Suman Roychoudhury

Analyzing Enterprise Models Using Enterprise Architecture-Based Ontology

Development and maintenance of enterprise systems is becoming more difficult due to change drivers along multiple interconnected dimensions. It is advisable to model the enterprise first and analyze it for potential concerns. For modeling enterprises, ontologies have been considered apt and have been used in the past for the same, but application of ontologies for EA analysis based on concepts of enterprise and relations between them have been scarce. We present our ongoing work on analyzing enterprise models using EA-based ontological representation of enterprise. Our contributions are twofold: first, we show how an existing EA modeling language can be leveraged to create EA ontology and second, we show how two known EA analyses can be realized using this ontology. Initial results suggest that ontology representation facilitates basic EA analysis prototyping due to right mix of representation and inference functionalities and is extensible for more involved EA analyses.

Modelling and Enterprises - The Past, the Present and the Future

Industry has been practicing model-driven development in various flavours. In general it can be said that modelling and use of models have delivered on the promises of platform independence, enhanced productivity, and delivery certainty as regards development of software-intensive systems. Globalization market forces, increased regulatory compliance, ever-increasing penetration of internet, and rapid advance of technology are some of the key drivers leading to increased business dynamics. Increased number of factors impacting the decision and interdependency amongst the key drivers is leading to increased complexity in making business decisions. Also, enterprise software systems need to commensurately change to quickly support the business decisions. The paper presents synthesis of our experience over a decade and half in developing model-driven development technology and using it to deliver several business-critical software systems worldwide.

A domain-specific controlled English language for automated regulatory compliance (industrial paper)

Modern enterprises operate in an unprecedented regulatory environment where increasing regulation and heavy penalties on non-compliance have placed regulatory compliance among the topmost concerns of enterprises worldwide. Previous research in the field of compliance has established that the manual specification of the regulations used by GRC frameworks not only fails to ensure their proper coverage but also negatively affects the turnaround time both in proving and maintaining the compliance. Our key contribution in this paper is an implementation of a controlled natural English like (domain-specific) language that can be used by domain experts to specify regulations for automated compliance checking. We demonstrate this language using examples from industry regulations in banking and financial services domain.

Analyzing Document Intensive Business Processes using Ontology

Knowledge is manifested in an enterprise in various forms ranging from unstructured operational data, to structured information like programs, as well as relational data stored in databases to semi-structured information stored in XML files. This information embodies the core of an enterprise knowledge base and analyzing the knowledge base can result in intelligent decision making. In order to realize this goal we begin with representing and analyzing unstructured knowledge present in an enterprise. In particular, this paper presents a real life example of a document intensive business process (International Trade) and attempts to model and analyze the process in a formal way. Typically, the information contained in a document intensive business process is of operational nature and requires extensive manual verification, which is both time consuming and error prone. Therefore, this research aims to eliminate such exhaustive manual verification by constructing a knowledge base in the form of ontology and apply suitable rule based reasoners to automate the verification process.

Mining enterprise models for knowledgeable decision making

Knowledge is stored in an enterprise in various forms ranging from unstructured operational data, legal documents to structured information like programs, as well as relational data stored in databases to semi-structured information stored in xml files. All these information if viewed from a holistic standpoint can help an enterprise to understand and reflect upon itself and thereby make knowledgeable decisions whenever required. In order to satisfy this objective of holistic knowledge representation and decision making, we begin with mining unstructured information present in an enterprise. In particular, in this paper, we intend to mine a document intensive business processes and extract information as a knowledge repository that captures the various stakeholders along with their intentions and the tasks they perform. The goal is to automate the validation of such business processes by eliminating any manual verification, which is time consuming and error prone. We believe this is the first step towards realizing our broader objective of collective modeling of enterprise knowledge that will involve mining of information available in unstructured, structured, relational as well as semi-structured form present in an enterprise.

A Case Study on Modeling and Validating Financial Regulations Using (Semi-) Automated Compliance Framework

Modern enterprises operate in an unprecedented regulatory environment where increasing regulation and heavy penalties on non-compliance have placed regulatory compliance among the topmost concerns of enterprises worldwide. Previous research in the field of compliance has established that the manual specification/tagging of the regulations not only fails to ensure their proper coverage but also negatively affects the turnaround time both in proving and maintaining the compliance. Our contribution in this paper is a case study using a subset of European Union Regulation in the financial markets, namely, Money Market Statistical Reporting (MMSR) and that we validated it in the context of our model-driven semi-automated compliance framework. The novelty of the framework is the key participation of domain experts to author regulatory rules in a controlled natural language to enable compliance checking. We demonstrate transformation of regulations present in legal natural language text (English) to a model form via authoring of Structured English rules in the context of MMSR regulations for a large European bank. This generated regulatory model is eventually translated to formal logic that enables formal compliance checking contrary to current industry practice, that provides content management-based, document-driven and expert-dependent ways of managing regulatory compliance.

Impact of Dynamicity and Causality on Cost Drivers in Effort Estimation

Software cost estimation is an important step that decides upon the effective manpower, schedule, pricing, profit and success for executing any medium to large sized project. Depending upon the underlying development methodology (e.g., code-centric, model-driven, product-line etc.) and past experience, every enterprise follows some cost estimation strategy that may be derived and customized from a standard cost model (e.g., COCOMO II). However, most software cost estimation techniques that are done at the start of a project do not consider the dynamicity and causality among cost drivers that can alter the accuracy of estimation. In this paper, we investigate those cost drivers that are time and inter-dependent and use system dynamics to simulate their effect in effort estimation.

Models to Aid Decision Making in Enterprises

Enterprises are complex heterogeneous entities consisting of multiple stakeholders with each performing a particular role to meet the desired overall objective. With increased dynamics that enterprises are witnessing, it is becoming progressively difficult to maintain synchrony within the enterprise for it to function effectively. Current practice is to rely on human expertise which is time-, cost-, and effort-wise expensive and also lacks in certainty. Use of machine-manipulable models that can aid in pro-active decision-making could be an alternative. In this paper, we describe such a prescriptive decision making facility that makes use of different modeling techniques and illustrate the same with an industrial case study.

Toward Structured Simulation of Enterprise Models

Todays Enterprises exist in highly dynamic environment. While enterprise-architecture (EA) based models help in holistic treatment of enterprise aspects, they are static in nature and do not represent the complex dynamic behavior of enterprise as it evolves over time. Instead of relying on guideline for simulating EA models as in other approaches, we propose a comprehensive metamodel of system dynamics and provide relation-based mapping to EA metamodel. Ongoing explorations suggest that while several challenges of simulating enterprise aspects remain unaddressed, in the least we take a step toward making simulation of EA models more structured.

Toward Structured Simulation of What-If Analyses for Enterprise

Todays Enterprises exist in highly dynamic environment. Simulation could be used to reveal complex dynamic behavior of enterprise, especially for playing out dynamic what-if scenarios, in determining enterprises response to a change. Instead of relying on guidelines for simulating prescriptive models of enterprise as in other approaches including our own in which we simulated intentional models of enterprise, we propose a comprehensive metamodel of system dynamics and provide relation-based mapping to intentional metamodel. Ongoing explorations suggest that while several challenges of simulating enterprise aspects for what-if analyses remain unaddressed, in the least we take a step toward making simulation of intentional models more structured.