Provide a workplace to develop a sharable context view of an information space E Electronic

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1 The proposed system CELTIC general description The CELTIC system includes the information visualisation design and the associated means to provide the interface with each individual user and the necessary services to support user interaction, shareable data, integration with information resources, and networking. The system s name results from its characteristics of beeing collaborative, computer mediated, and providing a simple visual language - 3D space - that makes possible to use information resources (figure 4). The control of this information space is made possible by direct manipulation the system to generate search and browse clues. C Collaborative Provide a workplace to develop a sharable context view of an information space E Electronic Design to take advantage of the potential integration with available (digital) information resources L Language Translation for T Offer means to translate into visual form the otherwise written material to support the context description of the information space I Information C Control Provides a two step tool for dealing with information retrieval in form of search and browse proposals Figure 4: Justification for CELTIC system s name The system was designed to address the problem of dealing with understanding problems in an information space. This means that we do not always have the chance of getting a valid path to search or browse information in an easy or straightforward way. We mean by a valid path for getting information, the necessary clues to start browsing and even search an available information resource like a database, a Web searcher or even the World Wide Web itself, for retrieving information about a specific area (the one described by an information space). Basically, the

2 proposed information visualisation design, support users in obtaining clues on how to get information for browsing and searching tasks. It provides a high abstraction information visualisation that can be integrated with different information resources. From the resulting visualisation, the clues are presented as a group of related keywords used to feed browsing and searching tasks and allowing better query and navigation tactics. Due to its high abstration notion of an information space, the CELTIC information visualisation design deals with just general keywords, ratings and keywords clusters to describe the information space. These keywords are represented in a visual form, and takes advantage of computers to render 3D environments and interact with them. The proposed system does not intend to map the content of an existing information resource. The main objective is to provide a general cognitive working map for information resources use. The following three sections describe the CELTIC system. The first one introduces its core part: the information visualisation design. The second section describes the CELTIC system components that deal with support issues like networking and user interface. The last section presents the system s functionality, and its working description. The Information Visualisation Design CELTIC Information Visualisation Design The information visualisation design is based on the idea of a workspace where users interact by sharing an information space representation that allows direct manipulation and gives to each user some control of the ongoing result. This facility allows each user to participate in the changing of the information space state. The CELTIC information visualisation design approach is composed of two parts (figure 5). These two parts have different functionality and can be controlled by the user independently. We can summarise the role of each information visualisation design part as: x part I: context, giving a global picture of the available information space, shared and edited in collaboration by different users; x part II: focus, allowing each user to customise an information visualisation of the information space for his particular needs.

3 user C user A user B Part II: focus Different for each user Part II: focus The part of the information space that is under attention Part II: focus Shared among users Part I: Context The global picture of the available information space Figure 5: Two part information visualisation design Information visualisation part I give a global picture of the available information space, shared and edited in collaboration by different users. Part II helps generating smooth operations in the partial information space that are under attention. Smooth operations where arlier defined as blocking or facilitating the understanding. Part II also gives the opportunity to integrate some real-time statistics from information resources, and eventually extend the available information with a data window. The data windows contains the result of using system information to query information resources. The integration between the two views of the information visualisation design is accomplished by generating part II from part I values. It is always the part I definitions that are used to update part II visualisation, since it is part I that is shared among users and possesses the high abstraction data of the model. CELTIC Definitions The CELTIC information space is based on three basic elements: x information cluster, x keyword, x and keyword rating. The CELTIC information visualisation design will render a visual image of an information space described by an ordered group of these three elements. The

4 information space can be stated as a set of information clusters. By its own, each information cluster is a set of keywords. At last, each keyword has a correspondent rating value (figure 6). Information Cluster - IC K i - keywords K 1 K 3 K 2 Rating < value > K 4 K 5 Figure 6: Information cluster, keywords, and ratings At a high abstraction level we can cluster information based on agreed keywords about a problem, a knowledge area, or a process in a given context. To structure the collected information, the keywords are grouped together, based on their contribution to identify a relevant topic within the work context. We refer each keyword group as an information cluster, each one named with the concept or object it represents. Several keywords can exist in more than one information cluster, providing the means for overlapping concepts. The process in which keywords and information clusters are feed into the system is based on the contribution that each user can make to the overall context definition (part I information visualisation). Each user can participate on the keywords rating by proposing new keywords and their relative rating on a given information clusters, according to specific rules and user permissions. Information Space Set of information clusters (IC), n t 0 Information Cluster Set of keywords (K), i t 0 IC 1 IC 2... IC n K 1 1 K 2 1 K n 1 K K 1 i K 2 2 K n K 2 i K n i Figure 7: Information clusters and keywords relationship whitin an information space

