The Added Value of OLAP Preferences in What-If Applications

Mariana Carvalho and Orlando Belo

ALGORITMI R&D Centre, Department of Informatics, School of Engineering, University of Minho,

Campus de Gualtar, 4710-057, Braga, Portugal

Keywords: Business Intelligence, What-If Analysis, On-Line Analytical Processing, Usage Preferences, OLAP Mining,

Multidimensional Databases, Hybrid Methodology, Decision-making Process.

Abstract: These days, enterprise managers involved with decision-making processes struggle with several problems

related to market position or business reputation of their companies. Collecting data and retrieving high-

quality set of information is one of the main priority tasks of enterprise managers involved in decision-making

processes. To overcome the difficulties that may arise from market competitiveness and gain some kind of

competitive advantage, it is important that these managers make the most of adequate tools in order to get the

right set of highly refined information. What-If analysis can help managers getting the competitive advantage

they need. It allows for simulating hypothetical scenarios and analyzing the consequences of specific changes

without harming business activities. In this paper, we propose a hybridization methodology, which combines

What-If analysis process with OLAP usage preferences, for optimizing decision processes. We present and

discuss the integration of OLAP usage preferences in the conventional What-If analysis with a case example.

1 INTRODUCTION

The incredible growth in gathering electronic data

and the increase competitiveness in business

environments are important factors to consider in

knowledge-based society. Companies need to make

better use of analytical information systems,

techniques and models for multidimensional data

exploration and analysis, trying to gain competitive

advantages from a better use of knowledge. An

increasing number of companies need to obtain

relevant information using tools and business data for

reducing redundant information, increasing profits

and saving time, reducing waste and optimizing

decisions. There has been a noticeable increase in the

number and quality of data retrieving and handling

processes created, developed or used by companies.

On-line analytical processing (OLAP) is one of

the most important tools used by companies in

decision-support systems. Navigating in

multidimensional data using OLAP operators allows

for exploring and analyzing data stored in high-

specialized structures: cubes. OLAP tools provide

means for business analytics as well as

multidimensional view over business data that are

very efficient logical ways for analyzing businesses

activities. A decision-support analysis process is an

interactive exploration of multidimensional

databases, often performed in ad hoc manner

allowing users to see data from different perspectives

of analysis. OLAP supports quite well decision

process, but it is not capable of giving anticipations

of future trends. What-If analysis (Golfarelli et al.,

2006) technology helps filling this gap. The OLAP

cube (Harinarayan et al., 1996) is the most adequate

data structure for supporting a What-If simulation due

to its characteristics (Golfarelli et al., 2006). It is a

well-known data structure for supporting information

analysis, being capable for representing historical

trends supporting information at different abstraction

levels.

The process of What-If analysis allows for

analyzing possible effects in the behavior of the

business caused by changing the variables’ values.

These effects cannot be noticed with a manual

analysis of historical data. What-If analysis permits

decision makers to manipulate parameters to make

hypothetical scenarios and get better decisions. In

other words, What-If analysis helps decision makers

for assessing beforehand what can happen in complex

systems as result of changing what can be consider a

normal business behavior. This technology is useful

because they can make decisions according to the

results without endangering the real business and get

What-If analysis scenarios for testing and validating

their business hypothesis to support their decisions.

In fact, What-If analysis can be a safer solution for

addressing any doubt and ensuring, if possible, that

Carvalho, M. and Belo, O.

The Added Value of OLAP Preferences in What-If Applications.

DOI: 10.5220/0008354301710180

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 171-180

ISBN: 978-989-758-382-7

171

the subsequent decision will have some success.

Moreover, it allows for analyzing different scenarios

and perspectives of business, anticipating some

solutions.

Usually, the lack of user’s expertise during a

What-If design and implementation is one of the

pitfalls of the What-If analysis process. A user, who

may not be familiar with the What-If process or even

the business data, probably will not choose the most

correct parameters in an application scenario, leading

to poor results and inadequate outcomes. One

possible solution is to integrate OLAP usage

preferences (Golfarelli and Rizzi, 2009) (Jerbi et al.,

2009) in this process. In OLAP platforms, when

performing complex queries, it is likely that the

outcome will be a huge volume of data that may be

quite difficult to analyze. With OLAP usage

preferences, it is possible to filter the volume of data.

