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公开(公告)号：US12282386B2
公开(公告)日：2025-04-22
申请号：US18519822
申请日：2023-11-27
Applicant: BMC Software, Inc.
Inventor： Sai Eswar Garapati , Erhan Giral
IPC: G06F11/07 , G06F16/901 , G06N3/08 , G06N5/022 , H04L41/0631 , H04L41/12 , H04L41/14
Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.
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公开(公告)号：US20250077331A1
公开(公告)日：2025-03-06
申请号：US18241095
申请日：2023-08-31
Applicant: BMC Software, Inc.
Inventor： Vikas Prasad , Ajoy Kumar
IPC: G06F11/07
Abstract: A plurality of textual log records characterizing operations occurring within a technology landscape may be received and converted into numerical log record vectors. For a current log record vector and a preceding set of log record vectors of the numerical log record vectors, a similarity series may be computed that includes a similarity measure for each of a set of log record vector pairs, with each log record vector pair including the current log record vector and one of the preceding set of log record vectors. A similarity distribution of the similarity series may be generated, and an anomaly in the operations occurring within the technology landscape may be detected, based on the similarity distribution.
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公开(公告)号：US12130699B2
公开(公告)日：2024-10-29
申请号：US18310288
申请日：2023-05-01
Applicant: BMC Software, Inc.
Inventor： Nigel Slinger , Wenjie Zhu
CPC classification number: G06F11/079 , G06F11/0772 , G06F11/2263 , G06F11/3006 , G06F11/3075 , G06F11/327 , G06F11/3409 , G06N20/00
Abstract: An event graph schema for a technology landscape may be determined, where the technology landscape is characterized using scores assigned to performance metrics. The event graph schema may include a plurality of nodes corresponding to the performance metrics and the scores, and directional edges connecting node pairs of the plurality of nodes, with each directional edge having a score-dependent validity criteria defined by scores of a corresponding node pair. Anomalous scores associated with an event within the technology landscape may be used to find anomalous nodes. Valid edges connecting two of the anomalous nodes and satisfying the score-dependent validity criteria thereof may be used to determine at least one path that includes the valid edges and connected anomalous nodes. In this way, it is possible to traverse the at least one path to identify at least one of the connected anomalous nodes as a root cause node of the event.
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公开(公告)号：US12056032B2
公开(公告)日：2024-08-06
申请号：US17657617
申请日：2022-03-31
Applicant: BMC Software, Inc.
Inventor： Jason Ronald Torola , Anthony Louis Lubrano
CPC classification number: G06F11/3075 , G06F9/544 , G06F11/0772 , G06F11/3034 , G06F2209/543
Abstract: Described techniques provide convenient, reliable, straightforward techniques for enabling multi-path application outputs. A single application may be configured to output two or more data sets to two or more output destinations within a mainframe environment, without requiring copying or forwarding by an intermediate application utility.
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公开(公告)号：US12056021B2
公开(公告)日：2024-08-06
申请号：US17450686
申请日：2021-10-12
Applicant: BMC Software, Inc.
Inventor： Rupak Ranjan Devroy , Carroll William Andrews, Jr.
CPC classification number: G06F11/1469 , G06F11/1451 , G06F16/113 , G06F16/2358
Abstract: Described systems and techniques enable the capture of an archive image copy of at least one database at a first time, as well as the capture of database metadata that includes a runtime environment of the at least one database. The archive image copy and the database metadata may be stored. A request to restore the at least one database may be received at a second time. In response to the request, the archive image copy and the database metadata may be determined. The at least one database may be restored, including re-establishing the runtime environment using the database metadata.
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Patent Agency Ranking
公开(公告)号：US11972129B2
公开(公告)日：2024-04-30
申请号：US17934167
申请日：2022-09-21
Applicant: BMC Software, Inc.
