International Business Machines Corporation

Tomasz Andrzej Stopa

Piotr Kania

Michal S Paluch

Pawel K Gocek

Piotr P Godowski

Bartlomiej Tomasz Malecki

Marcin Labenski

Grzegorz Sawina

Szymon M Brandys

Joanna Barbara Koza

Antonio Perrone

Bernardo Pastorelli

Paolo Salerno

Rossella De Gaetano

Artur Zezula

Brian Joseph Schlosser

Lukasz Tomasz Jeda

Jakub Kania

Lukasz Jozef Matyasik

Tomasz P Zeller

An initial software scan is performed to detect a set of software deployed on an endpoint. An ongoing scan of the endpoint is performed to map a set of file directories associated with each software within the set of software. Via the ongoing scan, a usage frequency for each mapped file directory is determined. A heat map is generated for each mapped file directory, according to usage frequency, using a randomized meta-heuristic. A request is received for a software discovery scan result. The software discovery scan, based on the heat map, is performed in response to the request. The result of the software discovery scan is provided to a user.

Meta-heuristics based on heat maps for software discovery

Provided are a computer program product, network computing system, and method for automated system integration. A first system gathers a request list of requested computational resources to transmit to the second system, receives a list of accessible computational resources available through the second system, and determines matching computational resources in the request list that match computational resources in the list of accessible computational resources. The first system sends information on the matching computational resources to the second system. The second system gathers the matching computational resources received from the first system to make accessible to the first system at a resource location accessible over a network and sends access information for the matching computational resources gathered by the second system. The first system uses the access information to access the matching computational resources at the resource location over the network to deploy the matching computational resources at the first system.

Automated system integration

A method retrieves and displays an alternative webpage in response to an initially-requested webpage no longer being available. A browser on a client device sends a request to a web server for an original webpage, which is associated with an original Universal Resource Locator (URL). The browser receives an error response, from the web server, which indicates that the original webpage no longer exists. A search engine on the client device searches for multiple alternative Universal Resource Locators (URLs), which are based on variations from the original URL. An alternative webpage that is associated with one of the multiple alternative URLs is then displayed on the client device.

Link corrections by cognitive analysis of web resources

Identification of unmatched registry entries may be provided by scanning a file system, discovering software, collecting first attribute values of the discovered software, and receiving a plurality of filtering rules including a method and an attribute. The attribute may comprise a software-specific condition. The method may further comprise collecting native registry entries comprising second attribute values indicated by said attributes of at least one of said rule, and comparing said first attribute values of said discovered software with related ones of said second attribute values of said collected native registry entries. Then, the native registry entries may be grouped into two groups. The first group represents matched registry entries and the second group represents unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery. Finally, the filtering rules may be applied against said collected registry entries based on said filtering method.

Enhanceable cross-domain rules engine for unmatched registry entries filtering

In an approach for providing adaptive software inventory scan frequencies and schedules, a processor receives information from an initial scan of a set of software inventory scans, wherein the information includes at least one of: running processes, file system entries, registry entries, and software catalog evaluations. A processor analyzes the information from the initial scan. A processor predicts an outcome for future software inventory scans based on the analysis of the information, wherein the prediction includes a scanning frequency and a scanning schedule.

Dynamic scheduling for a scan

A method for monitoring software usage for an application installed in a computer system. The computer system determines whether a change has occurred in a set of files stored in a set of directories for the application in a file system between inventory scans of the file system. The set of the files is associated with the application and the set of the files changes when the application is used. A usage metric for the application is adjusted by the computer system to indicate a use of the application when the change has occurred in the set of the files, increasing accuracy, by the computer system, in identifying application usage.

Application tracking system

One or more processors scan a first software container template for one or more identities of software present on a first software container associated with the first software container template. One or more processors generate a map of the one or more identities of software present on the first software container. The one or more identities of software present on the first software container are mapped with one or both of: an identifier of the first software container template and an identifier of the first software container associated with the first software container template.

Determining the identity of software in software containers

A method and a related system for identifying unmatched registry entries may be provided. The method may comprise scanning a file system and discovering software based on a file signature, collecting first attributes of the discovered software, collecting native registry entries, and comparing the first attributes against second attributes of the collected registry entries based on a filtering rule. Thereby, the registry entries may be grouped into two groups. One group may represent matched registry entries and the other group may represent unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery.

Identifying unmatched registry entries

A scan of a computing environment produces an inventory listing of software components installed in the computing environment. A user establishes a set of filter criteria which when applied to the inventory listing, produces a filtered inventory listing. The filtered inventory listing includes only software components that should collectively be classified in a common way. Once the user indicates that the filtered listing includes only the software components that should be collectively classified in a common way, and the user has performed (applied) a classification, the system creates a rule based on the modified filter criteria and the applied classification. During subsequent scans of the computing environment, the system applies the rule automatically, to identify and classify any newly discovered instances of software components that match the filtering conditions embodied in the rule.

Software classification based on custom rules

Identification of unmatched registry entries may be provided, by scanning a file system, discovering software, collecting first attribute values of the discovered software, receiving a plurality of filtering rules including a method and an attribute. The attribute may comprise a software-specific condition. The method may further comprise collecting native registry entries comprising second attribute values indicated by said attributes of at least one of said rule, and comparing said first attribute values of said discovered software with related ones of said second attribute values of said collected native registry entries. Then, the native registry entries may be grouped into two groups. The first group represents matched registry entries and the second group represents unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery. Finally, the filtering rules may be applied against said collected registry entries based on said filtering method.

Installation information of a software component is received. The software component is software. The installation information includes an installation state for the software component, an identifier for the software component, a vendor of the software component, and a calendar date of the installation. The installation information is stored in a pseudo-permanent database. The record of the installation information in the pseudo-permanent database will survive the uninstallation of the software component. An update to the installation information is received in response to the installation state of the software component changing. The update includes modified installation information and a calendar date of when the installation information changed. The updated installation information is stored in the pseudo-permanent database.

Storing computing component installation information

A first computing entity receives a respective set of software discovery rules from each of a plurality of other computing entities. Based on each of the sets of software discovery rules, a set of software discovery rules associated with the first computing entity is modified. The modified set of software discovery rules is communicated to each of the plurality of other computing entities.

Software catalog information management

A plurality of software instances deployed in a monitored environment are discovered by a software asset management tool operated by a software asset administrator who is responsible for monitoring software license compliance within the monitored environment. The software asset management tool then collects metrics associated with the plurality of software instances. The collected metrics are then provided to a first analytic service adapted to generate analytic information about the plurality of software instances. Using at least the collected metrics, the analytic service generates analytic information. The analytic information is then obtained from the analytic service. By reviewing the analytic information, the software asset administrator is able to obtain additional insight into the monitored environment that would not otherwise be available to him.

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Cracow, Poland

Tomasz Andrzej Stopa