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ParallelSpaces: Simultaneous Exploration of Feature and Datafor Hypothesis Generation
Deokgun ParkDepartment of Computer Science
University of MarylandCollege Park, MD, USA
Jungu ChoiSchool of Electrical and Computer Engineering
Purdue UniversityWest Lafayette, IN, USA
Email: [email protected]
Niklas ElmqvistCollege of Information Studies
University of MarylandCollege Park, MD, USA
Abstract—We present ParallelSpaces, a novel method toexplore bipartite datasets in both feature and data dimensions.This dyadic data is displayed as weighted bipartite graphs usingscatterplots in two separated visual spaces, where each entityis positioned according to multi-dimensional properties of eachentity or similarity in preferences. Selecting or navigating inone space is reflected in the other space, so that organic visualpatterns can be formed to facilitate the characterization ofunderlying groupings. To aid visual pattern recognition wealso overlay a contour plot based on kernel density estimation.We have implemented two instantiations of ParallelSpacesfor (a) movie preferences, and (b) business reviews as web-based visualizations. To validate the method, we performed aqualitative user study involving eleven participants using theseweb-based tools to explore data and collect deep insights.
Keywords-Multimodal graphs, multivariate graphs, socialnetwork analysis, kernel density estimation.
I. INTRODUCTION
Bipartite graphs are common ways to represent content-actor relationships [13], such as movie ratings by users or e-commerce transaction between customers and products. Forexample, a movie fan may use a movie ratings dataset to dis-cover interesting patterns to discuss with like-minded indi-viduals in their social networks, whereas a market researchermay use it to find target segments of the market, such as“product A is favored by male engineers from the West Coastof ages 20 to 30.” While there are many statistical analysesto aid this process, establishing initial hypotheses remainschallenging. In particular, the bipartite network graph natureof these datasets combined with the immense amount of dataoften becomes a barrier. Landesberger et al. [26] poses thisas a future research challenge, where interactive feedbackenables a hypothesis-insight-driven analytical process.
Even though there exist many statistical and compu-tational tools to support this process, deriving such hy-potheses in the first place is a creative, domain-specific,and culture-dependent process that requires human analysts.After the hypothesis has been formulated and tested, large-scale machine learning and statistical tools can streamlinethe validation process. While large data volumes, such asyears of transaction records of a national retailer, can be
managed by rapidly evolving technical advances in big dataanalytics, this is not true for the abilities of human analystsexploring the data to generate the initial hypotheses.
In this paper, we argue that the main barrier againsteffective adoption of big data machine learning methodsis in interpreting their result. These methods often yieldlarge coefficient vectors, which are difficult to map tohigh-level tasks such as selecting the target group for thenext advertisement campaign, or finding major advantagesand disadvantages of a company’s products compared toits competitors. To fill this gap, we propose a novel vi-sualization technique for business transaction data calledParallelSpaces. ParallelSpaces visualizes the result of thestatistical analysis in a user-friendly format. The visualdesign of ParallelSpaces is motivated by the fact that muchanalytic CRM data can be classified within two categories:qualitative and quantitative relations between and within thecustomers of a business as well as its products (and services).
ParallelSpaces, thus, creates dual side-by-side scatterplotsand assigns separate 2D spaces to each such class of anentity. Each space uses a multivariate visualization of theentities in that class. Nodes are initially shown according tothe similarity in the relationship with other spaces. Selec-tions in one space are highlighted in the other space usingbrushing [15] based on the relationship between the items,thereby forming visual patterns in the views. The user canscan these patterns to gain an overview of the transactiondata. Furthermore, scatterplots axes can be changed to enableexploration of multivariate properties of each node, such ascustomer demographic data or product properties. Figure 1illustrates this basic concept.