Inventor： Offer Baruch , Dori Polotsky , Tomer Zelberzvig , Adi Shtatfeld , Roded Bahat , Shy Ifrah , Gil Peleg
CPC classification number: G06F3/064 , G06F3/067 , G06F3/0682 , G06F9/30174 , G11B5/5508 , G06F3/0607
Abstract: Methods, system and computer program product, the method comprising: from high level language code (HLLC), receiving a request for reading a data set from a tape onto an object storage connected over TCP/IP to a mainframe; from the HLLC, allocating a data set on a tape comprising information to be imported, the allocation being in a format of the stored data set record and associated with a JFCB, the tape is mounted in SL mode; updating the JFCB to BLP mode; reading from the tape VOL1 data, and for each stored file initiating by the HLLC: reading HDR1/2, content block-by-block; EOF1/2 of the file; organizing the VOL1, HDR1, HDR2, content, EOF1 and EOF2 in the object storage; and closing the tape, wherein said reading is performed without setting a JES of the mainframe to BLP mode, and said reading is performed without unmounting the tape after each file.
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公开(公告)号：US20240112071A1
公开(公告)日：2024-04-04
申请号：US17937254
申请日：2022-09-30
Applicant: BMC Software, Inc.
Inventor： Nigel Slinger , Vincent Huynh Nguyen , Roxanne Kallman , Wenjie Zhu
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: Described systems and techniques provide fast, efficient, and cost-effective techniques for detecting anomalous behaviors of monitored objects. Multiple hashing algorithms, each providing multiple hash bins, may be used to generate a unique hash signature for each of the monitored objects. Metric values characterizing the behavior of the monitored objects may be aggregated within individual ones of the multiple hash bins of each of the multiple hashing algorithms. Then, one or more machine learning models may be trained using the unique hash signatures and their included, aggregated metric values. During subsequent scoring using the trained machine learning model(s), each of the aggregated metric values of each of the hash bins may be scored, and a single or small subset of anomalous objects may be identified.
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公开(公告)号：US20240111736A1
公开(公告)日：2024-04-04
申请号：US17937261
申请日：2022-09-30
Applicant: BMC Software, Inc.
Inventor： Eyal Dahari , Michal Barak
IPC: G06F16/215 , G06F16/21 , G06F40/30
CPC classification number: G06F16/215 , G06F16/211 , G06F40/30
Abstract: Described systems and techniques enable fast and accurate semantic classification of data. Such semantic classification may be performed efficiently, e.g., in a manner that uses a minimal required set of resources to perform a given semantic classification. Moreover, described techniques dynamically improve over time, so that even when more resource-intensive operations are initially required to semantically classify data in a first iteration, similar data will be recognized more quickly and using fewer resources in later iterations.
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公开(公告)号：US20230418657A1
公开(公告)日：2023-12-28
申请号：US17809486
申请日：2022-06-28
Applicant: BMC Software, Inc.
Inventor： Nikolai OZEROV
CPC classification number: G06F9/4818 , G06N5/02
Abstract: Described techniques provide optimized job management with accurate runtime predictions for individual job instances. By classifying jobs with respect to combinations of multiple prediction algorithms and multiple job properties, including classifying different job instances of a single job, the described techniques enable use of fast, simple prediction techniques while still providing accurate predictions.
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公开(公告)号：US20230401141A1
公开(公告)日：2023-12-14
申请号：US18332336
申请日：2023-06-09
Applicant: BMC Software, Inc.
Inventor： Ajoy Kumar , Mantinder Jit Singh , Smijith Pichappan
CPC classification number: G06F11/3608 , G06F11/302 , G06N20/00 , G06N5/04 , G06F11/3419 , G06F11/3447
Abstract: Described systems and techniques enable prediction of a state of an application at a future time, with high levels of accuracy and specificity. Accordingly, operators may be provided with sufficient warning to avert poor user experiences. Unsupervised machine learning techniques may be used to characterize current states of applications and underlying components in a standardized manner. The resulting data effectively provides labelled training data that may then be used by supervised machine learning algorithms to build state prediction models. Resulting state prediction models may then be deployed and used to predict an application state of an application at a specified future time.
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