# A Proposal of a Process Model for Requirements Elicitation in Information Mining Projects

Diego Mansilla, M. Pollo-Cattaneo, Paola Britos, Ramón García-Martínez

► To cite this version:

Diego Mansilla, M. Pollo-Cattaneo, Paola Britos, Ramón García-Martínez. A Proposal of a Process Model for Requirements Elicitation in Information Mining Projects. 6th Conference on Research and Practical Issues in Enterprise Information Systems (CONFENIS), Sep 2012, Ghent, Belgium. pp.165-173, 10.1007/978-3-642-36611-6_13 . hal-01484676

HAL Id: hal-01484676

https://inria.hal.science/hal-01484676

Submitted on 7 Mar 2017

**HAL** is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# A Proposal of a Process Model for Requirements Elicitation in Information Mining Projects

Mansilla, D.¹, Pollo-Cattaneo, M.¹,², Britos, P.³, García-Martínez, R.⁴

¹ Information System Methodologies Research Group. Technological National University. Buenos Aires, Argentina.

² PhD Program in Computer Science. Computer Science School. National University of La Plata. Buenos Aires, Argentina.

³ Information Mining Research Group. National University of Rio Negro at El Bolson, Río Negro, Argentina.

⁴ Information Systems Research Group. National University of Lanus, Buenos Aires, Argentina.

dmansilla@educ.ar; fpollo@posgrado.frba.utn.edu.ar; pbritos@unrn.edu.ar; rgarcia@unla.edu.ar

**Abstract.** A problem addressed by an information mining project is transforming existing business information of an organization into useful knowledge for decision making. Thus, the traditional software development process for requirements elicitation cannot be used to acquire required information for information mining process. In this context, a process of requirements gathering for information mining projects is presented, emphasizing the following phases: conceptualization, business definition and information mining process identification.

**Keywords.** Process, elicitation, information mining projects, requirements.

## 1 Introduction

Traditional Software Engineering offers tools and process for software requirements elicitation which are used for creating automatized information systems. Requirements are referred as a formal specification of what needs to be developed. They are descriptions of the system behaviour [1].

Software development projects usually begin by obtaining an understanding of the business domain and rules that govern it. Understanding business domains help to identify requirements at the business level and at product level [2], which define the product to be built considering the context where it will be used. Models such as Context Diagram, Data Flow Diagrams and others are used to graphically represent the business process in the study and are used as validation tools for these business processes. A functional analyst is oriented to gather data about inputs and outputs of the software product to be developed and how that information is transformed by the software system.
Unlike software development projects, the problem addressed by information min-
ing projects is to transform existing information of an organization into useful knowl-
edge for decision making, using analytical tools [3]. Models for requirements elicita-
tion and project management, by focusing on the software product to be developed,
cannot be used to acquire required information for information mining processes. In
this context, it is necessary to transform existing experience in the use of requirements
elicitation tools in the software development domain into knowledge that can be used
to build models used in business intelligence projects and in information mining proc-
ess [4] [5] [6].

This work will describe the problem (section 2), it will present a proposal for a
process model for requirements elicitation in information mining projects (section 3),
emphasizing in three phases: Conceptualization (section 3.1), Business Definition
 subsections such as DM [7], P3TQ [8], y SEMMA [9].

## 2 State of current practice

Currently, several disciplines have been standardized in order to incorporate best
practices learned from experience and from new discoveries.

The discipline of project management, for example, generated a body of knowl-
edge where the different process areas of project management are defined. Software
engineering specify different software development methodologies, like the software
requirements development process [1]. On the other side, related to information min-
ing projects, there are some methodologies for developing information mining sys-
tems such as DM [7], P3TQ [8], y SEMMA [9].

In the field of information mining there is not a unique process for managing pro-
jects [10]. However, there are several approaches that attempt to integrate the knowl-
edge acquired in traditional software development projects, like the Kimball Lifecycle
[11], and project management framework in medium and small organizations [12]. In
[13] an operative approach regarding information mining project execution is pro-
posed, but it does not detail which elicitation techniques can be used in a project.

The found problem is that previously mentioned approaches emphasize work
methodologies associated with information mining projects and do not adapt tradi-
tional software engineering requirements elicitation techniques. In this situation, it is
necessary to understand the activities that should be taken and which traditional elici-
tation techniques can be adapted for using in information mining projects.

