Jonice2008_RedeSocialParaPesquisadores

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The Use of Visualization for Analysis and Recommendation onPeople Replacement on Virtual Communities and Teams in the

Brazilian Scientific Scenario

Diogo Krejci1, Jonice Oliveira2, Jano M. de Souza11COPPE-PESC/UFRJ - Graduate School and Research in Engineering (COPPE) -Department of Computer and Systems Engineering (PESC) / Federal University ofRio of Janeiro (UFRJ)PO Box 68.511 ZIP 21.941-972 - Rio de Janeiro Brazil

2UERJ-IME/DICC - State of Rio de Janeiro University (UERJ) - Institute ofMathematics and Statistics (IME)-Informatic and Computer Science

Department(DICC)[email protected], [email protected], [email protected]

Abstract

Due to the lack of resources and the low number of official examinations, turnover is high

in the scientific environment. The search for an appropriated substitute researcher to be hired

or to moderate communities involves the analysis of a large amount of information.

Consequently, we base our choices on the recommendation of others. To solve this problem,

this approach indicates professionals through the support of Information Visualization

techniques that assess competences and the working contexts of the researcher that is leaving

a post. The visualization usage streamlines the replacement process as it provides a clear and

concise representation of parameter values used during the assessment of applicants. This

approach is part of the Scientific Knowledge Management Project.

1. Introduction

The lack of resources and the low number of official examinations generate high turnover

in the scientific environment. Consequently, some researchers remain little time with theirresearch projects as they are forced to enter the job market or change the institutions that are

their workplaces. Thus, institutions must look inside their own environments for capable

research staff to supply this need. However, discovering such a person means that a large

amount of information has to be examined. This information overload demands considerable

time to process or leads to choices based on the recommendation of others, which can go

awry. So, it is necessary to use some strategy to organize and represent this information.

Thus, problems caused by the flood of information are minimized and choices are optimized

as a function of time and features that map the project, group or community and the

professional that is to be replaced.

GCC is an environment for knowledge management in a scientific scenario [1]. Despite the

GCC stores all the information necessary to search for a substitute research staff member, thesearch available in the system as on other systems - is not effective to find a substitute as it

requires the assessment of several comparative and timeline reports. To gather the full

information in a single report would not solve the problem, as a great cognitive endeavour

would be needed to assess the criteria and the decision variables at once, where the risks of

inaccurate decisions would continue to exist.

The research area called Information Visualization studies how the use of computers, in the

creation of iterative and visual artifacts, can facilitate the process of external cognition.

According to Card, et al. [2], Information Visualization is the use of visual representation,

Simpsio Brasileiro de Sistemas Colaborativos

978-0-7695-3500-5/08 $25.00 2008 IEEE

DOI 10.1109/SBSC.2008.33

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interactive and supported by computers, of abstract data to improve cognition. The techniques

and concepts of Information Visualization seek to optimize the use of human visual abilities,

trying to provide the user with brief explanatory views on an abstract phenomenon for which

there is not inherent spatial visual representation [3] - and this is case of most of the data

related to the search for a replacement.

This paper describes the BEE (BEE as the Portuguese acronym for Busca de Especialistapor Exemplo, Search of Expert by Example) approach. The purpose of this computational

tool is to support the replacement of research staff, on an organization, community, group or

team. Therefore, BEE identifies the areas that are to be most significantly affected after the

departure of the member of staff and recommends replacements based on the similarity

amongst ones own competences and the ones of the replaced person, where additional

information from the MBTI (Myers-Briggs Type Indicator) profile [4] and learning capacity

also can be considered. The BEE tool applies Visualization Information techniques to assist

the replacement process described above. These techniques provide a clear and concise

representation of the parameter values that the user considered as the most important to be

used during candidate assessment.

The rest of the paper is organized as follows. Section 2 gives a short description of the

GCC environment. Section 3 presents the work methodology and the tool functionalities.

Section 4 compares the methodology of this work and describes its benefits. Finally, section 5

provides some conclusions and further work suggested.

2. The GCC architecture

Collaboration in scientific environments is usually restricted, happening amongst a small

number of people who act in the same group, treating or researching more specific items of

their domain. Outside these groups, the exchange of information is based on paper (through

articles, theses, technical reports, and others) and with physical presence events (classes,

conferences, etc). Consequently, many researchers who hold correlated works do not get to

know each other [5].

