Sap Se

Atlassian Corporation Pty Ltd.

Microsoft Technology Licensing, LLC

Atlassian Us, Inc.

Atlassian Inc.

Vipul Gupta

Nipun Aggarwal

Manish Sharma

Rick Banerjee

Aparajita

Arun K Dixit

Akshar Prasad

Supriya A Supugade

Darpan Dinker

Shashank Prasad Rao

Balaji Raghunathan

Abhishek Gupta

Sandipan Biswas

Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adjustment model, and a similarity-based alert priority score adjustment model.

Machine-learning-based techniques for predictive monitoring of a software application framework

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention describe techniques for enabling access to anatomized data associated with a database table without using computationally expensive data anatomization techniques that use data masking and/or data manipulation and by using limited access pointers that are generated using randomized associations between database rows of a database table.

Enabling limited database access using randomized limited access pointers

Systems and methods for determining the priority of a set of reports available to a user. The method comprising determining, by a reporting application, a set of reports available to a user, each report including one or more metrics. The method further comprising obtaining metric data associated with each of the set of reports available to the user, accessing one or more priority calculation parameters for each report in the set of reports, calculating a priority value of each report of the set of reports based on the metric data and the priority calculation parameters for each report, and determining one or more high priority reports based on the priority value of each report of the set of reports.

Reporting systems and methods

Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using alert signatures for the software application that are generated by using at least one of first-order alert classification machine learning models, temporal-sequential alert classification machine learning models, categorical-sequential alert classification machine learning models, alert classification machine learning models, and second-order alert classification machine learning models.

Embodiments of the present disclosure provide improved identification and handling of root causes for data event(s). Some embodiments improve the accuracy of determinations of a root cause or likely order of root causes of a data event affecting any number of system(s), and cause transmission of data associated with such root cause(s) for use in triaging such data event(s) and/or facilitating efficient servicing to resolve the data event. Some embodiments utilize modified centrality algorithm(s) to efficiently and accurately identify a likely root cause of a data event in a computing environment. Some embodiments generate and/or output notifications that indicate the particular computing system(s) identified as a root cause of a data event, and/or the particular computing system(s) identified not as a root cause but affected by a data event of the root cause computing system.

Apparatuses, computer-implemented methods, and computer program products for improved data event root cause identification and remediation

The disclosure herein describes a system for generating customizable summaries of multi-message conversations in email threads and other multi-message and multi-participant messaging applications. A summary icon is displayed if the number of messages in a thread exceeds a threshold number. Message data is cleaned and analyzed by ML models to generate feature vectors representing the messages content. Multiple loglinear regression models compute similarity between message sentences. Clustering algorithm(s) to generate a multi-message conversation thread summary based on the values. If the user selects the summary icon, the multi-message conversation thread summary is displayed. The summary includes summary sentences and/or annotations describing content of the messages in the thread, participants contributing to the messages in the thread, links back to the original messages in the thread and/or a count of the number of messages contributed by each participant.

Multi-message conversation summaries and annotations

A method of and system of efficient storage of data entries containing textual data is disclosed. The method may include accessing a plurality of data entries in a dataset, arranging the plurality of data entries in the dataset in a lexical order, placing a predetermined number of the plurality of data entries in each of a plurality of subblocks, performing data compression on the plurality of data entries in each of the plurality of subblocks to reduce redundancy in the plurality of data entries and create compressed data entries, placing one or more subblocks in each of a plurality of page blocks, and storing each of the plurality of page blocks in a storage device to provide efficient searching and improved functionality for the dataset.

Efficient storage and retrieval of textual data

Techniques are provided for identifying and encrypting fields of an application object at an application layer in a multi-tenant cloud architecture, using an object metadata structure of the application object. Accordingly, transparent, per-tenant encryption capabilities are provided, while enabling transfer of encrypted object data between the application layer and a storage layer.

Encryption of application data using field-level metadata

In one aspect, systems and techniques for modifying a storage structure with newly encryptable fields is described. A delta encryption system loads a current database schema and an object model for a new database schema to a memory. The current database schema includes meta information identifying multiple fields and field information for a current application build. The object model includes meta information identifying multiple fields and field information for a new application build. The fields identified by the object model meta information include one or more encryptable fields. The delta encryption system calculates a field size information for the encryptable fields and compares the meta information from the current database schema with the meta information from the object model. In response to the comparison, the delta encryption system identifies which of the encryptable fields are newly encryptable fields and modifies a database based on the calculated field size information.

Automated reshaping and restructuring of storage structures

Methods and apparatus, including computer program products, are provided for securing data in a multi-tenant cloud-based system. In some implementations, there is provided a method. The method may include requesting access to at least one encrypted data element; obtaining, in response to the requesting, a long bit stream assigned to a client associated with the requested access; generating a key to decrypt the at least one data element, the key generated by selecting, based on a permutation, portions of the long bit stream; and decrypting, based on the generated key, the at least one data element. Related systems, methods, and articles of manufacture are also disclosed.

Protecting data in a multi-tenant cloud-based system

The present disclosure generally relates to a software application runtime having dynamic evaluation functions and parameters. A dynamic evaluation engine of an application's runtime accepts evaluation requests from methods of a software application. A request can be associated with an identifier for a method of the software application. The method identifier can be associated with one or more dynamic evaluation functions having one or more dynamic evaluation parameters. When the dynamic evaluation engine receives a request from an application method, the dynamic evaluation engine can determine the one or more current dynamic evaluation functions or parameters to use with the request. The dynamic evaluation engine can return an evaluation result to the method. The dynamic evaluation engine can be in communication with a repository, such as a central repository, providing a unique mechanism to look up dynamic evaluation functions and parameters, which can be imported into the application's runtime.

Software application runtime having dynamic evaluation functions and parameters

Cloud-based data is securely retrieved by obfuscating access patterns. A cloud storage system receives a request for data from a remote client that specifies a key. Thereafter, the cloud storage system iterates through an index to identify all locations corresponding to the specified key. Such index is generated by applying a series of j hash functions to each key resulting in a j different tables forming part of the index. Using the index, the cloud storage system returns data from the identified locations to the client. As each write operation works by using non-deterministic encryption, the write operation changes the records stored in this data structure, and when the record is not changed, the algorithm simply rewrites the data which is stored in the data structure by rewriting the same value back again. However since, a nondeterministic encryption is utilized, it makes it indistinguishable as to when new data was written and when existing data is rewritten.

Secure retrieval of cloud-based sensitive data by obfuscating data access patterns

When storing encrypted data within a database, a key identifier may be appended to the encrypted data as a prefix. Then, when decrypting the encrypted data, the key identifier may then be used to identify an encryption key used to encrypt the data, even when multiple encryption keys have been used in encrypting and storing the encrypted data as a whole.

Appended key ID for key identification during data encryption

Systems and methods include determination of a first data analysis period, determination of a first plurality of sets of parameter values, each of the first plurality of sets of parameter values being associated with a respective time period within the first data analysis period and describing a statistical distribution of data points associated with the respective time period, determination of a statistical distribution associated with the first data analysis period based on the first plurality of sets of parameter values, determination of a system condition based on the statistical distribution associated with the first data analysis period, and initiation of an action based on the determined system condition.

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Bengaluru, India

Vipul Gupta