## 3 Proposed Elicitation Requirement Process Model

The proposed process defines a set of high level activities that must be performed
as a part of the business understanding stage, presented in the CRISP-DM methodol-
ogy, and can be used in the business requirements definition stage of the Kimball
Lifecycle. This process breaks down the problem of requirement elicitation in infor-
mation mining projects into several phases, which will transform the knowledge ac-
quired in the earlier stage. Figure 1 shows strategic phases of an information mining
project, focusing on the proposed requirement elicitation activities.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Information Mining Process phases.</figcaption></figure>

The project management layer deals with coordination of different activities needed to achieve the objectives. Defining activities in this layer are beyond this work. This work identifies activities related to the process exposed in [11], and can be used as a guide for the activities to be performed in an information mining project.

## 3.1 Business Conceptualization Phase.

The Business Conceptualization phase is the phase of the elicitation process that will be used by the analyst to understand the language used by the organization and the specific words used by the business. Table 1 summarizes inputs and outputs of the Business Conceptualization phase.

<table><caption>Table 1. Business Conceptualization phase inputs and outputs.</caption><thead><tr><th rowspan="2">Phase</th><th rowspan="2">Task</th><th colspan="2">Input product</th><th rowspan="2">Transformation technique</th><th colspan="2">Output Product</th></tr><tr><th>Input</th><th>Representation</th><th>Output</th><th>Representation</th></tr></thead><tbody><tr><td rowspan="3">Business Conceptualization</td><td>Business Understanding</td><td>Project Definition</td><td>Project KickOff</td><td>Project Sponsors Analysis</td><td>List of users to be interviewed</td><td>List of users to be interviewed template.</td></tr><tr><td>Business Process data gathering</td><td>List of users to be interviewed</td><td>List of users to be interviewed</td><td>Interviews Workshops</td><td>Gathered Information</td><td>Information gathering template</td></tr><tr><td>Business Model Building</td><td>Gathered Information</td><td>Information gathering Template</td><td>Analysis of gathered information</td><td>Use Case Model</td><td>Use Case Model template</td></tr></tbody></table>

Interviewing business users will define information related problems that the organization has. The first activity is to identify a list of people that will be interviewed. This is done as part of the business process gathering activity.

In these interviews, information related to business process is collected and modeled in use cases. A business process is defined as the process of using the business on behalf of a customer and how different events in the system occur, allowing the customer to start, execute and complete the business process [14]. The Business Analyst should collect specific words used in business processes in order to obtain both a description of the different tasks performed in each function, as well as the terminology used in each use case.

The Use Case modeling task uses information acquired during business data gathering and, as a last activity of this phase, will generate these models.

## 3.2 Business Definition Phase

This phase defines the business in terms of concepts, vocabulary and information repositories. Table 2 shows inputs and outputs of this phase
<table><caption>**Table 2.** Business definition phase inputs and outputs</caption><thead><tr><th rowspan="2">Phase</th><th rowspan="2">Task</th><th colspan="2">Input product</th><th rowspan="2">Transformation technique</th><th colspan="2">Output Product</th></tr><tr><th>Input</th><th>Representation</th><th>Output</th><th>Representation</th></tr></thead><tbody><tr><td rowspan="3">Business Definition</td><td>Build Dictionary of Business Vocabulary</td><td>Use Case Model</td><td>Use Case Model Template</td><td>Use Case Analysis</td><td>Concepts Dictionary</td><td>Concept Dictionary template</td></tr><tr><td>Establish Concept Relationships</td><td>Concepts Dictionary</td><td>Concept Dictionary template</td><td>Entity Relationship model</td><td>Concepts relationships</td><td>Concept Relationship Model</td></tr><tr><td>Build a Data Repository Map</td><td>Concepts relationships</td><td>Concept Relationship Model</td><td>Documentation Analysis</td><td>Data Repository Map</td><td>Data Repository Map template</td></tr></tbody></table>

The objective is to document concepts related to business process gathered in the Business Conceptualization Phase and discover its relationships with other terms or concepts. A dictionary is the proposed tool to define these terms. The structure of a concept can be defined as shown in table 3.

