Enterprise organizations have large amounts of data which is utilized by multiple Machine Learning (ML) models over various software frameworks. These models provide trends and insights from the data that can help enterprises define business rules around their processes. However, if certain aspects of this data are removed from the datasets, it could influence the business rules and policies in place. When a user requests data to be removed, the model retraining may be required called Machine Unlearning (MU). Recent research works in the area of MU include different methods of retraining the machine learning models. It turns out that there is lack of work in removing certain aspects of data, and quantifying its impact on the models. This paper aspires to provide a novel methodology IDMU (Impact Driven Machine Unlearning) that performs quantification of the impact of data removal requests while performing MU. Our method provides recommendations for data removal requests, factoring in underlying features of data. The results from the industrial application and evaluation of our method on a financial services dataset are encouraging. The overall IDMU had a mean MAPE of 10.25% over a set of 120 data removal requests. It also saved ~1900 hours of model retraining time by factoring in urgency and impact of data removal requests over a period of three years.

Enterprise organizations have vast datasets that need comprehensive analysis on a frequent basis, in order to manage data and take business decisions based on it. However, we observe that there can be a lack of industry standards for definitions of key terms. Additionally, there is a lack of governance for maintaining business processes. This typically leads to disconnected siloed datasets generated from disintegrated systems. To address these challenges, we developed a novel, integrated methodology DaME (Data Mapping Engine) that performs data mapping using ensemble of NLP techniques.The results from the industrial application and evaluation of DaME on a financial services dataset are encouraging that it can help reduce manual effort by automating data mapping and reusing the learning. The accuracy from our dataset in the application is much higher at 69% compared to the existing state-of-the-art with an accuracy of 34%. It has also helped improve the productivity of the industry practitioners, by saving them 14,000 hours of time spent manually mapping vast data stores over a period of ten months.

The transactions of goods and services between enterprise service providers are often driven by contracts and purchase orders. Every month thousands of invoices are billed to customers who settle them based on the usage of services. Considering the vast number of purchase orders that are signed, it requires considerable manual effort by the service provider to process and manage them. Moreover, the invoice’s billed data may not be maintained in the same cloud system as the purchase orders. This leads to complexity with data mapping between the two data sets. Sometimes the invoices may get into a dispute due to over exhaustion of allocated funds or may be billed to an expired purchase order. Hence managing the billing service is a huge undertaking along with increased cost.To address these challenges, we developed an order manage- ment system that transforms the monitoring of purchase orders to increase renewals as well as decrease disputes. The system includes an automated purchase order-invoice data mapping model along with a risk analytics model that evaluates the orders against the invoices billed. The output is the set of actionable and non actionable insights based on customer portfolio, risk level as well as market trends in usage of services. We illustrate our method with some promising results on data of one of the world’s largest IT service providers.

Enterprises are rapidly working on strategies to migrate their applications to cloud. Multi-cloud allows mixing and matching multiple cloud vendors when migrating thousands of applications based on their set of requirements and various different types of constraints. To utilize the advantages of different clouds, achieve maximum flexibility and avoid concentration risk, enterprises spread their applications across cloud providers. But this activity is not trivial as it would require honoring the constraints which the customer has, and at the same time generating the most optimal configuration of cloud resources.There are a few challenges associated with this. Firstly, the applications to be migrated need to be documented well, in order to migrate them successfully. Some applications may be very old (legacy) and need an architect overhaul which means the cloud feasibility needs to be checked. Also, enterprises would like to have an optimal list of cloud vendors that satisfy their need. To overcome these challenges, we propose a smart system for determining multi-cloud pathway for applications. The system identifies cloud feasible applications, understands their requirements and recommends optimal set of cloud vendors honoring their constraints. This is enabled through Reinforcement Learning with Human-in-the-Loop. We show our results with a use case from real world scenario.

