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Dive into the research topics where Deepika Prakash is active.

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Featured researches published by Deepika Prakash.


conference on advanced information systems engineering | 2010

Decisions and Decision Requirements for Data Warehouse Systems

Naveen Prakash; Deepika Prakash; Daya Gupta

We develop the notion of a decision requirement as the pair where ‘information’ is that required by the decision maker to assess if the ‘decision’ is to be taken or not. It is shown that there are two kinds of decisions, imperative and managerial. The former are decisions about which transactional service out of a choice of transactional services is to be provided. Managerial decisions determine what infrastructure out of a set of possibilities is to be put in place. It is shown that a decision is the reason why a functionality of an information system is invoked. The notion of decision requirement is clarified through a decisional requirement meta model. This is supported by a decision and information meta model. The example of a health scheme is taken to illustrate the different kinds of decisions and decision requirements.


International Journal of Information System Modeling and Design | 2014

Eliciting Data Warehouse Contents for Policy Enforcement Rules

Deepika Prakash; Daya Gupta

Data Warehouse requirements engineering has been extensively looked at from the ENDS perspective of the Business Motivation Model, in terms of goals the system to-be wants to achieve. The authors propose that the MEANS perspective of this Model can drive the requirements engineering process. MEANS are organized into business policies and ‘policy enforcement rules. Starting from policies expressed in a higher order logic, the authors propose an approach to formulate policy enforcement rules. That subset of the set of formulated policy enforcement rules which is most appropriate for the business is to be selected. For this, the information relevant to the rules is to be kept in the Data Warehouse. The authors technique picks up the components of the policy enforcement rule to elicit the information that has a bearing on its selection. The elicited information is represented as an ER diagram. The authors rely on existing methodologies to convert an ER form into star schemas. The authors use the medical domain to illustrate our methodology.


the practice of enterprise modeling | 2009

Towards Better Fitting Data Warehouse Systems

Naveen Prakash; Deepika Prakash; Y. K. Sharma

In order to produce data warehouse systems that reflect organizational decisional needs, development should be rooted in the goals and decisions of organizations. The goal-decision-information model and associated information elicitation techniques for decision making are presented. There are four main techniques, Ends analysis, Means analysis, Critical Success Factor analysis, and Outcome Feedback analysis. Using these, the requirements engineer is able to elicit the required information as well as the sub decisions of a given decision. The elicitation techniques are then applied to these sub decisions. The elicitation process ends when all decisions/sub decisions have been thus processed. A comparison of this approach is made with data base driven and ER driven development approaches to data warehouse development to show that it produces systems that fit well with decisional requirements.


the practice of enterprise modeling | 2015

Towards DW Support for Formulating Policies

Deepika Prakash; Naveen Prakash

Data warehousing is largely directed towards “what to do next” type of decisions that essentially address operational decision-making needs. We argue for developing data warehouse support for deciding on organizational policies: policies evolve and therefore need continual decision-making support. We propose an RE approach for discovering information contents of a data warehouse for policy decision-making. Each policy is represented in an extended first order logic that can be converted into a policy hierarchy. Each node in this hierarchy can be selected, rejected, or modified. In order to take this decision, the relevant information is determined by using Ends Information Elicitation and Critical Success Factor Elicitation techniques for information elicitation. The elicited information is converted into an ER diagram from which star schemas are obtained.


research challenges in information science | 2017

A requirements driven approach to data warehouse consolidation

Deepika Prakash; Naveen Prakash

Data mart consolidation does schema as well as data integration of data marts so as to produce a single physical data mart/warehouse. This implies that multiple data marts must exist. Our proposal is to integrate requirements specifications of data marts. This upstream integration saves the effort of downstream activities performed when data marts are independently developed and then integrated. Without this integration, we get a new problem of loss of business control. Our integration approach is organized in five steps.


international conference on conceptual modeling | 2013

A Framework for Business Rules

Naveen Prakash; Deepak Kumar Sharma; Deepika Prakash; D. P. Singh

The subject of business rules is complex. We propose a 4-dimensional framework to better understand, communicate, and realize such rules in organizations and application systems. Our 4-dimensions are domain, that considers the role of business rules in business; system for properties of business rule management systems; application platform to understand support for business rules applications; and representation for expressing business rules. We derive these from work of the Business Rules Group, namely, the Business Rules Manifesto, Business Motivation Model, and Semantics of Business Vocabulary and business Rules, SBVR. We characterize our research position in terms of this framework.


Archive | 2018

Requirements Engineering for Data Warehousing

Naveen Prakash; Deepika Prakash

This chapter discusses the manner in which data warehouses are developed and the major issues in development of these systems. We start by providing a brief introduction to data warehousing concepts and the main problems of data warehouse projects. Thereafter, the systems development life cycle of data warehouse development, and the different ways in which development proceeds including the recently developed agile approaches are considered in this chapter. Finally, we focus on the requirements stage of the development life cycle and discuss the different techniques used for requirements engineering in detail.


Archive | 2018

Issues in Data Warehouse Requirements Engineering

Naveen Prakash; Deepika Prakash

The central role of a decision in data warehouse development is established in this chapter. It is shown that there are three types of decisions, for policy formulation, making policy enforcement rules and for business operations. The problem of requirements engineering is to discover these decisions as well as the information, the requirements granules, relevant to these. A data warehouse fragment is built for each granule. The factors that facilitate eliciting of requirements granules are identified. Finally, a requirements engineering process is outlined that subsumes consolidation of requirements granules.


Archive | 2018

Requirements Engineering for Transactional Systems

Naveen Prakash; Deepika Prakash

The book starts with failure statistics of transactional systems and the technological responses for failure mitigation. We discuss the evolution of development processes for transactional systems and highlight the emerging role of agile development. This mitigates failure through iterative and incremental development. The second response is to focus on system requirements. Here we first define the term requirements and consider the requirements engineering process. The approaches to requirements elicitation are reviewed. This lays the basis for the next chapter so as to discuss the differences between transactional and data warehouse requirement engineering respectively.


Archive | 2018

The Development Process

Naveen Prakash; Deepika Prakash

Since data warehouse projects are large and complex, this chapter proposes an agile technique for data warehouse development. The notion of a data warehouse user story is developed and its difference with user stories of Scrum is brought out. A model-driven approach is presented using which it is possible to build data warehouse user stories. In this model, a decision-making application is seen at three levels. Agility involves obtaining a vertical slice across these levels and thereby defining a requirements granule. A granule is implemented as a data warehouse fragment. The issue of consolidating data warehouse fragments is considered thereafter and an approach to consolidation is formulated. It is proposed that consolidation of requirements granules should be done and therefore, the consolidation process is a part of the requirements engineering process. The chapter ends with a description of the tool support that is provided.

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Daya Gupta

Delhi Technological University

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D. P. Singh

Central Avian Research Institute

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