Dell Products Inc.

Emc IP Holding Company LLC

Tousif Mohammed

Hung The Dinh

Sabu K Syed

Abhijit Mishra

Jatin Kamlesh Thakkar

John Kenneth Maxi

Vinod Kumar

Satish Ranjan Das

Panguluru Vijaya Sekhar

Manikandan Pammal Rathinavelu

Krishna Mohan Akkinapalli

Reddeppa Kollu

Nagireddy Bonthu

Shubham Gupta

Vijaya Panguluru Sekhar

Vinay Sathyanarayana

Craig J Van Der Bogart

Pallavi Jaini

Falaah Arif Khan

Madhusudhana Reddy Chilipi

Vellore Mohammed Imran

Ramu Kannappan

Chakradhar Kommana

Karan Kapoor

Gnanesh Gowda

Pravash Ranjan Panda

Saravanan Kannan

Naveen Silvester Muttikal Thomas

Akanksha Bansal

Sharath Kumar Mudigere Yathiraj

Amirthraj Ramakrishnan

Venkat Allaka

Jonathan Andrew Hernandez

Jyothi K R

Peter John Sarkis

Anu Bala Thakur

Abhishek Joshi

Puttaraju Bommanna Chikkanna

Prince Mathew

In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Use machine learning to verify and modify a specification of an integration interface for a software module

Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Identifying and remediating system anomalies through machine learning algorithms

As an example, a server hosting a search engine may receive a search query and determine a searched time interval, a searched object, and a searched event. The server may select, based on the searched time interval, a portion of an object-event bipartite graph that was created using information gathered from social media sites. The server may compare attributes of individual events in the portion with attributes of the searched event to identify a set of relevant events. The server may determine objects associated with the relevant events and compare attributes of individual objects with the attributes of the searched object to identify a set of relevant objects. The search engine may provide search results that include the set of relevant objects ordered according to their similarity to the searched object.

Event-based search engine

A method includes retrieving vendor specific data from one or more message oriented middleware servers of a message oriented middleware infrastructure, and inputting the vendor specific data from the one or more message oriented middleware servers into a back-end database. The vendor specific data is converted into commonly formatted data, and the commonly formatted data is inputted into a front-end database. The method also includes retrieving the commonly formatted data from the front-end database, and displaying the commonly formatted data on a user interface providing a visualization of a topology of the message oriented middleware infrastructure.

Message oriented middleware topology explorer

A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.

Time series forecasting classification

An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to provide a plurality of applications with centralized access to a plurality of message oriented middleware (MOM) servers via a connectivity layer, to establish a connection between a given one of the plurality of applications and a given one of the plurality of MOM servers via the connectivity layer, and to exchange data between the given one of the plurality of applications and the given one of the plurality of MOM servers via the connectivity layer.

Messaging abstraction layer for integration with message oriented middleware platforms

Growing community of inventors

Bengaluru, India

Tousif Mohammed