5 A given information space is composed by a set of information clusters. Figure 7 shows the relationships of the CELTIC elements (for each keyword there exists a corresponding rating). The following figures 8 and 9 give the structure of the information cluster, keyword and rating elements to be used as the computer internal representation. The table structure has three columns that give, respectively, the name of field, a small description for it, and its computer representation type. The sctruture for the Information Cluster (figure 8) has eight fields. A name field, used as the identifier for each particular Information Cluster. A field named type, used to allow future addition of facilities. The field type gives the possibility to further describe with extra semantic each information cluster. The two next fields, ICNumber and DateCreation support reference information for the information cluster and its creation date. The information cluster table contain most of the data needed to construct the information visualisation, including its position in the information space given by the x, y, z co-ordinates ICPosX, ICPosY, and ICPosZ fields. The last field gives a small description of the informaion cluster. INFORMATION CLUSTER Name The name for the information cluster, String must be just one word Type For future use. Char ICNumber The code of the information cluster, to be used to refer it Integer DateCreation Timestamp for keyword creation, Date includes full date format ICPosX Value for the X space position Float ICPosY Value for the Y space position Float ICPosZ Value for the Z space position Float Description A small description of the meaning and goal for this information cluster String Figure 8: Information cluster internal representation Figure 9 shows the structure for the Keyword element, with six fields. The name field with the identifier word for the keyword. A field named type, used to allow future addition of facilities. The field type gives the possibility to further describe a keyword with added semantic. The field rating, represents the corresponding relevance of the keyword to the associated information cluster, referrenced by the field ICNumber. The field DateCreation stores the date and time of the keyword creation and UseElement gives the number of modification made to the keyword fields.

6 KEYWORD Name The name for the keyword, must be String just one word Type For future use, allocated I, initial Char setup, and S, standard use Rating The rating element, a value between Float 0.0 and 1.0 ICNumber The code of the information cluster for which this keyword is a member Integer DateCreation Timestamp for keyword creation, Date include full date format UseElement Version number, counts updates Integer Figure 9: Keyword internal representation CELTIC part I, Information Visualisation Design The part I from the information visualisation design gives the shareable visualisation that can be worked among users; providing the values to map data into the information space. The used co-ordinates works as the external ones, refered as the extrinsic dimensions by (Benedikt, 1991). These co-ordinates are responsible for mapping the information space elements into an 3D space. The 3D space is rendered to be shared and serve as the base visualisation to users interaction, both with other users, and to generate the part II information visualisation. Figure 10 provides a representation of the graphical components in a part I information visualisation. The elements used in the 3D space are spheres, and lines between spheres. Each sphere has its own position in the 3D space, represented by the sphere central 3D point (x, y, z); they act as the extrinsic coordinates. The user can navigate among the existant collection of spheres and links, selecting a particular element from the 3D space, to have more information about it. In order to give meaning to the different symbols used in the figure 10 (part I information visualisation) we have to make the following correspondence with the already introduced information cluster, keyword and rating elements: x each sphere represents an information cluster (IC); x each line represents a relation between two information clusters, named as semantic distance (D); x the importance (R) is given by the sphere dimension; As an additional note to part I information visualisation, colour coding will not to be used in the first phase. It can be used for adding more semantic to each sphere, by giving a visual clue of the information cluster field type.

