Sagar Sunkle

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.

Cost estimation for model-driven engineering

Cost estimation studies in model-driven engineering (MDE) are scarce; first, due to difficulty in quantifying qualitative characteristics of MDE that supposedly influence software development effort and second, due to the complexity of measuring varied artifacts that are generated and used in an end-to-end MDE toolset. A cost estimation approach is therefore needed that can incorporate characteristics of MDE that affect economies of scale and effort in application development with the size computation of various artifacts in MDE. We plan to use the constructive cost model (COCOMO) II to obtain baseline cost estimation of MDE applications. Our main contributions are a method to capture the qualitative characteristics of MDE in terms of cost drivers in COCOMO II and a method for computation of various artifacts generated by an MDE toolset. Our initial exploration of these ideas suggests that it is possible to automate cost estimation for MDE.

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.

Toward Better Mapping between Regulations and Operations of Enterprises Using Vocabularies and Semantic Similarity

Industry governance, risk, and compliance (GRC) solutions stand to gain from various analyses offered by formal compliance checking approaches. Such adoption is made difficult by the fact that most formal approaches assume that a mapping between concepts of regulations and models of operational specifics exists. Industry solutions offer tagging mechanisms to map regulations to operational specifics; however, they are mostly semi-formal in nature and tend to rely extensively on experts. We propose to use Semantics of Business Vocabularies and Rules along with similarity measures to create an explicit mapping between concepts of regulations and models of operational specifics of the enterprise. We believe that our work-in-progress takes a step toward adapting and leveraging formal compliance checking approaches in industry GRC solutions.

Explanation of Proofs of Regulatory (Non-)Compliance Using Semantic Vocabularies

With recent regulatory advances, modern enterprises have to not only comply with regulations but have to be prepared to provide explanation of proof of (non-)compliance. On top of compliance checking, this necessitates modeling concepts from regulations and enterprise operations so that stakeholder-specific and close to natural language explanations could be generated. We take a step in this direction by using Semantics of Business Vocabulary and Rules to model and map vocabularies of regulations and operations of enterprise. Using these vocabularies and leveraging proof generation abilities of an existing compliance engine, we show how such explanations can be created. Basic natural language explanations that we generate can be easily enriched by adding requisite domain knowledge to the vocabularies.

Comparison and Synergy Between Fact-Orientation and Relation Extraction for Domain Model Generation in Regulatory Compliance

Modern enterprises need to treat regulatory compliance in a holistic and maximally automated manner, given the stakes and complexity involved. The ability to derive the models of regulations in a given domain from natural language texts is vital in such a treatment. Existing approaches automate regulatory rule extraction with a restricted use of domain models counting on the knowledge and efforts of domain experts. We present a semi-automated treatment of regulatory texts by automating in unison, the key steps in fact-orientation and relation extraction. In addition, we utilize the domain models in learning to identify rules from the text. The key benefit of our approach is that it can be applied to any legal text with a considerably reduced burden on domain experts. Early results are encouraging and pave the way for further explorations.

Model-driven regulatory compliance: A case study of “Know Your Customer” regulations

Modern enterprises face an unprecedented regulatory regime. Industry governance, risk, and compliance (GRC) solutions are document-oriented and expert-driven. Formal compliance checking techniques in contrast attempt to provide ways for rigorous modeling and analysis of regulatory compliance but miss out on holistic GRC perspective due to missing integration between diverse set of (semi-) formal models. We show that streamlining regulatory compliance using multiple purposive models of various aspects of regulations, it is possible to leverage both the rigor of formal techniques and the holistic enterprise GRC perspective. Our contributions are twofold. First, we present a model-driven architecture based on a conceptual model of integrated GRC that is capable of addressing key challenges of regulatory compliance. Second, using Know Your Customer regulations in Indian context as a case study, we demonstrate the utility of this architecture. Initial results with KYC regulations are promising and point to further work in model-driven regulatory compliance.

Incorporating Directives into Enterprise TO-BE Architecture

To stay competitive, enterprises must respond to changes as effectively and efficiently as possible and ensure the employed courses of action, whether in response to change or even to optimize business as usual, fall within the purview of internal and external directives. Often, the traceability from change drivers that led to specific directives being applied to actual business rules implementing the directives is never captured in machine processable and analyzable manner, making compliance to directives hard to track and demonstrate. We present a model-based solution that enables a) modeling directives at various levels of detail on top of extended enterprise architecture-based models of enterprise, b) analyzing the models for compliance, and c) ensuring operationalization of directives. Initial explorations with a real world case study suggest that it might be possible to establish both top-down and bottom-up traceability for directives toward compliance checking.

Towards Automated Generation of Regulation Rule Bases using MDA.

Enterprises today face the problem of complying with ever-increasing regulation. Use of rule engines for implementing compliance is widespread, however, the rule base needs to be encoded manually. We present a method using model-driven architecture (MDA) to automate generation of rules in a rule language, from a platform-independent model derived from a specification given by domain experts. We demonstrate how a Semantics of Business Vocabulary and Rules (SBVR) model of regulation rules can serve as the common source model for generating rules on various categories of rule engine platforms. The approach is illustrated using a real-life case study from the MiFID-2 financial regulation.

Informed Active Learning to Aid Domain Experts in Modeling Compliance

Modern enterprises face an unprecedented regulatory regime. Traditional compliance practices in enterprises rely heavily on domain experts whose judgement determines what compliance means and how to reflect regulations onto the enterprise processes and data to make them compliant. These activities are mostly manual in nature. We present a machine learning approach to modeling compliance. Our key innovations are a) use of active learning- a semi-supervised system capable of learning interactively from the domain expert to identify regulations and b) informing the feature representation of the active learner based on domain- specific entities and relations to effectively build a domain model of regulations. Early results show that our system reduces the burden on domain experts to a large extent, enables latching domain experts knowledge, and makes further steps in compliance easier by the use of models.