To demonstrate the effectiveness of the ParallelSpacesvisualization technique, we have built web-based prototypeimplementations for two separate datasets: (1) a movie rat-ings dataset called MovieLens dataset, and (2) Yelp businessreviews. Figure 2 shows a screen image of the system.We used these prototypes in a qualitative user study whereeleven participants were asked to explore the movie andbusiness data in order to collect interesting findings. Ourresults highlight the utility of the ParallelSpaces method
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Figure 1. Movie entities and user entities are represented as blue circles on the left and red circles on the right, respectively. The system uses the mappingmatrix, middle, to brush and link the two spaces according to the user-defined criteria. Selecting users causes selection of movies they prefer and selectinga movie or movies leads to selection of users who give similar common ratings, vice versa. common ratings. Using axes rotation, the linked users andmovie data can be further explored according to demographic criteria, shown in the right table, and the movie criteria, shown in the left table.
as well as our interaction techniques for the hypothesisgeneration step.
Our contributions are (1) the use of connected plots toshow the results of the co-clustering, (2) the design ofvisual elements and interactions to enable exploration, and(3) an example system with a user study on the utility ofParallelSpaces to aid hypothesis generation.
II. RELATED WORKS
Our work intersects several research areas within thegeneral areas of visualization and visual analytics:
• Bipartite graphs: our data is graph-based and bimodal;• Multidimensional visualization: our focus is on dis-
playing multivariate data associated with graph vertices;• Machine learning: use of mathematical and statistical
modeling to extract data from multivariate datasets.
A. Bipartite Graphs
A bipartite graph (bigraph) is a graph G = (V,E) whosevertices V can be partitioned into two independent sets (i.e.,none of the vertices in the set are adjacent) T and U . Thetwo vertex classes can be seen as two different types, ormodes, of the graph, and can for example be colored usingonly two colors. A weighted graph, on the other hand, isa graph whose edges E have a weight wi. This means thata weighted bipartite graph is a bipartite graph where theedges connecting the two sets have an associated weight.
Graphs in general are an active area of research, andis a core dataset for information visualization [20]. Multi-ple general graph visualizations exist [9]. Some tools andtechniques are targeted specifically at bigraphs. Perhaps,the closest to our work is NetLens [13], which visualizesso-called “content-actor” networks using two side-by-sideand coordinated views. This content-actor network model isessentially equivalent to bipartite graphs, except their modelallows for intra-relationships (within-mode) to the same set.Furthermore, the interaction propagation from one mode tothe other is similar to those in our ParallelSpaces work.However, NetLens was originally designed for publicationdata where the contents represent papers and actors representauthors. As a result, whereas NetLens has a complex inter-face with many different views and visual representations,ParallelSpaces uses two side-by-side scatterplots and simpli-fies the visual representation and interactions between them.Because the properties of entities are visible in scatterplots,making a query becomes selecting a region which will beeasier for the users.
Another highly relevant work is semantic substrates [21],where graph nodes of different modes are partitioned intoseparate 2D regions on the visual space, often using anattribute-based layout such as time. The visualization sup-presses edges between modes except for when a nodeis selected. ParallelSpaces similarly employs parallel 2Dspaces to partition the two different sets of vertices in thebipartite graph, and also suppresses edges. However, themain difference is that ParallelSpaces puts more emphasis
Axis Selection Menu
Configuration for
Contour plot and Selection
VisDock Toolset
User Space
Movie Space ace
Figure 2. The MovieVis tool. Two groups in the movie space have been selected to compare corresponding user distribution. Two movies selected inthe upper-center region—One flew Over the Cuckoo’s Nest (1975) and Amadeus (1984)–and are shown in blue color. Another two movies selected in alower-center region—Phenomenon (1996) and Twister (1996)—are shown in orange. The highlighted users are those who liked all both pairs of movies(because the group mode is set to “common”). Based on the user space axes—gender for the horizontal and age for the vertical—we can see that while themovie One Flew Over the Cuckoo’s Nest and Amadeus were favored by male reviewers of all ages, the Phenomenon and Twister were liked by relativelyyounger male audiences.
on visualizing the multivariate attributes of the nodes, and isintegrated with contour density plots to show how selectionsrelate across spaces.