7 R D Figure 10: Part I information visualisation components With the part I information visualisation design two new concepts are presented: importance (R) and semantic distance (D): x importance (R) is calculated as a function of ratings of the available keywords in a given information cluster. The result is a value between 0 and 10. Figure 11 introduces the algorithm to calculate the R value; Importance (R) algorithm // calculate the sphere dimension, a value between 0 and Take an information cluster (IC) 2. List all the (IC) keywords by decending order of the field rating 3. Calculate the (F) parameter 4. Consider the keywords to which the sum of the rating values are equal or greater than (F) parameter 5. Obtain (R) by multiplying the respective ratings of the selected keywords 6. Obtain the final (R) value by multiply the existant (R) value by 10 Figure 11: The importance algorithm x semantic distance (D) is calculated as a function of keywords similarity between two given information clusters (when this distance is infinite, that is, when there are no common keywords, the line between the two information clusters is not represented). Figure 12 introduces the algorithm to calculate the D value. Semantic distance (D) // calculate the value for decide about the existance of a line between to IC s, D > 0 1. take (IC1) and (IC2) 2. Calculate the (F1) parameter for (IC1) 3. Calculate the (F2) parameter for (IC2) 4. Consider the (IC1) keywords to which the sum of the rating values are equal or greater than (F1) parameter 5. Consider the (IC2) keywords to which the sum of the rating values are equal or greater than the (F2) parameter 6. Initialise (m) and (s) to zero 7. Compare each keyword of (IC1) with each (IC2) 8. When the keywords are equal, store (m) = (m) + (IC1.K.rating) * (IC2.K.rating) 9. When the keywords are equal, store (s) = (s) + max((ic1.k.rating); (IC2.K.rating)) 10. Obtain the (D) value by using the following formula: D = (m) / (s) Figure 12: The semantic distance algorithm Notice from figure 11 and 12, the need for calculate the F parameter. This is a

8 heighted value resulting from all the keywords ratings of a given information cluster. The value is needed in order to consider for each information cluster, just the more important keywords, adding extra meaning to the semantic distance, because its value is obtained from a close selection of keywords on each information cluster. The figure 13 presents the F value algorithm. It returns a F value to base the selection of the more important keywords, the ones with better ratings. (F) parameter algorithm // take the F sum of rating of the more representative IC s keywords // (the ones with better ratings) 1. take the sum (s) of all the keyword ratings of given (IC) 2. count the number (n) of different keywords for the (IC) 3. obtain the value (m) multiplying (s) by (n) 4. calculate the F value by using the following formula: F = (m) / (10 + (n) / 10) Figure 13: The F parameter algorithm The semantic distance is a value that results from comparing two information clusters. This means that we need to use the algorithm from figure 12 several times to computer all the semantic distances between the existant information clusters. If we have an information space with (n) information clusters, we need to calculate the semantic distance of several information clusters pairs. The exact number is given by the following formula (figure 14). number = ( (n) * (n) (n) ) / 2 (n) total number of existance information clusters with n = 1, number is 0 with n = 2, number is 1 with n = 3, number is 3 with n = 4, number is 6 with n = 5, number is 10 and so on Figure 14: Number of information cluster pairs Figure 15 give the necessary code to combine the information clusters pairs necessary to calculate the existant number of semantic distances. The cycles are used to generate the different existant number of information cluster pairs and to invoke, to each pair, the semantic distance algorithm from figure 12. for (i) = 2 to (n) do for (j) = 1 to (i) - 1 do calculate the semantic distance between (i) and (j) information clusters (n) total number of information clusters; (i), (j) identify a particular ICNumber Figure 15: Generation of the different information cluster pairs To apply the method described in figure 15, one condition must be present.

9 The existent information clusters have to be numbered with sequential numbers, from 1 to the final information number, without interruptions. This means that, if we delete a particular information cluster from the information space, all the other information cluster must be renumbered in order to use the above algorithms. After introducing the concepts concerning with the information visualisation part I, we need to place each sphere in the 3D space. This way, a method for giving valid values for the ICPosX, ICPosY, and ICPosZ of each information cluster must take into account the following specifications: x generate (x, y, z) co-ordinates that can be used as the central point used as argument to draw a particular sphere; x the values must take into account a maximum radius value for the sphere and allow a minimum distance between each sphere; x allow the input of a distance factor to be multiplied with the minimum distance between two spheres; x allow the input of a direction option, that give users the freedom to place a new sphere into a relative position selected from a finite set of available directions; x store the current position (selected sphere or an initial 3D point) to serve as a base for generate the new value; x test for the resulting 3D point value if it is already used by an existent sphere (if yes, test its neighbours position to assign the 3D point with the closest available value possible) If we respect the above specifications we can generate valid 3D point values to be used in the information visualisation design, allowing their position be chosen by users which can be of great importance because that way, the users group can construct a map with the spheres that can have its own meaning and allow the input of added semantic to the system. This way, we can use the relative position of the spheres also to share information among users. I N P U T S Direction Distance factor Initial point or current position Generate X Y Z New Need for a new point Check available 3D point OK! Figure 16: Block diagram for generate valid 3D point co-ordinates The user can choose a possible direction to place the new sphere, from a number of alternatives. This way, for a two dimension space, we consider 9 distinct positions, including the origin. The number results from combining three options for each dimension (a positive, a negative, and a null value) between the