GCC is an environment for knowledge management in a scientific scenario. This

environment allows data, information and knowledge centralization within the research

environment, facilitating the sharing and dissemination of the knowledge generated [1]. In

addition, the GCC provides infrastructure for the creation and maintenance of virtual research

communities and project management efforts, stimulating the development of new ideas,

work and collaboration among researchers [1].

However, the search by researchers in the GCC as in several knowledge management

systems - is made in a textual way and its results are returned in the form of reports. When the

proposal is to find a substitute this method becomes inappropriate, due to the inability to

display all relevant information (competences, areas of interest, publication and project

history, communities, etc.) in a single report, the user needs to repeatedly execute textual

searches, which demand great cognitive endeavour, needed to analyze all criteria and decisionvariables, with the continuation of the risks of inaccurate decisions.

3. BEE: search for expert by example

The location of suitable people to replace an expert, for the execution of a task or the

filling of a post is a frequent problem in large organizations [6]. The same problem happens

in the scientific environment. To find an appropriated substitute for research staff is not a

trivial task as it involves the analysis of a large amount of data on the parties: ones work

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contexts (projects, communities), related items of scientific production (as publications,

advising works, projects, patents, and other) and interaction (materials, contributions, forums

participations, etc...), competences and personal characteristics. This information overload

demands much time in processing large volumes of data or the making of a choice based on

the recommendation of others, so that in most cases the researcher chosen is not the most

appropriate replacement.

In our approach, the search method consists of three steps: loading the data,

determining which area the researcher is critical for, and searching for substitutes.

These steps are looked into in the following sections. In this paper, "context" is the set

of communities, projects and social capital of a researcher, while the "area" represents

an element of this collection.

3.1. Loading the data

For the correct functioning of the tool, some items need to be specified in the possible

contexts of the GCC. Each system researcher will have filled a profile form, describing ones

own competences, and answered queries on preferences and interests, and personal data.

While in the profiling stage the user performs a MBTI [4] test to identify ones psychological

profile. In the community context we have the competences required by the community and

the news, surveys, events, links, materials and forums, which result from the members

interactions in the community. In the project context we have the required competences of the

applicant, the resulting publications from scientific work engaged into, contributions (ideas,

suggestions, questions and complaints), materials and activities a participant executes in the

project. All this information will be considered by BEE during the execution of steps 2 and 3

of the search methodology.

Amongst the items mentioned above, the competence element deserves more attention.

The competences related to community and project contexts or any other item have their own

importance defined by a score ranging from 1 to 10 by a moderator or leader, respectively.

The competences related to the researcher are inferred using some information. The first

information is defined by the researcher, who indicates his/her competences and its degree ofknowledge (high, medium or low, rated 3, 2 and 1, respectively). The score of these

competences is entered manually by a researcher, and is recorded directly by the researcher

into the GCC. We use the researcher's Lattes curriculum to detect competences from his/her

professional life. So, information on the academic background, technical production,

bibliographical production, completed thesis advising, project participation, and participation

in thesis presentation boards are used in the competence extraction from the researcher's

Lattes curriculum. The titles of these works are mined, using the S-Miner [7] module of the

GCC, and as a result some issues are identified, related to the researcher activities. This tool

applies mining techniques to extract the radicals of relevant words [8], which are then linked

to their competences. The relevance of each competence is determined by the number and

creation time of scientific productions related to it. Based on the returned result, a rating that

can be high, medium or low is suggested for the user. Other information that we used to infer

competences are topics which a researcher usually interacts (in communities) or competences

which are pre-requisites on activities that a researcher successfully concluded.

3.2. Determining which area the researcher is critical for

The goal of this step is providing a visualization that allows the user to discover all areas

(communities, projects and organization) where the researcher to be replaced is inserted and

ones relevance for them. Thus, the user will know which projects and communities should be

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the most affected after the researchers departure, and ones real importance in the institution.

In addition, this step works as an information filter, allowing that in the next step be analyzed

only the competences of the selected context and researcher.

In the GCC, there are three possible navigation contexts for the tool (projects",

"community" and "organization") that are organized in a hierarchical structure. As the user

navigates down a context hierarchy, information is presented in more detail. In level 1 of thehierarchy, an overview of the system projects, communities and participants is shown. Level

2 (projects, community and participants) shows the areas of the selected context; for example,

if the user chose the context "projects", all projects of the substitute researcher will be

revealed. Finally, on level 3 participants are shown in the selected item context; for example,

all participants of the Gesto do Conhecimento (Knowledge Management) project.