B. Multidimensional Visualization
Named as one of the classic information visualization datatypes [20], multidimensional data consist of multiple (morethan three) dimensions and are often represented using datatables. Many systems for multidimensional visualizationexist, including Tukey’s PRIM-9 [25] system, Becker andCleveland’s trellis displays [3], Ward’s XmdvTool [28], andthe GGobi system [23]. ScatterDice [8] showed multidimen-sional visual exploration using scatterplot, where the userscan interactively assign properties to axes. ParallelSpacesfollows a similar approach, but extends the idea to multi-modal datasets by juxtaposing two displays.
Creating multiple data views is rooted in linked graphsfrom more than 25 years of statistics [2], [3], and has oftenbeen combined with brushing. It is also a common strategyfor dealing with multidimensional datasets in interactive vi-sualization; examples of this practice include Mondrian [24],Improvise [29], and Tableau/Polaris [22]. The most commonapproach to organize multiple views is called coordinatedmultiple views (CMV) [1], [19] and simply juxtaposes viewsin the same visual space with brushing [2]—dynamic high-lighting of items selected in one view in all other views—asthe main coordination mechanism.
C. Machine Learning
Machine learning, data mining, and information retrievalare all research areas that, similar to visualization, aretackling sensemaking for big data. Many of the methodsproposed from these domains are already extensively utilizedin visualization and visual analytics. Arguably the mostpopular of such methods is cluster analysis [12] that uses themultivariate properties of data to find similar items so thatthey can be grouped together. This fits well with the conceptof visual variables for visualization, where the position orlocation of a mark is its most salient visual feature [4]. Inother words, visualizations of cluster analysis promote theunderstanding of latent classes in the data.
There exist many ways to extract visual coordinates froma multivariate dataset. Thus, techniques such as dimension-ality reduction have long been an active area of research [5].The challenge is that the process is an inherently lossyone. Self-organizing maps have been widely used as a toolfor this purpose [14]. Another algorithm based on singularvector decomposition (SVD) tries to reduce the dimension-ality to an underlying set of latent taste dimensions [10].The reduced dimensionality represents “hidden themes” or“latent concepts” in the document, yielding the name LatentSemantic Indexing (LSI). A generalization of probabilisticLSI called Latent Dirichlet Allocation (LDA) [6] providesimproved accuracy.
One of the applications of machine learning, wherebipartite data is used, is collaborative filtering [18] forrecommender systems. A recommender system [16] is an
information filtering system designed to predict contentsfor a particular user based on their own past ratings andthat of other like-minded individuals (collaborative filter-ing), as well as based on the characteristics of the contentitself (content-based filtering). The data used for the formerapproach—collaborative filtering—is a dyadic dataset con-taining implicit ratings of the form “User A bought Content1,” or explicit ratings of the form “User A gave Content 1a rating 4 out of 5.” As it turns out, this type of dyadic datacan be modeled as a weighted bipartite graph, where the twosets represent users and content, and the undirected edgesbetween the sets are ratings that individual users appliedto specific content. Iwata et al. [11] used latent semanticanalysis methods to create scatterplot representations ofextracted data. His scatterplot arranged the movies accordingto their similarity in ratings patterns of users. However, itis hard to see what each cluster means. To overcome thislimitation, the ParallelSpaces tries to show the distributionof users who liked each cluster, in terms of their propertieslike age, gender or job. It will enable hypothetical labelingof each cluster.
III. DATA ANALYSIS: BUSINESS TRANSACTIONS
There are two kinds of datasets that characterize themajority of business intelligence data: quantitative and qual-itative. We chose the two example datasets used in this paperfor the purpose of representing both of these general types.
A quantitative dataset is mostly numeric, and an exampleis customer transaction records for a product. Such a datasetcan be expressed as “customer A bought item B five times,”or (A, B, 5). In this paper, these kinds of dataset are repre-sented with the movie preference dataset called MovieLens.1
Even if not strictly a traditional business dataset, themovie dataset is adequate for the purposes of our paperfor two reasons. First, there are no privacy issues, whereastransaction data from a real merchant can reveal the identityof customers and sensitive data related to medical or adultproducts. Our movie dataset has no such issues to beginwith. Second, the movie preferences in our dataset are easilyunderstandable without prerequisite knowledge and alsogeneralizes to domain-specific business data. For instance, inthe case of real transaction data, we cannot directly comparethe preference based on the number of purchases if theproduct A and product B belongs different category.