<table><caption>**Table 3.** Concept Structure</caption><thead><tr><th>Structure element</th><th>Description</th></tr></thead><tbody><tr><td>Concept</td><td>Term to be defined</td></tr><tr><td>Definition</td><td>Description of the concept meaning.</td></tr><tr><td>Data structure</td><td>Description of data structures contained in the concept</td></tr><tr><td>Relationships</td><td>A List of Relationships with other concepts</td></tr><tr><td>Processes</td><td>A list of processes that use this concept</td></tr></tbody></table>

Once the dictionary is completed the map, the analyst begins to analyze the various repositories of information in the organization. It is also important to determine volume information, as this data can be used to select the information mining processes applicable to the project. The acquired information is used to build a map, or a model, that shows the relationship between the business use cases, business concepts and information repositories. This triple relationship can be used as the start point of any technique of information mining processes.

### 3.3 Identification of Information Mining Process Phase

The objective of the phase is to define which information mining process can be used to solve the identified problems in the business process. There are several processes that can be used [16], for instance:

- Discovery of behavior rules (DBR)

- Discovery of groups (DOG)

- Attribute Weighting of interdependence (AWI)

- Discovery of membership group rules (DMG)

- Weighting rules of behavior or Membership Groups (WMG)

This phase does not require any previous input, so activities can be performed in parallel with activities related to Business Conceptualization. Table 4 shows inputs and outputs of this phase.
<table><caption>**Table 4.** Inputs and Outputs of Identification of Information Mining Process Phase</caption><thead><tr><th rowspan="2">Phase</th><th rowspan="2">Task</th><th colspan="2">Input product</th><th rowspan="2">Transformation technique</th><th colspan="2">Output Product</th></tr><tr><th>Input</th><th>Representation</th><th>Output</th><th>Representation</th></tr></thead><tbody><tr><td rowspan="2">Identification of Information Mining Process</td><td>Identify Business Problems</td><td>Use Case Model</td><td>Use Case Model Template</td><td>Documentation Analysis</td><td>Problem List</td><td>Problem List Template</td></tr><tr><td>Select an information mining process</td><td>Problem List Concept Dictionary</td><td>Problem List Template Dictionary Template</td><td>LEL Analysis</td><td>An information Mining process to be applied</td><td></td></tr></tbody></table>

The list of business problems must be prioritized and be written in natural language, using the user's vocabulary. Only important or critical problems are identified.

Analysis of the problems in the list has to be done. This analysis can be done using the model known as "Language Extended Lexicon (LEL)" [17][18] and can be used as a foundation of the work performed in this phase: breaking down the problem into several symbols presented in the LEL model. This model shows 4 general types of symbol, subject, object, verb and state.

To define useful information mining process, a decision table is proposed. The table analyses LEL structures, concepts identified in the Business Conceptualization phase, existing information repositories and problems to be solved. All the information is analyzed together and according to this analysis, an information mining process is selected as the best option for the project. Table 5 shows the conditions and rules identified as foundations in this work. An important remark is that subjects discovery refers to concepts or subject that hasn't been identified as part of the business domain

<table><caption>**Table 5.** Information mining process selection decision table</caption><thead><tr><th>Condition</th><th>R01</th><th>R02</th><th>R03</th><th>R04</th><th>R05</th></tr></thead><tbody><tr><td>Does concepts identified as objects exist in some information repository?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Are there any concept identified as an object that are not stored in any information repository?</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>The action represented by a verb. Denotes the discovery of subject concepts?</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Are there any concept that can be associated with factors?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Have the required factors been identified?</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Can we deduce factors based on existing information available on repositories?</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>The action represented by a verb. Associates subjects and objects?</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Is Analysis of factors required to obtain a group of subjects or objects?</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Actions</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>The technique to be applied is:</td><td>DBR</td><td>DOG</td><td>AWL</td><td>DMG</td><td>WMG</td></tr></tbody></table>

The objective of the table is to be able to decide, through the analysis of the information gathered about the business, which information mining process can be applied to the project. An important remark is that this decision table will add new knowledge and new rules, with the end of improving the selection technique criteria. With more
projects used as input and more experience acquired in these projects, the rules pro-
posed on the table can be adjusted and then we can get a better selection choice.

The Process Information Mining Identification phase is the last phase of the proc-
ess. The following tasks will depend upon project managing process and tasks defined
for the project.

4 Proof of concept

A case study is presented next to prove the proposed model.

4.1 Business Description

A real estate agency works mainly with residential properties in the suburban area. It's lead by two owners, partners in equal shares. This real state agency publishes its portfolio in different media, mostly local real estate magazines. Published properties are in the mid range value, up to three hundred thousand dollars. It only has one store, where all the employees work. The following business roles are covered: a real estate agent, salesman, administrative collaborators and several consultants.