The returned data is adjusted to the users’ needs and

to the business requirements without losing data

quality. The extraction of OLAP usage preferences

according to each analytic session promoted by a user

may come as an advantage to decision-makers, since

it provides a very effective way to personalize the

outcome of queries of analytical sessions and

multidimensional data structures acting as their

decision-making support. OLAP preferences can

recommend axis of analysis that are strongly related

to each other, introducing helpful and useful

information to the application scenario under

construction.

The hybridization process (Carvalho and Belo,

2016) consists in the integration of the OLAP usage

preferences in the conventional What-If scenarios.

The hybridization process can suggest OLAP

preferences, providing the user the most adequate

scenario parameters according to its needs and

making What-If scenarios more valuable. Therefore,

in this paper, we propose a recommendation

methodology for assisting the user during the

decision-support analysis process; we present and

discuss the integration of OLAP usage preferences in

conventional What-If scenarios and present a case

example that illustrates the proposed hybridization

methodology. The remaining part of this paper

presents an overview about the importance of What-

If analysis and its application (Section 2), describes

the authors’ methodology and also show how the

What-If scenarios are created and enhanced using the

extracted OLAP preferences (Section 3). Next, in

section 4, a case example using the proposed

methodology is presented and analyzed, and with a

developed software platform, we show how the

process works, showing all the steps between the

extraction of the rules until the definition of the What-

If scenario. Finally, section 5 concludes the paper and

discusses some possible future research directions.

2 RELATED WORK

Over the last decade, the evolution of What-If

analysis was remarkable, as evidenced by several

papers that were published during this period.

Golfarelli et al., (2006) are a landmark in What-If

analysis. The authors present What-If analysis as a

solution methodology for the resolution of problems

in a business intelligence context. They analysed and

discussed some of the lessons learned and the

experience obtained after using What-If projects in

real business processes, where they found immature

technology, complexity of design and lack of design

methodology. They also suggest several tools that

present What-If features that help to ease users’

problems.

Kottemann et al., (2009) addressed unaided

decision support systems and decision support

systems aided with What-If analysis. These authors

presented a formal simulation approach, comparing

unaided and aided decision-making performance.

They verified that the performance differences

between the two cases are significant, concluding that

the effectiveness of the decision making strategies is

dependent on the environmental factors and on the

supporting tools; and though What-If analysis is very

helpful and popular in decision making, it is not 100%

effective. In the same year, Zhou and Chen (2009)

addressed What-If analysis in Multidimensional

OLAP environments. They paid special attention to

storage and organization of hypothetical modified

data, when dealing with What-If analysis, because

some cells of the data cube may change over time.

The solution proposed by Zhou and Chen (2009)

consists in storing the new hypothetical modified data

into a HU-Tree data structure (variant of r*-tree). This

allows for storing and managing hypothetical

modified cells using the hypothetical cube, instead of

modifying the original cube directly. When a What-If

analysis is processed, the original cube and the What-

If cube are manipulated simultaneously. In the next

year and following their previous work, Golfarelli

and Rizzi (2010) focuses on the resolution of a

particular problem of a real case study using the

What-If methodology they proposed before. In this

paper, they mainly focused on getting a precise

formalism for expressing conceptually the simulation

model. They achieve a simulation model that satisfies

several issues; for instance, with their methodology,

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they can model static, functional and dynamic aspects

in an integrated fashion, combining use cases, class

and activity diagrams, build specific What-If

constructs using the UML stereotyping mechanism,

and get multiple levels of abstraction using YAM2.

Later, Gavanelli et al. (2012) suggested

improving the traditional What-If analysis process,

typically based on a “generate and test” paradigm, by

integrating a combinatorial optimization and

decision-making component, which helps in

enriching and identifying the most interesting What-

If scenarios, which are then used when performing the

simulation. The authors apply their methodology in

social policy making. In the next year, Xu et al.

(2013) presented a specialized work that mainly

focused on improving the performance of What-If

query processing strategies for Big Data in an OLAP

system. They aimed to improve the classical delta-

table merge algorithm in the process of What-If,

taking advantage from the MapReduce framework.