10 two dimensions. Figure 17 shows the table for the available positions on a two dimensions list, with the corresponding axis diagram (where position 9 is the origin). Position X Y Figure 17: The two dimensions direction table Following the same reasoning, if we have three dimensions, each one with a positive, a negative, and null value options we can identify 27 different directions as shown is figure 18. Note that position 27 represents the current position. Position X Y Z Figure 18: Possible directions for a three co-ordinate system Figure 19 presents the algorithm for implementing the block diagram from figure 16, for generate valid 3D point co-ordinates. 5 2 Y 9 1 X

11 // 3D point co-ordinates generation // // (dir) direction // (df) distance factor read (dir), (df) // input an initial point or current position read (cposx), (cposy), (cposz) (direction) = (dir) (distace) = (df) (x) = (cposx) (y) = (cposy) (z) = (cposz) // call the generate new position algorithm gen_pos(x, y, z, direction, distance) (valid) = false while (!valid) // invoke the check position algorithm if (check_pos(x, y, z)) (valid) = true else if ( (direction) < 27) (direction) += 1 else (distance) += 1 (direction) = 1 gen_pos(x, y, z, direction, distance) Figure 19: Algorithm to generate valid 3D point co-ordinates In figure 20, the algorithm to check if the co-ordinates are already in use for other sphere is made by checking all the available information cluster coordinates. Notice that we need to know the number of the existent information clusters and that each one is numbered sequentially from 1 to the total number, without missing any integer in the above interval. // checks for a given 3D point co-ordinates availability (check_pos) = true // (n) represents the number of existent information clusters for (i) = 1 to (n) if ( (x) == (IC.ICPosX) ) && ( (y) == (IC.ICPosY) ) && ( (z) == (IC.PosZ) ) (check_pos) = false break Figure 20: Algorithm to check position availability The last component algorithm to obtain valid 3D co-ordinates is the generation of the co-ordinates itself. For this we need a valid distance code (figure 18), aand a distance factor to be multiplied by 10. Note that we choose the number 10 as the one that permits to each sphere not be intersected by existent ones. This value can be changed as a parameter for the minimum distance between two adjacent spheres. // generate a new 3D position given an initial one, a direction and a distance factor // note that the value 10 is used as the minimum distance between two spheres // (dir) = (direction) if ( (dir) < 1 ) ( (dir) > 26 )) (dir) = 1

12 (df) = (distance) if ( (df) < 1 )) (df) = 1 // calculate the new X co-ordinate if ( MOD ( (dir) ; 3) == 0 )) (nx) = x else if ( MOD ( (dir) ; 3) <= 1 ) (nx) = (x) + 10 * (df) if ( MOD ( (dir) ; 3) <= 2 ) (nx) = (x) 10 * (df) else (nx) = (x) // calculate the new Y co-ordinate if ( MOD ( (dir) ; 9) == 0 )) (ny) = (y) else if ( MOD ( (dir) ; 9 ) <= 3 ) (ny) = (y) + 10 * (df) else if ( MOD ( (dir) ; 9 ) <= 6 ) (ny) = (y) 10 * (df) else (ny) = (y) // calculate the new Z co-ordinate if ( (dir) < 10 ) (nz) = (z) + 10 * (df) else if ( (dir) < 19 ) (nz) = (z) 10 * (df) else (nz) = (z) Figure 21: Algorithm to generate new 3D point co-ordinates To represent the lines between the sphere we need to extend the algorithm from figure 15 in order to collect the starting and end point for drawing the line. The modified algorithm is shown in figure 22. // Semantic distance representation in information visualisation part I for (i) = 2 to (n) do for (j) = 1 to (i) - 1 do calculate the semantic distance between (i) and (j) information clusters if (semantic distance > 0) drawline between (xi, yi, zi) and (xj, yj, zj) (n) total number of information clusters; (i), (j) identify a particular ICNumber (xi, yi, zi) corresponds to the ICPosX, ICPosY, and ICPosZ of the ICNumber (i) (xj, yj, zj) corresponds to the ICPosX, ICPosY, and ICPosZ of the ICNumber (j) Figure 22: Data for line generation between spheres Part I information visualisation gives an 3D space representation of an information space where it is possible to navigate around the existant information clusters, visualise its relations, their relative importance, and select a particular information cluster to list their keyword list. Figure 10 presents a possible information visualisation part I, with four spheres (information clusters) with different sizes (importance) are represented. Some of the spheres are linked together, indicating the presence of common keywords and a measurable semantic distance. Notice that one of the spheres is isolated (without lines to its central point), which means that its keyword group is not related with the others information cluster s keyword groups (as calculated in the semantic distance algorithms). The lines between information clusters represent the preference of related (match) keywords, and thus, indicating a semantic distance. Notice that the mapped distance has no correlaction with the distance in the information visualisation representation. In fact, the user must select a particular line in order