So, different visualizations are used to analyze different facets of researcher profile on a

specific context. The first visualization technique which is used is TreeMap.

The TreeMap technique is used to view the critical areas. This visualization allows the

exhibition of the large hierarchical information groups through methods of space completion

[9]. The TreeMap of this work can be built using 4 algorithms: Slice and Dice [9], Squarified

TreeMap [10], Ordered TreeMap [11] and Cluster TreeMap [12]. Figure 1 illustratesTreeMap, deep hierarchical 3, created to provide a view of the areas in which the researcher

to be replaced is critical for the Scientific Knowledge Management project.

Figure 1. TreeMap for Jonice Oliveira in the Gesto doConhecimento (Knowledge Management) area.

The left tree contains the areas and contexts the researcher being replaced belongs to, andall researchers from institutions registered with the GCC. The navigation through these areas

allows the user to define which of these will be affected the most effect after the departure of

the research staff member. TreeMap rectangles represent researchers who participate in an

area of any context. Positioning the cursor over the rectangle displays a list containing the

item quantities and the researchers importance in the context.

Rectangle significance (colour and size) change through the users navigation context;

colour represents the researchers relevance and size indicates the degree of participation.

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Therefore, the researchers importance is represented by the size and colour of ones rectangle

in a context. Considering the TreeMap above, note that the Knowledge Management (in

Figure 1, Gesto do Conhecimento) community would be greatly affected after the

departure of the "Jonice Oliveira" researcher.

This importance is calculated from an item set that depends on the context. The

"communities" item considers the intersection between competences required by thecommunity and the participants competences, materials, surveys, links, events, news and

forums participations. In the context, "projects" is considered to be the intersection between

competences required for a project and the participants competences, materials, tasks,

contributions and publications. Finally, the "participants" context item uses information from

the previous contexts to determine the importance of the researcher in the institution and the

number of relationships of each participant (social network).

Rectangle colour is set by the common competences between the participant and the area

context. See Equation 1, where RC (x, y) represents the importance of researcher "x" in area

y. CR(x, y) is the competence value of researcher "x" which is similar to the competence of

area "y". While CS (y, x) is the competence value of area "y", on the specific context, that is

similar to the competence of researcher "x". For example, suppose a researcher "A" has

competences c1=3(high), c2=1(low), c3=3(high), c4=2(medium) and c5=1(low), and area "B"

has competences c2=8, c3=6 and c6=9. So, RC (A, B) = 1x8 + 3x6 + 0x9 =26.

The area size related to a researcher is set by the sum of the provided items for someone in

ones context; see Equation 2. Where RS (x, y) is the value of the rectangle area value related

to researcher x in area y. While Item (x, y, z) represents the total of elements in "z"

related to "x" in "y". For example, suppose researcher A has 10 tasks, 20 publications, and

15 materials in project "B". So, RS (A", "B") = 10 + 20 + 15 = 45.

n

Z

zyxyxRS1

)),,((Item),( (Equation 2)

The TreeMap allows the identification, in a simple and faster way, of areas that will be

most affected by the researchers departure. However, this visualization lacks the perception

of a knowledge "gap" among researchers and their ability to learn. To counter this limitation,

line, polar charts were used. This kind of visualization generates a space perception of

competences, facilitating the identification of the knowledge "gap" and the ability to learn

amongst researchers.

3.3. Searching for substitutes

The goal of this step is to point the user the substitute candidate. For this, the competences

of selected context are used and if necessary MBTI profile, learning ability and level of

researchers interest also can be used. Figure 2 illustrates the screen for consultation.

)),(*),((),( xyCSyxCRyxRC (Equation 1)

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Figure 2. Screen for consultation of Searching Substitute step.

3.3.1. Polar and line visualization: Polar or line visualizations may be used in the

identification of such similarities, which are represented in the graph as the difference

between the areas corresponding to the candidates and the substitute's degree of competence.