Also, they explain a What-If algorithm of

BloomFilterDM (Bloom filter-based delta table

merging algorithm) and What-If algorithm of

DistributedCacheDM (distributed cache-based delta

table merging algorithm). Next, Hung et al. (2017)

presents a work that aims to address the What-If

analysis process when there are conflicting goals, i.e.,

multiple goals that are contradictory between each

other. The authors propose the use of data ranges for

the input scenario parameters in the What-If

simulation, for limiting the number of scenarios

explored. They present several ways for optimizing

input parameters to get a What-If analysis outcomes

that balances the conflicting goals.

What-If analysis has been widely used in several

areas, and it proved to be a useful technique for BI,

like data warehouses, relational databases and OLAP

cubes. Deutch et al (2013) described the Caravan

system, which was developed for performing What-If

analysis. With this system, it is possible to users to get

a personalized session, oriented to their needs,

displaying only relevant data and exploring different

answers within computed views. The novelty of this

system is the use of Provisioned Autonomous

Representations (PARs) to maintain the necessary

information of the What-If scenarios instead of

preserving the entire source database. Saxena et al.

(2013) aimed to use in-memory What-If analysis

using a query system to introduce new values. They

aimed essentially to maintain intact the real data cube,

not changing it, by introducing new values for

dimensions and measures and storing them as

scenarios. More recently, Hartmann et al., (2018)

focuses on predictive analytics, also known as What-

If analysis. They focused mainly in extract temporal

models from current and past historical facts with the

intention of creating predictions of the future. Their

intent was to solve the problems inherent to predictive

analytics, like the complexity and the diversity of the

data models, using novel data model to support large-

scale What-If analysis on time-evolving graphs,

called Many-World Graph.

3 THE METHODOLOGY

3.1 Conventional What-If Analysis

What-If analysis allows for simulating changes in

historical data, creating hypothetical scenarios and

helping to predict the future. To do this, we altered

the data in order to assess the effects of the changes.

The user is accountable to change the value of one or

more business variables and set the scenario

parameters in a specific scenario, taking into

consideration the analysis goals. The What-If process

then calculates the effect of the impact of the change

of the business variables, presenting the user a new

changed scenario, called the prediction scenario

(Figure 1). It is the responsibility of the user to accept

or recalculate the obtained scenario (Golfarelli et al.,

2006).

Figure 1: Historical and prediction scenarios.

This process provides several advantages to the user.

It makes possible to study the behavior of a system

without building it or creating the circumstances to

make it happen in a real-world system, clearly saving

time and reducing costs. Another advantage is that it

becomes possible to modify business variables in

order to find an unexpected behavior of the system.

With this, the business manager can be aware of the

conditions that lead to an erratic behavior and avoid

them in the future. Usually a What-If analysis starts

with the definition of a What-If question, for

example, “What if …?”. The What-If question

represents a question that denotes the intent on

exploring the consequences of changes on business-

related variables, in other words, what will happen if

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a user changes values of a set of variables. The What-

If analysis allows the user to get the required

information to answer the What-If question.

Figure 2: A general overview of a What-If analysis process.

A simulation model (Figure 2) is the focus of a What-

If application. Commonly, this model is a

representation of a real business model and usually is

composed into several application scenarios. Each

scenario considers set of scenario settings. These

scenario settings are composed by a set of business

variables (the source variables) and a set of setting

parameters (scenario parameters). It is the user

responsibility to delineate the axis of analysis, the set

of values for analyzing, and the set of values to

change according to the goals defined previously.

Then, the What-If process is performed with an

appropriate tool and the outcome would be a

prediction scenario. The What-If analysis tool

calculates and lets the user to explore and analyze the

impact of the changes in the setting values of the

entire application scenario. The user is responsible to

accept the new data cube, or to return to change the

settings of the application scenario and make the

changes required over to the target data. As already

referred, the lack of expertise of the user can be an

obstacle during the What-If analysis process design

and implementation. If a user is not aware of the

process or the business, may not choose the most

correct parameters in a particular application

scenario, and the outcome provided may not be the

most adequate. Therefore, we developed a

hybridization process, which will help to overcome

the lack of user expertise.