4.2 Process Execution

The first step of the process consists in two activities: identify project stakeholders
and set the list of people to be interviewed. In this case, with the little business infor-
mation that we have, we can identify three stakeholders: the owners and the real estate
agent.

The second step is to set up the interviews of the stakeholders and gather informa-
tion related to the business in the study. The following paragraph describes informa-
tion obtained in the interview.

This agency focuses on leasing and selling real estate. Any person can offer a
house for sale. If a house is for sale, the real estate agent will estimate the best offer
for the property being sold. When a person has an interest in buying a home, they
complete a form with the contact details and characteristics that must meet the prop-
erty. If there are any properties that meet the requested criteria, they are presented to
the customer. The real estate agency considered clients as those who have offered a
home to sell or have already begun a process of buying a home offered, and consid-
ered interested customers, persons who are consulting on the proposed properties or
are looking for properties to buy. If interested customers agree on the purchase of a
property will be customers of the estate and begins the process of buying property.
The customer contact information and the property details are stored in an Excel file.

In this case, we can identify the following Business Use Cases:

- Sell a property Use Case, action of a person selling a property.

- Buy a property Use Case, action of a person buying a property.

- Show a property managed by the real estate agency Use Case, reflects the action of showing a real estate available for sale to interested parties.

For the Business Definition Phase, the business concept dictionary is created. From
gathered information, the concepts shown in table 6 can be identified.
<table><caption>**Table 6. Identified Business Concepts**</caption><tr><td><strong>Selling Customer:</strong></td><td>A person who offers a property for sale</td></tr><tr><td><strong>Structure:</strong></td><td>Name and Last Name<br>Contact Information</td></tr><tr><td><strong>Relationships:</strong></td><td>Property<br>Property appraisal</td></tr><tr><td><strong>Business Process:</strong></td><td>Sell a Property</td></tr></table>

<table><tr><td><strong>Property Appraisal:</strong></td><td>Appraisal of property for sale.</td></tr><tr><td><strong>Structure:</strong></td><td>Appraisal value (Number)<br>Property ID<br>Transaction Currency</td></tr><tr><td><strong>Relationships:</strong></td><td>Property<br>Customer</td></tr><tr><td><strong>Business Processes</strong></td><td>Sell a Property<br>Offer a Property</td></tr></table>

Identified concepts are analyzed in order to find relationships between themselves. A class model can show the basic relationships between identified concepts in the case.

From the gathered business information, a problem found is that the real estate agent wants to know, when a property is offered for sale, which customers could be interested in buying the property. Following the identification, a LEL analysis is done with each problem on the list. In this case, the analysis finds the symbols presented in table 7.

<table><caption>**Table 7. Real Estate agency problems related symbols.**</caption><thead><tr><th>Property</th><th>[Object]</th></tr></thead><tbody><tr><td><strong>Idea</strong><br>- It's the object that the real estate agency sells<br>- It has its own attributes<br><strong>Impact</strong>: It is sold to a Customer</td><td></td></tr><tr><td><strong>To Offer a property</strong></td><td>[Verb]</td></tr><tr><td><strong>Idea</strong><br>- The action of showing a property to a customer.</td><td></td></tr><tr><td><strong>Impact</strong><br>- The property must satisfy the customer requirements.</td><td></td></tr></tbody></table>

<table><thead><tr><th>Customer</th><th>[Subject]</th></tr></thead><tbody><tr><td><strong>Idea</strong><br>- A Person interested in buying a property.<br>- A Person who is selling a property.<br><strong>Impact</strong>: Fills a form with buying criteria.</td><td></td></tr><tr><td><strong>Interested</strong></td><td>[Status]</td></tr><tr><td><strong>Idea</strong><br>- A Customer state achieved when a property meets his or her requirements</td><td></td></tr><tr><td><strong>Impact</strong><br>- The property is shown to the interested party.</td><td></td></tr></tbody></table>

With the obtained LEL analysis, information repositories and defined business concepts, the information mining process to apply in the project, will be determined. The decision table presented in section 3.3 is used, checking the conditions against the gathered information. The result of this analysis states that the project can apply the process of Discovery Rules of Conduct.