Only the relevant competences in the selected context will be used in this comparison. Thefollowing items will be used to generate these visualizations: (1) Context competences:

represents the competences of the selected area that can be used to determine the relevant

semantics. Only researchers who have all the competences selected in the consultation part of

the comparison; (2) Only the relevant: determines if all researcher competences or only those

relevant to the context are shown in the visualization. This option allows irrelevant

competences in the context which, although needed for the comparison, are to be used in the

similarity analysis; (3) Visualization types: defines the type of visualization, line or polar

charts; (4) Weight: sets the weight (relevance) that items referring to the executed activities of

the context (i.e., project experiences, publications on the subject, orientations, etc) will have

on the valuation of the competence degree, ranging from 1 to 10. It is important to point that

the set of items used in weighing varies from one context to another.

The importance of the researcher competences is ones area is set by equation 3, where CI

(x, y, z) is the importance of competence "x" of participant "z" in area "y". CS (x, y, z) is the

value of "x" (1-10) in "y", multiplied by the value of "x" (1-3) in "z". So the importance given

to the associated value will be greater than that assigned to the items. While Item (x, y, z, w)

represents the total of elements of type "w" that "z" related to the "x" in "y". Finally, W (w) is

the weight that item "w" will have in the calculation of the degree of "x", which varies from 1

to 10. For example, let us suppose researcher "A" has 10 tasks, 20 publications and 15

materials in project "B", and that competence "c1" holds high importance (value 3) in "A" and

6 in B. Additionally, the user assigned weight 8 to the item "publications". Therefore, CI

(c1, B, A) = (6 x 3) + (10 x 0.1x 1) + (20 x 0.1x 8) + (15 x 0.1 x 1) = 18 + 1 + 16 + 1.5

= 36.5.

n

w

wWwzyxzyxCI1

))(),,,((Itemz)y,CS(x,),,( (Equation 3)

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Figure 3. Line Visualization.

Figure 3 and Figure 4 show an example of line and polar visualization. Both are made by

two parts. The upper part compares the competences of the researcher that is to be replaced

and the (each) possible substitutes in the selected context. The same type of comparison is

shown in the bottom part but all candidates are displayed on the same screen simultaneously.

If the area under examination has more than five researchers, only the five most similar are

displayed in the visualization to avoid confusion. In this example (Figure 3 and Figure 4), we

have 3 candidates to substitute the researcher Jonice on the project entitled Scientific

Knowledge Management.

Figure 4. Polar Visualization.

Red: Competence

Degree as

re uired b the

Other Colours: Competence

Degree of likely substitutesBlue: Competence Degreeof the person who is toleave thecommunity

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3.3.2. Learning flow visualization of competence and interest: In this paper, learning

flow refers to the farthest point reached by a person in learning about a particular subject

(competence or interest) over a period of time. In the GCC, competence is an subject

dominated by the researcher, something one has already researched or worked with, whereas

interest represents something a researcher wants to learn. As a researcher advances ones

learning on a particular interest, it may turn into a competence.

The purpose of "Learning Flow Visualization of Competence" helps the user to discover

how a researcher learned or used a competence, over a certain length of time. Sometimes,

during the calculation of similarity amongst researchers competences, subjects can display

the same level on a given competence. However, when considering the temporal aspect that

level can vary. The temporal aspect allows the user to know which researchers worked most

recently with a competence and it can be decisive to identify a suitable person. The same

considerations made for competences apply to interest. The difference is that interest

visualization is used when no researcher has the competence being sought by the user.

In the learning degree identification we use the frequency with which the person interacted

on a subject of their interest. This interaction can be done in an electronic meeting, forum

access and posting of material, sending of e-mails and what one wrote in a personal blog.

Figure 5 shows an example of the learning flow visualization of competence. Each line

represents a researcher, where the name of the competence to be examined is on the title of

the graph generated and the knowledge level in a competence is given in the column "Total".

The number of graphs generated is equal to the number of competences analyzed, while the

value of the competence is the amount of items relating to it in the consideration context.

Figure 5. Learning Flow Chart for the Knowledge ManagementCompetence.

3.3.3. Visualization of MBTI (Myers-Briggs Type Indicator) similarity:MBTI [4] is an

instrument for measuring a persons preferences, using four basic scales with opposite poles.

The four scales are: (1) extraversion/introversion; (2) sensate/intuitive; (3) thinking/feeling;

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and (4) judging/perceiving. The various combinations of these preferences produce 16

personality types [13] [4]. Types are typically denoted by four letters; for example, INTJ

(Introversion, Intuition with Thinking and Judging), to represent ones tendencies on the four

scales. A profile for each of the sixteen types has been developed. Each profile consists of a

list of characteristics frequently associated with ones type. In the GCC, the persons

preferences are determined by responses to an on-line questionnaire.