3.2 The Hybridization Methodology

The methodology of integrating OLAP preferences in

What-If Analysis is a general methodology and can

be implemented through various methods and using

different technologies and tools, such as the tool to

perform the simulation, or the way preferences are

extracted. Clearly other choices of tools and

techniques are also possible.

After the overview of our hybridization process,

we define a methodology that should be followed

when dealing with What-If-based problems.

Figure 3: The schema of the methodology for the

hybridization process.

Our methodology incorporates six distinct phases

(Figure 3), namely:

1) Problem Analysis and Definition of the What-If

question. It starts when a doubt arises, the user

needs to define a What-If question. A what-if

question translates in a question about what can

happen in a specific hypothetical scenario and

the consequences of changing variables. In this

phase, it also needed to define the goal of

analysis and the set of business variables to add

in the simulation. It is necessary to know the

context of the problem to create the What-If

question.

2) Business and Data analysis. The user needs to

perform an analysis of the business and data.

One should know the set of business variables

and associations to be included in the simulation

model, identifying the dependent and

independent ones. The relevant data sources

need to be analyzed to understand which set of

data needs to be added to the simulation. One

should take into consideration the quality of the

data: if the simulation data has noise, the

outcome of the simulation could not be the most

adequate.

3) Multidimensional Modelling. In this phase, the

data structure is prepared to extract preferences.

The multidimensional structure is constructed

based on the information collected in the

previous phase and the goal analysis defined in

the first phase.

4) Extraction of Preferences. A mining technique is

applied to the created multidimensional

structure; this process is called OLAP mining

(Han, 1997). The outcome is stored in a mining

structure. Then, a filter process, which is

explained in detail in Carvalho and Belo (2016),

is applied to the outcome of the association rules

technique. This filter process consists in filtering

the data that is interesting to the user and should

be included in the simulation. To do this, it is

necessary to filter the set of association rules and

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return only the set of strong association rules that

contain the goal analysis business variable. At

the end, this process suggests to the user a set of

variables, which are strongly related to the goal

analysis, to introduce in the simulation model.

5) What-If Analysis simulation. In this phase, the

user performs the What-If simulation. To

perform the simulation the user needs to

introduce some scenario settings: source or

business variables and scenario parameters. The

set of business variables includes the goal

analysis business variable (the focus of the

analysis defined in the first phase) and a set of

suggested preferences. The set of scenario

parameters, as seen before, depends on the tool.

The set of scenario parameters that are

introduced according to the chosen tool, like the

algorithm and additional parameters.

6) Validation and Implementation of the decisions.

In the last phase, the user evaluates how credible

and practicable is the simulation model created.

The user needs to compare the results of the

simulation model with the real business model

outcome and to evaluate if the behavior of the

simulation model is adequate. If the simulation

outcome is irregular or unacceptable, the user

needs to go back and to redefine the simulation

model.

For implementing our methodology, in the third

phase we used Microsoft Visual Studio 2017 to create

the multidimensional structure within an Analysis

Services Multidimensional and Data Mining project.

Next, and using the same tool, we used a data mining

technique for extracting preferences, opting for an

Apriori-based algorithm (Agrawal and Srikant, 1994)

for extracting preferences from the multidimensional

structure in the fourth phase. We claim that this

algorithm is the most adequate mining technique to

extract preferences from the multidimensional

structure. The extracted preferences recommend to

the user axis of analysis that are strongly related to the

previously defined goal in the what-if question.

Preferences consist on information (patterns or

knowledge) from previous sessions of analysis

derived from the application of a data mining

algorithm. They provide access to relevant

information and eliminate irrelevant one. Therefore,

preferences help to introduce valuable information to

the scenario analysis, which otherwise may not

happen. To perform the What-If simulation in the

hybridization methodology we chose Microsoft

Office Excel functions.

4 A CASE EXAMPLE

For receiving and support the application of the

methodology we proposed, we designed and

implemented a specific software platform, which we

named as “OPWIF” - OLAP Preferences What-IF

analysis integration. To illustrate our hybridization

methodology, we selected a simple case study, from

the Wide World Importers (WWI) (SQL Server Blog,

2016) data warehouse. The creation and analysis of

the small data cube can clearly be generalized to

larger complex cases. The WWI database contains

information about a fictitious company, which is a

wholesale novelty goods importer and distributor.