Microservices Architecture (MSA) has become a de-facto standard for designing cloud-native enterprise applications due to its efficient infrastructure setup, service availability, elastic scalability, dependability, and better security. Existing (monolithic) systems must be decomposed into microservices to harness these characteristics. Since manual decomposition of large scale applications can be laborious and error-prone, AI-based systems to decompose applications are gaining popularity. However, the usefulness of these approaches is limited by the expressiveness of the program representation and their inability to model the application’s dependency on critical external resources such as databases. Consequently, partitioning recommendations offered by current tools result in architectures that result in (a) distributed monoliths, and/or (b) force the use of (often criticized) distributed transactions. This work attempts to overcome these challenges by introducing CARGO (short for Context-sensitive lAbel pRopaGatiOn)—a novel un-/semi-supervised partition refinement technique that uses a context- and flow-sensitive system dependency graph of the monolithic application to refine and thereby enrich the partitioning quality of the current state-of-the-art algorithms. CARGO was used to augment four state-of-the-art microservice partitioning techniques (comprised of 1 industrial tool and 3 open-source projects). These were applied on five Java EE applications (comprised of 1 proprietary and 4 open source projects). Experiments show that CARGO is capable of improving the partition quality of all four partitioning techniques. Further, CARGO substantially reduces distributed transactions, and a real-world performance evaluation of a benchmark application (deployed under varying loads) shows that CARGO also lowers the overall the latency of the deployed microservice application by 11% and increases throughput by 120% on average.

When Information Technology (IT) service providers cater services to their customers, there’s a common practice to document the intent of buyer to purchase them through a Purchase Order (PO). The details of a PO document are complex in nature, as they include hierarchical structure of sub-services, price points over the duration of PO, base setup cost, billing frequency, renewal terms etc. In large enterprises with high volume PO’s, the traditional approach of managing PO’s with their invoices involved a great deal of inefficiency and labor-intensive manual work due to lack of automation and disconnected processing. As a result, they often suffered tedious manual monitoring and failed PO renewals, resulting in delays and added costs.In this paper, we propose a joint time-series learning framework that tackles the high dimensionality PO data. It identifies the metrics to monitor, while using time-series prediction coupled with tone analysis to reach out to customers, in order to maximise PO renewals. We demonstrate utility of our approach by implementing our method on a dataset from a global IT service provider that indicates encouraging results.

In the face of emergent external factors (e.g., supply chain disruptions or public health crises like COVID-19), businesses must adapt their business model quickly in order to ensure service continuity. However, providing recommendations regarding changes should be made to the business model is a challenging problem. First, it requires details of interactions between different components of the business (e.g., service offerings, inventory, staffing, demand) to understand what possible courses of action will have the most business impact. Second, automated models may provide recommendations on changes required in the business operations. However, with lack of human insight, it will be hard to verify the feasibility of these recommendations. Third, a generic model may not be able to provide good recommendations for diverse set of business models. Fourth, the model may not have enough features or training data to provide good recommendations.In this paper, we propose a novel approach to provide actionable items that can be recommended to business users given their business features and recommendations given to businesses in similar domain. Here we first use clustering to find the business domain and similar feature set of the domain. Then, we build a machine-learning model with explainable insights to provide recommendations on different business actions that can be taken to ensure business operations in the face of emergent external factors. Next we augment our approach with human-in-the-loop to improve its performance. Finally, we federate the machine-learning model in a similar domain to add more explainable and trusted insights and recommendations by other businesses. We describe our method, illustrate its utility with results from our implementation, and discuss areas for future work.

Many organizations, driven by the need for greater productivity and lower costs, are moving their portfolio of applications to the cloud. However, there are many challenges in migrating an application to the cloud. That is, there are different aspects of an application that affect its cloud-migration feasibility. That is why assessing the feasibility of migrating an application to the cloud is not an easy task. Also, it is non-trivial to map which cloud solution - whether infrastructure as a service, platform as a service, or software as a service, should be used for hosting the application, if it is shown to be cloud-migration feasible. In this paper, we present a novel cloud-migration feasibility advisor that analyzes the meta data received about an application and evaluates whether it is feasible to move it to the cloud or not. In addition, the advisor uses dependency graphs and the aforementioned meta data to classify which cloud solution would suit best for the application, if it is deemed migration feasible.

Although widely used in various areas, the Deep Neural Network suffers from the lack of interpretability. Existing works usually focus on one data instance and the found explanations are thus limited. We argue that a good understanding of a model should contain both systematic explanations of model behavior and effective detection of its vulnerability. Particularly we propose to use backward deduction to achieve these two goals. Given a constraint on the model output, the deduction backtracks the architecture to find corresponding data ranges in the input. In each layer, depending on the type, specific rules and/or algorithms are developed. The resulted ranges in the end can be interpreted by sampling exemplary data from them. In experiment we show that with different strategies in selecting the data ranges, the sampled fake data can either explain the model or reflect its vulnerability.