A cloud services monitoring system (CSMS) helps a consumer of cloud computing resources determine the state of the cloud computing resources available to the consumer The CSMS is particularly effective for cloud models where the consumer of the cloud computing resources is not the same as the provider of the cloud resource The CSMS 102 also provides mechanisms for adjusting operations actions to preserve service guarantees for the consumer In that regard, the CSMS dynamically adjusts operations actions in response to the state of the cloud, rather than executing fixed predetermined operations actions that are not tailored to the monitored conditions

Cloud service monitoring system

Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.

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The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Resources in a resource-on-demand system are reserved to support user desktop utility demand. A demand calendar specifies capacities of resources in the resource-on-demand system reserved to support user desktop utility demand for future time periods. A requested capacity is compared to capacities from the demand calendar for a requested future period of time to determine whether resources for the requested capacity are to be reserved.

Reserving resources in a resource-on-demand system for user desktop utility demand

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Total structural risk model

Objects are placed on hosts using hard constraints and soft constraints. The objects to be placed on the host may be many different types of objects. For example, the objects to place may include tenants in a database, virtual machines on a physical machine, databases on a virtual machine, tenants in directory forests, tenants in farms, and the like. When determining a host for an object, a pool of hosts is filtered through a series of hard constraints. The remaining pool of hosts is further filtered through soft constraints to help in selection of a host. A host is then chosen from the remaining hosts.

Placing objects on hosts using hard and soft constraints

A system for dynamically allocating resources in a computing grid is provided The system comprises a resource management component, a service level agreement (SLA) management component and a job scheduling component The resource management component is configured to determine availability of a set of resources in a computing grid, for executing job requests submitted by users of the computing grid The SLA management component is configured to manage service level agreements (SLAs) corresponding to the users and perform a negotiation of resources for the users submitting the job requests, in real-time, based on a resource steerability attribute associated with the job requests The job-scheduling component is configured to schedule the job requests for the users, based on the resource steerability attribute associated with the job requests The job requests comprise at least one of a job having batch characteristics and a job having interactive characteristics The computing grid is configured to process the job based on the resource steerability attribute associated with the job request

System and method for dynamic allocation of resources in a computing grid

A method for selecting a target list of customers for making cross sell offers to, for a financial product is provided. The method includes obtaining customer-level information related to one or more members from a historical database. The method then includes building one or more response models and one or more profit models for one or more subsets of members, using the customer-level information. Then, the method includes generating one or more response scores and one or more profit scores for one or more members from a target population, using the one or more response models and the one or more profit models. Finally, the method includes determining a target list of customers for making cross-sell offers to, based on the one or more response scores and the one or more profit scores using an optimization methodology.

System and method for optimizing cross-sell decisions for financial products

A system includes plural, different sensors configured to be operably connected to a first vehicle The sensors obtain status data indicative of a state of a cooling system of the first vehicle The system includes a digital twin system having processors configured to modify the status data having sampling rates or resolutions that differ such that the status data that is modified has a common sampling rate resolution as that of other data The processors create a digital twin matrix of the status data The digital twin matrix is indicative of the state of the cooling system The processors also determine a health score of the cooling system or simulate operation of the cooling system based on the digital twin matrix The digital twin system is configured to change or control actual operation of the cooling system based on the health score or the operation that is simulated

Digital twin system for a cooling system

We consider the problem of analyzing influences in financial networks by studying correlations in stock price movements of companies in the S&P 500 index and measures of influence that can be attributed to each company. We demonstrate that under a novel and natural measure of influence involving cross-correlations of stock market returns and market capitalization, the resulting network of financial influences is Scale Free. This is further corroborated by the existence of an intuitive set of highly influential hub nodes in the network. Finally, it is also shown that companies that have been deleted from the S&P 500 index had low values of influence.

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Financial influences and scale-free networks

We consider the issue of model selection for some prediction problems in consumer finance. In particular, we look at performance metrics in the context of classification problems. Example areas considered include response modeling, profitability modeling and default prediction in the framework of a customer relationship management (CRM) system. We propose some guidelines for choosing the appropriate performance measure for the predictive model based on the decision framework it is part of.

Babu Narayanan

Papers

System and method for dynamic allocation of resources in a computing grid

System and method for optimizing cross-sell decisions for financial products

Digital twin system for a cooling system

Financial influences and scale-free networks

Metrics for model selection in consumer finance problems