Micro Focus Lcc

Entit Software LLC

Hewlett Packard Enterprise Development LP

Eli Revach

Fernando Vizer

Avihay Mor

Amos Nesher

Amitai Shlomo Shtossel

Elad Levi

Gabriel Dayan

Pavel Danichev

Luba Tsirulnik

Lioz Medina

Keren Gattegno

Michael Aaron

Shahar Tal

Mordechai Lehrer

A database having multiple data columns is disclosed. Whether metadata for the multiple data columns resides in multiple different metadata columns may be determined. If so, the metadata may be consolidated into a single consolidated metadata column.

Consolidated metadata in databases

In examples, a system adaptively orders a set of regular expressions based on frequencies that respective regular expressions of the set of regular expressions match a set of messages, the adaptive ordering to produce an adaptively ordered set of regular expressions. The system determines, for a first message of the set of messages, whether a plurality of regular expressions of the adaptively ordered set of regular expressions match the first message. The system constructs a bi-directional graph representing the plurality of regular expressions that match the first message, and classifies a second message based on the adaptively ordered set of regular expressions and the bi-directional graph.

Ordering regular expressions

A probabilistic data structure may be queried to test whether text of a received log message is present in the probabilistic data structure. The representative log messages may be included in respective existing clusters are included in the probabilistic data structure. Based on whether the text is present, whether to create a new cluster for the received log message may be determined.

Clustering log messages using probabilistic data structures

Implementations disclosed herein relate to a device for applying a degree of transparency. The device may comprise a display engine to display a foreground and background area, wherein the background area is associated with a first application and the foreground area is associated with a second application. The device may further comprise a transparency engine to apply a degree of transparency to the foreground area in an overlap area between the foreground area and the background area. The device may further comprise a trigger engine to determine whether a trigger event associated with the first application has occurred, wherein the trigger event is based on a metric value received by the first application, and in response to determining that the triggering event associated with the first application has occurred, cause the display engine to display the first application in front of the second application.

Applying a degree of transparency

In examples, an apparatus comprises: a memory, and a processor coupled to the memory. The processor to: adaptively order an ordered set of regular expressions based on training messages to produce a set of adaptively ordered regular expressions having an adaptive order, determine a first of the adaptively ordered regular expressions that matches an additional message, and determine whether a second of the adaptively ordered regular expressions matches the additional message. Responsive to determining that the second of the other of the adaptively ordered regular expressions matches the additional message, the processor to: classify the additional message with the first regular expression if the first regular expression has a higher priority in the adaptive order; and classify the additional message with the second regular expression if the second regular expression has a higher priority in the adaptive order.

Examples relate to purging storage partitions of a database. The examples disclosed herein identify a first partition of a database to be purged and identify a data entry in the first storage partition, where the data entry is to be copied. Examples herein copy an updated version of the data entry to a future storage partition of the database and purge the first storage partition. A dummy data entry is created in a second storage partition of the database, where the dummy data entry identifies the future storage partition.

Purging storage partitions of databases

According to an example, different types of data stored at a database may be identified. The identified data may be scored, where different types of data are scored according to different schemes. The scored data that is above a threshold may be selectively retained. The different schemes may relate to measuring a relevancy of the identified data for predicting behavior of a system.

Retain data above threshold

A computer-implemented method includes determining a plurality of bulk sets for querying database records. The method also includes assigning a plurality of keysets to the plurality of bulk sets, with each keyset comprising a unique set of dimension attribute values from the database records. The method also includes calculating a predicted load score of each bulk set. The method also includes performing a transfer of a keyset from a first bulk set to a second bulk set when the transfer reduces a difference between predicted load scores of the first bulk set and the second bulk set. The method also includes, after the transfer, executing bulk queries using the plurality of bulk sets.

Bulk sets for executing database queries

According to an example, a batch processing system is to determine a cluster of related metrics. A stability of a baseline for a metric in the cluster is determined. A frequency of execution of batch jobs for calculating baselines for metrics is determined based on the stability.

Batch job frequency control

Examples relate to calculating normalize metrics. The examples disclosed herein calculate respective normalized first metric values for each of a plurality of first metric values that are on a time scale and respective normalized second metric values for each of the plurality of raw second metric values that are on the time scale, where the plurality of first metric values are associated with a first metric, and the plurality of second metric values are associated with a second metric. An extremum of the normalized first metric value and the normalized second metric value at each time of the time scale is averaged to calculate a plurality of extremum baseline values. Examples herein calculate a plurality of sleeve values of the plurality of extremum baseline values based on a standard deviation of the plurality of extremum baseline values.

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Yehud, Israel

Eli Revach