Mitesh Vasa

Optimal control point selection for continuous business process compliance monitoring

Service delivery organizations fulfill their business obligations by defining and implementing business processes. Such processes also need to adhere to several regulations such as security, confidentiality and data integrity. These regulations are typically defined as policies, each of which contains a list of clauses. Organizations typically conduct periodic audits of executed process instances to determine how well they have adhered to the stated policies. Since such auditing takes place after the processes have been implemented, their efficacy as a tool for preventing non-compliances against policies is doubtful. In this paper, we analyze the problem of continuous compliance monitoring at run time. This involves selecting a subset of policy clauses at process tasks, called control points, at which compliance can be checked. Selecting control points involves a tradeoff. Selecting too few control points would raise the risk of increased non-compliance; however, selecting too many may not be cost-effective. In this paper, we represent this as an optimization problem, and discuss it from the viewpoint of two key optimization criteria. We also demonstrate our optimization techniques on a simplified version of a real-life example drawn from our industry experience.

Process Trace Identification from Unstructured Execution Logs

Many real world business processes are executed without explicit orchestration and hence do not generate structured execution logs. This is particularly true for the class of business processes which are executed in service delivery centers in emerging markets where rapid changes in processes and in the people executing the processes are common. In such environments, the process execution logs are usually natural language descriptions of actions performed and hence are noisy. Despite the lack of structured logs, it is crucial to know the trace of activities as they happen on the ground. Without such a visibility into the ground activities, regulatory compliance audit, process optimization, and best practices standardization are severely disabled. Process monitoring on top of unstructured execution logs has been a relatively unexplored research area. This paper proposes an approach for process trace identification from unstructured logs that applies state-of-the-art text mining techniques. It applies this approach on logs of a real-world business process used in a service delivery center and shows that individual process activities are correctly identified 90% of the time. Also, 65% of the activity traces were identified with zero errors and an additional 24% with a single error. This approach is generic and applicable to a wide array of business processes.

Indra: an integrated quantitative system for compliance management for IT service delivery

The explosive growth of business process implementations in various industries has brought into sharp focus the need for process compliance with regulatory policies. This has raised the need for business process compliance solutions requiring an automated and quantitative approach. Quantification of compliance enables an organization to accurately determine its compliance posture and take steps to improve process noncompliances in the future. To that end, in this paper, we propose Indra, our system for integrated compliance management. Indra takes a holistic approach toward compliance, focusing on a compliance life cycle comprising process modeling for maximal compliance at minimal cost, measuring noncompliance at runtime, analyzing the results of the measurement, and suggesting corrective actions to continuously improve process compliance in the future. The scope of this paper covers the analytic models and formulations for compliance maximization, along with a demonstration on a simplified version of a real-life example drawn from the IBM IT (information technology) service delivery units. We also describe ongoing piloting of our analytic models on real audit data from the IBM India Business Controls department. To the best of our knowledge, Indra is the first of its kind in providing integrated and quantitative compliance management.

Creation and interaction with large-scale domain-specific knowledge bases

The ability to create and interact with large-scale domain-specific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.

Building apps with open data in india: an experience

Open data is a well-established paradigm to make data available freely to everyone. The general belief is that open data leads to rapid pace in problem discovery, empowerment of citizens and greater collaborations. Opening up government data for free public access is a global trend, which India too followed in 2012. Though India is one of the early adopters, it has been ranked low in the last years Open Data Index. We participated in an open data app contest conducted by Government of India to come up with societal applications based on the datasets provided in data.gov.in portal. We would like to share our experiences and challenges during this contest and compare them with a similar internal contest that we participated in, where datasets were from U.S.

Effective Business Development for In-Market IT Innovations with Industry-Driven API Composition

Businesses around the world are recognizing the need for continuous in-market IT innovations to differentiate their products and services with competition and drive business success. However, they find it difficult to invest and incorporate innovative solutions because, by definition, the innovations have not been field tested enough before to become off-the-shelf offerings and provide proven cost versus benefit business case. On the other hand, the providers of innovative solutions also face the problem of communicating business value while making their technology available for demonstration because they do not have access to the proprietary client data and business processes to estimate their technology’s impact on client’s dynamic environment and cannot part with the solution without adequate compensation for their intellectual property in the innovations. To resolve these twin issues, in this paper, we propose a framework to develop and incorporate innovative solutions in the context of industry business processes and metrics using (Web) API composition. We have implemented this approach into a prototype, and early experience shows that it is able to expedite business development for in-market innovations.