The purpose of the MBTI visualization is differentiated, as it takes into account the

characteristics of the researchers psychological profile and not ones competence. Hence its

use on two points: (1) When the visualization (line and polar) for the analysis of competence

similarities indicates more than a substitute, the MBTI visualization can be used as a

resolution criterion; and (2) When the persons preferences are more important than ones

own competences, only the MBTI visualization can be used to indicate an appropriated

substitute for a research staff member.

Figure 6 shows the visualization of MBTI similarity which is set by the number of the

equivalent scale. At the centre of the visualization is the researcher to be replaced, and the

distance between the image decreases as the similarity among the profiles. The user can

define two types of search: "Referenced Type" and "My Criteria." In the first, the

characteristics of the MBTI profile of the researcher being replaced are used as a parameter

for the search, while the characteristics of the MBTI profile are manually defined by the user

in the second one. Positioning the cursor over the balloon icon displays a description of each

item.

Figure 6. Visualization of MBTI Similarity.

4. Related work

According to Reichling, T. et al. [14], a recommendation system for specialists (SRSs)

returns references to human actors who are identified as specialists in the required domain.

Thus, we can classify this work as being a SRS, given it recommends replacement researchers

based on the similarity amongst ones own competences and the ones of the replaced person.

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There are several SRSs developed, such as HALe [15], Navigator [16], and Tabuna [14],

amongst others. They use various profile building techniques, recommendation approaches

and criteria for determining whether a person is an expert and in what area of knowledge. But

none of these systems considers the importance of visualization during the identification of

the specialist. We believe that a SRS using techniques of information visualization can be

more efficient, as it aggregates relevant information into visual forms and provides

mechanisms that enable the operation of that set of information, allowing morerecommendations directed to the needs of the user. This section presents some jobs that are

used in the recommendation of experts. In this work, researchers are recommended based on

their similar competences and other characteristics described on previous chapter. This

approach filters information of a context, avoiding the analysis of information that is not

relevant to the context. Another advantage of this work is the use of visualization techniques

which, through their operation, increase the perception of all information returned, facilitating

its analysis. There are several works on visualization techniques, but the majority of them do

not use different visualization techniques to analyze different facet of a profile. Fewer enable

dynamic interaction.

To examine many attributes related to the scientific scenario in order to identify

similarities amongst researchers and their scientific productions, the recommendation ofpossible communities would not be practical. It is in this sense that visualization techniques

contribute to transmit, in a single screen, and aggregate information that can be easily

interpreted by the user. Thus, this work overcomes a limitation found in most expert

recommendation systems that is the static way in which the result is displayed [17].

This work also allows the change of the indicators relevance, as used in the comparison of

similar researchers, and this allows customizing the search according to ones own interests

such as, for example, the search for researchers who worked in projects that use the

competences sought, or with a large number of publications. On Brazilian Scientific

Scenario, we do not have SRSs using visualization techniques.

5. Conclusions and future workThis paper presented an approach, implemented through the BEE computational tool for

researchers looking for substitutes on scientific scenario. The GCC infrastructure and its main

features were also discussed. Moreover, concepts were presented on Information

Visualization and the benefits of its use.

The use of visualizations during data analysis enables information aggregation and

consequently expands cognition as it allows people to observe in detail and to detect patterns,

acquiring information that would be difficult to analyze and to understand. This point places

BEE above other SRSs (specialists' recommendation systems) that do not consider the

benefits of visualization during the final result analysis. Furthermore, BEE also allows a

better gauging of competences on the comparison of similar researchers, allowing user-

customized searches, according to ones own interests.As future work, we intended to evaluate the usability of this tool through a real-life case

study, using groups from different domains in our university, with the following expansion of

this study in different Brazilian universities. Besides, we will analyze the recall and precision

metrics, improving the accuracy, if necessary. We also intend to discuss the researcher's

agreement in being recommended. After all, for a successful recommendation, the researcher

should also agree to migrate to another area, project or community. Another possible

improvement would be to look for a way to integrate the information presented in both

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visualizations into a single visualization, enabling the immediate visualization and analysis of

all information.

Acknowledgments

We thank CAPES, CNPq and COPPETEC Foundation.

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