13 for the system to compute its semantic distance value. This way, the relative positions of the different IC s can be represented in constant positions in the visualisation, in a way that new modifications do not affect the overall position of the existant ones. This characteristic can assist users in recognising a particular path or region of the information visualisation by keeping its appearance. A final remark goes to the scalability capacity of the information visualisation part I. Its visual elements are spheres and lines. A typical visualisation of a given information space tends to be composed by a medium to small number of concepts easilly tracked by a group of people. The usual number can be between 6 and 40 information clusters. With the formula of figure 14, we can calculate a maximum of 15 and 780 lines between the spheres that represent the information clusters. If we consider that just the more relevant keywords from each information cluster are taken to calculate the semantic distance (figure 12 and 13), we can consider a maximum of 60% of lines represented for 6 spheres, and a maximum of 10% for 40 spheres. The decrease in percentage of nonzero semantic distance is the result of more diversity in greater information spaces (otherwise something is not correct on defining the concepts and its components (keywords). Figure 23 resumes the above described values. elements minimum maximum Spheres 6 40 Lines Keywords overlaping rate 60% 10% Nonzero semantic distance 9 78 Figure 23: Typical elements on the information visualisation part I CELTIC part II, Information Visualisation Design The information visualisation design part II represents the external co-ordinate system and allows the integration with the more abstract information visualisation part I. Part II also enables each user to analyse the shareable part I model and generate clues for browse and search tasks into information resources, based on the user own operation of the part I information visualisation design. This operation consists of organising the available information using the part II, getting a focus on a particular criteria from part I information visualisation. These criteria can be keywords, combination of keywords and some statistics according to some function rules, to be introduced. The information visualisation part II is composed by a small group of the same information clusters of the part I visualisation, but without the representation of the semantic distance. The represented information clusters are filtered from all the ones of the part I visualisation, following criteria chosen by the user. The

14 space co-ordinates used to place each sphere in the part I visualisation are no longer used in part II. Instead, the selected spheres are posicioned according to the criteria choosen by the users. It is possible to customise the information visualisation part II by introducing a three criteria chosen by the user, that present the spheres according the position of these criteria in a XYZ Cartesian space (the redraw can include all the part I design or a select set of spheres). Figure 24 shows an information visualisation part II diagram. The information visualisation part II is always generated based on the part I using criterias from a criteria list. Two types of criteries can be used: x internal ones: keywords or a combination of keywords, based on their ratings or count updates, from the information visualisation design, part I; x external ones (based on potential queries to information resources like a database or the Web, as counting occurrencies of a given keyword or set of keywords). The criteria list is composed by internal and external data that can be associated with the three axis (X, Y, Z). Figure 24: Part II, Information Visualisation Design In figure 24 it is possible to see that the links between spheres (information clusters) are not considered. The part II information visualisation was generated from part I, on figure 10, and include all the four represented information clusters. Their relative position is now different, based on the criteria used on each axe. In figure 24, with the available criteria we have two groups for information clusters,

15 where the X axis, provides the more visible difference. The shadow is used to give the notion of the z position. For the two information clusters near the co-ordinates origin no shadow is represented. This gives an additional help to the users to map selected information clusters, based on a given criteria. However the shadow cue will not be implemented in a first prototype of the CELTIC system, because the z position is given by spatial positioning. An aggregate group of information clusters is represented by a cylinder, as represented in figure 24, which provide a user with another tools for composing browse and search clues based on the IC s aggregation given by the cylinder. Neither the shadows nor the cylinder are implemented in the CELTIC system prototype in order to avoid extra complexity (both in development and usage phases). For the part II visualisation it is possible, in function of each sphere position, that two or more spheres can occupy the same space, merging together or overlapping their visualisation. In information visualisation part II, just the spheres from part I with related keywords are represented. This way, in the three chosen criteria from the complete list, we must need to include at least one of the keywords presented in any of the information clusters, to generate a part II visualisation.