4.1 Data and Goal Analysis

The database schema of the case study “Sales” is

presented in Figure

. It contains a fact Table “Sale”

and all the related dimension tables, namely:

“Customer”, “Employee”, “Stock Item”, “City” and

“Date”, each one containing the information about

customers, employees, stock items’, about cities of 49

states of EUA and date details between January 1,

2013 and December 31, 2016, respectively.

Figure 4: Selected data warehouse's view "Sales" schema.

The analysis example selected was one want to use

What-If analysis to explore the effects of increasing

the sales profit values by 10% of the profitable

products of a specific store. Considering this scenario

context, we formulate the following What-If

question: “What if we want to increase the sales profit

The Added Value of OLAP Preferences in What-If Applications

175

by 10% focusing mainly on the most profitable

products’ color?”.

Next, we need to define the goal analysis and a

set of business variables to add to the analysis

scenario. The goal analysis is “color”, because the

analyst wants to know how the profit values may vary

according to the products’ color. The set of variables

to be added to the scenario would be “sales profit”,

because it is the attribute that we aim at altering

(increasing 10%), and also it would be useful and

interesting to analyze the scenario data by year or

month.

4.2 Applying Conventional What-If

Analysis

The developed software platform allows for

performing conventional What-If analysis. Figure 5

represents the application UI of this tab, the WIF tab.

Figure 5: Overview of the software platform UI - WIF tab.

Using this tab, the user can create a typical What-If

scenario using the conventional What-If analysis. The

user chooses the parameters that he wants to

introduce in the scenario (according to the pre-

defined What-If question) and creates the graphic to

analyze the profit values. The set of parameters to be

chosen are: “Calendar Year” and “Calendar Month”

from the Dimension “Invoice Date” and “Color” from

the Dimension “Stock Item”, as we want to know

which is the most profitable products’ color. We opt

to choose “Calendar Year” and “Calendar Month to

analyze the scenario data by month. Then, after ‘See

Graphic’, the application shows the Historical

Scenario. As we want to analyze the effects of

changing the profit value by 10%, we set the new

value in the “New profit value”. After performing the

What-If analysis, the application returns the

Prediction Scenario (Figure 6).

Figure 6: Conventional What-If Analysis – The prediction

scenario.

4.3 The Hybridization Process

To support the proposed methodology, we developed

the HybridizationModel tab (Figure 7).

Figure 7: Overview of the application UI -

HybridizationModel tab.

The hybridization methodology is possible to be

followed using this tab. In Step 1, the user specifies

the minimum values support and probability to filter

the frequent itemsets and chooses the mining

structure according to the What-If question. For

example, if the user is focus on the sales profit of the

most profitable products’ color, he must choose the

‘Products’ mining structure. In step 2 the user chooses

the frequent itemset of its choice (according to the

What-If question), in this case, the ‘Color’ goal

analysis attribute. Finally, in step 3, and similarly to

step 1, the user specifies the mining values of support

and probability to filter the important set of

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association rules. These association rules are an

association rules’ subset that contain the chosen goal

analysis attribute in step 2. These steps are resumed

in Figure 8.

Figure 8: HybridizationModel tab – steps 1, 2 and 3

complete.

After filtering the association rules with default

minimum support and probability values in step 3, the

application UI shows the window, represented by

Figure 9, containing the final association rules’ list

ordered by probability of happening in the left. The

three chosen rules are the association rules in the

right:

1. [“Brand” = ‘Northwind’, “Color” = ‘Black’ ->

“Barcode” = ‘N/A’];

2. [“Brand” = ‘Northwind’, “Color” = ‘Black’ ->

“Buying Package” = ‘Each’];

3. [“Brand” = ‘Northwind’, “Color” = ‘Black’ -> “Is

Chiller Stock” = ‘Missing’].

These three top rules are chosen to form the OLAP

preferences.

Figure 9: Selection of top association rules.

