an agent device on a computing device of a network, computing device data and network connectivity data associated with the computing device and filter data associated with connectivity to non-WiFi networks from the captured computing device data and network connectivity data. The instructions can be executable to filter out-of-network data from the captured computing device data and network connectivity data, correlate the filtered data with wireless router performance data, and determine router and computing device performances based on the correlation.

Network connectivity performance determination on computing devices

An example of an apparatus including a network interface to receive enrollment data from a device, wherein the device is identified by a device identifier. The apparatus further includes a memory storage unit connected to the network interface. The memory storage unit receives device history data associated with the device identifier while the device is enrolled. The memory storage unit is also to maintain a database to store the device history data associated with the device identifier. The apparatus also includes a processor to receive a de-enrollment command associated with the device via the network interface. The de-enrollment command causes the processor to modify the device identifier stored in the database.

Databases to store device history data

In some examples, a system receives data from peripheral devices connected to respective point-of-sale (POS) base terminals, the data captured using agents executing in the POS base terminals during periods of reduced activity of the POS base terminals. Based on processing the received data, the system determines linkage of peripheral devices to the POS base terminals, and determines, for a first POS base terminal, swapping of a first peripheral device with a second peripheral device. The system generates an output indicating that the first peripheral device has been swapped with the second peripheral device, and identifies an issue associated with a POS base terminal or a peripheral device, and trigger a remediation action to address the issue.

Data of point-of-sale devices

The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Assessing spatial movement behavior

An example software patch difference device includes a processor to: receive current software version indicators of software installed at monitored devices; generate, using a cryptographic function, respective identifiers of the current software version indicators for the monitored devices; retrieve, from storage, respective previous identifiers of previous software version indicators of the software installed at the monitored devices, the respective previous identifiers generated using the cryptographic function; compare, for the current software version indicators, a respective identifier with a respective previous identifier; when a difference is determined therebetween for a given device: replace, at the storage, respective previous software version indicators for the given device with respective current software version indicators; and transmit, to an analytics device, a respective software change indicator of the given device, to trigger the analytics device to generate a report indicating statistics for respective software versions installed at the monitored devices.

Software patch difference devices

An example polling computing device includes a processor controlled to: determine an interval time to poll a monitored device in a queue of monitored devices based on a current processing time of a given monitored device being polled, and an average processing time for previously polled monitored devices; adjust a polling rate such that the interval time is between a minimum polling interval target and a maximum polling interval target; poll the monitored devices in the queue according to the polling rate; receive, in response to the poll, from the monitored devices in the queue, current software version indicators of software installed at the monitored devices; and transmit, to a software patch difference device, the current software version indicators, the software patch difference device to determine which of the monitored devices have been updated since a last poll based on the current software version indicators.

Polling computing devices

A system is provided including a memory in communication with a processor. The memory is to store a value of an attribute. The processor is to obtain from a source data structure the value. The processor is also to store in a further data structure the value in association with a descriptor identifying the source data structure. Moreover, the processor is to store in the further data structure an identifier in association with the value. The identifier is incrementable and unique to the value among values associated with the descriptor. In addition, the processor is to output the further data structure.

Data structure with identifiers

An example of an apparatus including a network interface to receive data about a data application via an external network is provided. The apparatus further includes a collection engine in communication with the network interface to receive the data. The apparatus also includes a metadata generation engine to generate application metadata associated with the data application. The apparatus includes a memory storage unit to maintain a database of metadata. The database includes the application metadata associated with the data application. The apparatus also includes a processor to apply the database of metadata to a lookup table.

Databases to store metadata

There is provided a method including determining whether a selection of database query identifiers is to be made in a fixed manner or a dynamic manner. The method also includes generating a collection including the selection from a list including database query identifiers related to a database. When the selection is to be made in the fixed manner, the selection of the database query identifiers includes a view name and/or database query logic. When the selection is to be made in the dynamic manner, the selection of the database query identifiers includes a corresponding view name. The method also includes generating a materialized view corresponding to a given database query identifier in the collection. Moreover, the method includes storing an identifier of the materialized view in association with the given database query identifier.

Materialized view generation

A system is provided including a memory in communication with a processor. The memory is to store a data source comprising source data. The processor is to store in a row of a table: a source identifier of the data source, a data category value, and an aggregation identifier of an aggregation operation. The processor is also to execute the aggregation operation against the source data based on the data category value to obtain a measure related to the source data. In addition, the processor is to store the measure in the row, and output the table.

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Spring, TX, United States of America

Gaurav Sujit Roy