## 5 Conclusion

This work presents a proposal of a process model for requirements elicitation in information mining projects, and how to adapt in these projects, existing elicitation techniques. The process breaks down in three phases, in the first phase the business is analyzed (Conceptualization phase), later, a business model is built and defined to understand its scope and the information it manages (Business Definition phase), and finally, we use the business problems found and the information repositories that stores business data as an input for a decision table to establish which information mining technique can be applied to a specific information mining project (Identification of an Information Mining Process).

As a future line of work, several cases are being identified to support the empirical case proposed, emphasizing the validation of the decision table presented in section 3.3.
6 References

[1] Sommerville, I., Sawyer, P. 1997. Requirements Engineering: A Good Practice Guide. Chichester, England: John Wiley & Sons.

[2] Soren Lauesen, 2002. Software Requirements. Styles and Techniques. London, Pearson Education.

[3] Pollo-Cattaneo, F., et al. 2010. Proceso de Educación de Requisitos en Proyectos de Explotación de Información. En Ingeniería de Software e Ingeniería del Conocimiento: Tendencias de Investigación e Innovación Tecnológica en Iberoamérica (Editores: R. Aguilar, J. Díaz, G. Gómez, E- León). Pág. 01-11. Alfaomega Grupo Editor. ISBN 978-607-707-096-2.

[4] Pollo-Cattaneo, F., et al. (2010). Ingeniería de Proyectos de Explotación de Información. Proceedings XII Workshop de Investigadores en Ciencias de la Computación. Pág. 172-176.

[5] Pytel, P., et al. (2011). Ingeniería de Requisitos Basada en Técnicas de Ingeniería del Conocimiento. Proceedings XIII Workshop de Investigadores en Ciencias de la Computación. Pág. 426-429. ISBN 978-950-673-892-1.

[6] Chapman, P., et al. (2000). CRISP-DM 1.0 Step by step BIguide. Edited by SPSS. http://tinyurl.com/crispdm

[7] García-Martínez, R., et al (2011). Information Mining Processes Based on Intelligent Systems. Proceedings of II International Congress on Computer Science and Informatics (INFONOR-CHILE 2011). pp. 87-94. ISBN 978-956-7701-03-2.

[8] Pyle, D. (2003). Business Modeling and Business Intelligence. Morgan Kauffmann Publishers.

[9] SAS. 2011. SAS Enterprise Miner: SEMMA. http://tinyurl.com/semmaSAS

[10] Pollo-Cattaneo, F., et al. (2009). Metodología para Especificación de Requisitos en Proyectos de Explotación de Información. Proceedings XI Workshop de Investigadores en Ciencias de la Computación. Pág. 333-335. ISBN 978-950-605-570-7.

[11] Kimball, R., et al. (2011), The Data Warehouse Lifecycle Toolkit. John Wiley & Sons.

[12] Vanrell, J., Bertone, R., García-Martínez, R., 2010. Modelo de Proceso de Operación para Proyectos de Explotación de Información.

[13] Britos, P., Dieste, O., García-Martínez, R. 2008. Requirements Elicitation in Data Mining for Business Intelligence Projects. Advances in Information Systems Research, Education and Practice. David Avison, George M. Kasper, Barbara Pernici, Isabel Ramos, DewaldRoode Eds. (Boston: Springer), IFIP Series, 274: 139-150.

[14] Jacobson, I., Ericsson, M., Jacobson, A. 1995. The Object Advantage. Business Process Reengineering with Object Technology. Addison Wesley Publishing Company. p. 98.

[15] García-Martínez, R., et al. (2011). Towards an Information Mining Engineering. En Software Engineering, Methods, Modeling and Teaching. Universidad de Medellín Editorial. ISBN 978-958-8692-32-6. pp. 83-99.

[16] Pollo-Cattaneo, F., et al. 2010. Ingeniería de Procesos de Explotación de Información. En Ingeniería de Software e Ingeniería del Conocimiento: Tendencias de Investigación e Innovación Tecnológica en Iberoamérica (Editores: R. Aguilar, J. Díaz, G. Gómez, E- León). Pág. 252-263. Alfaomega Grupo Editor. ISBN 978-607-707-096-2.

[17] Leite, J.C.S.P.. 1994. Notas de Aula. Material del curso de Ingeniería de Requisitos.

[18] Fresno, M., et al. 1998. Derivación de objetos utilizando LEL y Escenarios en un caso real. http://wer.inf.puc-rio.br/wer98/artigos/89.html. Last Access July 2012