Next, the item sets contained in the filtered

association rules will be suggested to the user as

preferences, as shown in Figure 10. The user choses

the scenario parameters to be part of the What-If

scenario. The preferences are the itemsets of the

chosen association rules “Brand”, “Barcode”,

“Buying Package” and “Is Chiller Stock” in the left.

“Calendar Year” and “Month Number of Year” are

suggested too to be part of the scenario.

Figure 10: Recommendations to the user.

Then, the application UI creates a historical scenario

with the chosen parameters and shows it to the user.

Finally, the application UI shows a new window, in

which the user can enter the desired final value. This

step is similar to the one in the conventional What-If

analysis, in which the user changes the value of the

goal analysis variable to the wanted one. In order

words, if the user wants to increase the profit value by

10%, we want to alter the profit final value by 10%.

Then, the application performs What-If analysis and

returns the new prediction scenario, represented by

Figure 11.

Figure 11: Hybridization process - Prediction scenario.

4.4 Comparative Analysis

Now, it is time to compare the results of both

approaches shown, the outcome of the application of

the conventional What-If analysis (Figure 6) and the

outcome of the application of our proposed

hybridization process (Figure 11). In both graphics,

it is possible to analyze the attributes “Profit”,

represented by the Y axis, with a range from ‘-200

000’ to ‘1 600 000’, and “Calendar Year” (‘2016’)

and “Month Number of Year”, represented by the X

axis, with a range of ‘1’ to ‘5’, representing the

months of a year, from ‘January’ to ‘May’; and

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177

“Color” which can be ‘Black’, ‘Red’, ‘Gray’,

‘Yellow’, ‘Blue’, ‘White’, ‘Light Brown’ and ‘N/A’

(not available).

Using the conventional What-If analysis

process, the graphic of the Prediction scenario (Figure

6) shows that in ‘2016’, products with ‘N/A’ color are

the most profitable and it is the product color that earn

more money, especially in ‘May’, ‘March’ and

‘January’, respectively; showing profit vales over

than ‘1,200,000’. Followed by the products’ color

‘Blue’, which is the most profitable in ‘March’,

‘April’ and ‘May’; and finally, the products’ color

‘Black’ is more profitable in the same months that the

products’ color ‘Blue’. ‘Light Brown’ is the products’

color less profitable, also with negative values in

‘2016’.

Now, we consider the outcome of the application

of our hybridization process. When we analyze the

prediction scenario (Figure 11), it is possible to verify

that products with Light Brown shows negative

profit. But this fact is not news, as we had already

concluded this fact by analyzing the outcome of the

conventional What-If analysis. The novelty using our

hybridization process is the suggestion of the "Buying

Package" parameter (Figure 10). With the addition of

this new parameter it is possible to conclude more

facts beyond what we previously conclude with the

conventional What-If analysis.

Products with unknown color or (‘N/A’) with

‘Each type’ buying packages are the most profitable

products (with profit values over ‘1,000,000’ in

‘January’, ‘March’ and ‘May’), comparing to (‘N/A’)

color products with ‘Carton’ and ‘Packet’ (less than

‘120,000’), which are less profitable.

Products’ color ‘Blue’ are the second most

profitable, especially in ‘May’, ‘April’ and ‘March’

(over ‘500,000’). The novelty here is the fact that the

most profitable ‘Blue’ products were sold with ‘Each

type’ buying packages.

The products’ color ‘Black’, similar to the

previous analysis, are the third most profitable

products in ‘May’, ‘April’ and ‘March’ (over

‘250,000’). The novelty here is that the most

profitable ‘Black’ products were sold with ‘Carton’

buying packages. Apart from these cases, products

that are sold in ‘Carton’ and ‘Packet’ (regardless of

Color) generally have low profit values (less than

‘150,000’).

Thus, we can conclude that regardless the color,

the buying package (‘Carton’ or ‘Packet’) influence

the negatively the profit. Finally, and already known

fact is that ‘Light Brown’ products have negative

profit values. The new information that we can

include in this last fact is that ‘Light Brown’ products

have buying package made from ‘Carton’.

Comparing the outcomes of both approaches, we can

conclude that when using the hybridization process,

we get more refined and detailed results, leading to

decisions that are more accurate. For example, in the

conventional What-If analysis, the most profitable

products’ color was ‘N/A’; on the other hand, in the

hybridization process, the most profitable products’

color was also ‘N/A’, but we learned that the most

profitable products with (‘N/A’) color were sold with

‘Each type’ buying packages. Moreover, products

with the buying package (‘Carton’ or ‘Packet’) and

regardless the color influence negatively the profit.

The second most profitable products’ color, in

the conventional What-If analysis approach, was

‘Blue’. In the hybridization process, by analyzing the

scenarios, we conclude that the most profitable ‘Blue’

products were sold with ‘Each type’ buying packages

(as in the ‘N/A’ colored products). Another fact that

we conclude using the hybridization process is that

‘Blue’ products sold in ‘Carton’ and ‘Packet’ buying

packages do not show any profit values.

Finally, in the conventional What-If analysis,

‘Black’ was the third most profitable products’ color.

In the hybridization process, we conclude that the

most profitable ‘Black’ products were sold with

‘Carton’ buying packages. This information is hidden

by the conventional What-If outcome analysis.

The presented case example analysis represents

a small case study. However, it demonstrates the

potential of the methodology, which helps up to be

helpful when dealing with cases that are more

complicated. With this methodology, we can add new

relevant information to the analysis.

5 CONCLUSIONS

In this paper, we presented a solution approach for

enhancing the conventional What-If analysis process

using OLAP usage preferences, a hybridization

methodology and its application. This methodology

process helps the user by suggesting new axes of

analysis to the What-If analysis scenario. These new

axes of analysis are discovered through OLAP mining

and cannot otherwise be discovered using a manual

analysis. At the end, this integration helps the user by

adding new relevant information to the What-If

scenario. This contributes significantly to enrich a

make more valuable a What-If scenario for a

particular business domain.

The main difference between our approach and

a conventional What-If analysis method is then to

become possible to simulate a system behaviour

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based on past data extracted from OLAP sessions, in

other words, our approach contains the process of

extraction of usage preferences using association

rules. Preferences can be defined based on historical

data provided from a data mining system. Preferences

can recommend to the user the axes of analysis that

are strongly related to each other, helping to introduce

valuable information in the application scenario being

building.

Following this methodology, the user experience

is eased. The choice of the scenario parameters is one

of the phases that may be quite difficult to a user that

is not familiar with the business data. A user that is

not familiar with the data, may choose the wrong or

inadequate scenario parameters. Instead of making

the wrong choices or choosing only the scenario

parameters included in the What-If question, our

process finds and recommends the set of strongly

related to the goal analysis attributes to the user. Thus,

it is possible to the user to add relevant and important

information to the scenario, which in a default or

usual situation would not be done.

Nevertheless, there we also recognized some

limitations that need to be overcome, in order to make

the system more efficient, especially at the level of

the usage of Microsoft Office Excel functions and

within the What-If process itself. Additionally, we

need to free the system from some limitations

imposed by user’s choices done in the most parts of

the What-If process. This is must be avoided, because

a user that has limited knowledge about the business

domain or even about the simulation process to be

implemented influences the entire process negatively,

leading consequently to poor results.

Despite the several advantages of using the

hybridization methodology, there are some

drawbacks related to this process. In a first stage of

the What-If process, if the goal analysis is not done

correctly, What-If questions and scenarios will be not

correctly defined, or the preferences outcome will be

not reliable. Thereafter, performed What-If queries

will be not the most suitable process and thus the

obtained prediction will be different of what is

expected as a normal behavior of a real business

system. One way of avoiding this is to study potential

and alternative application scenarios, in order to take

the best advantages of the What-If scenario analysis

tool. Finally, the What-If Analysis results depend

strongly from the data we want to analyze. If it

contains some errors, which is a very common

situation, the result will not be very useful. In order to

overcome this kind of drawbacks, we mainly aim at

restructuring automatically the What-If scenarios,

discarding the user’s dependency and finding a way

of overcoming the limitation we found in some Excel

functions.

ACKNOWLEDGMENTS

This work has been supported by national funds

through FCT – Fundação para a Ciência e Tecnologia

within the Project Scope: UID/CEC/00319/2019.

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