Qualitative data is often more subjective in nature, suchas customer reviews written for a product. Professionalmarketers try to understand the market responses by usingreviews for their own product or for a competitors productto identify strengths, weaknesses, opportunities, and threats.However, sometimes the sheer number of reviews can beoverwhelming. Methods such as topic modeling eases thisburden by clustering documents based on their similarity. We
1http://www.grouplens.org/node/12
argue that our Yelp datasets, which captures business reviewswritten by customers, represents such qualitative data. Forexample, the dataset allows for comparing good and badMexican or Asian restaurants based on these reviews. Again,the straightforward nature of this dataset demonstrates theParallSpaces approach and generalizes easily to more spe-cific qualitative business data.
IV. TASK ANALYSIS: DYADIC DATA EXPLORATION
Pirolli and Card [17] suggested a model for sensemaking,which can be used as reference for the hypothesis generationprocess. However given the specific forms of dataset in thecontext, the task of business intelligence analyst can befurther specified as follows:
• Search and filter: Retrieve entities according to spe-cific multivariate properties, such as age or rating range.
• Data distribution: Find the characteristics of selectedentities in a multivariate dimension.
• Finding similar entities: Find entities that shows simi-lar transaction patterns. Two definitions of similarity arepossible. First, the properties of nodes can be similar.For example, users can be similar if they belong tothe same age, gender, and geographical location group.Second, the nodes can be similar in their relations withthe opposite parties. For example, two users can besimilar if their buying patterns are similar.
• Finding similar linked entities: Find the related enti-ties where relationship can be defined in the context ofcustomer-product matrix. For example, in the context ofthe movie ratings dataset, given users, find the moviesthey gave more than 4 ratings. Also the relationshipshould be interactively adjustable. For example, thesystem should be able to find people who liked ordisliked a certain items.
• Estimate correlation: Estimate the strength of relation-ship. For example, judging whether there is a strongcorrelation between the age of customers and the kindof movies they like.
As a hypothetical example to illustrate the use of thesetasks, let us assume that a BI analyst is trying to find moviesto recommend to a set of viewers. First he needs to selectthese viewers using search and filter. Then he may examinethe property distribution of the selected moviegoers usingoverview of property distribution. Also the analyst may wantto find users who show similar rating patterns with theselected target group using identification of similar entities.After identifying the similar users, the analyst may identifythe movies these people like in common using identificationof related entities. Finally, having the ability to estimate thestrength of correlation helps the analyst to iteratively explorevarious options using information foraging models.
V. PARALLELSPACES: VISUAL DESIGN
ParallelSpaces is an interactive visualization techniquefor visualizing multimodal and multivariate data in dualjuxtaposed spaces that each use mutually brushed visualrepresentations (often scatterplots). In the section below wedescribe the visual design of the technique, including layout,position, size, color, brightness, and density plots.
A. Space Layout
A key observation from our bipartite graphs is that atits core, the graph can be split into two independent sets.For example, in the case of the movie preference data, theusers and the movies form these two independent domains.However, because the sets do not overlap, we design a basicvisual representation that consists of two parallel 2D spaces,one for each set. This design is similar to the separate contentand actor spaces used in NetLens [13].
The bivariate graph closely connects nodes in one spaceto the other. The natural way to represent this is to supportbrushing and highlighting between the spaces (even if we,strictly speaking, are not brushing the same entity butconnected entities).
Practically speaking, this means that selecting an entity inone space corresponds to selecting the connected entities inthe other parallel space. For example, if we select a movie inthe movie space, the users who liked the movie are selected(and highlighted) in the user space. Analogously, if a useris selected in the user space, the movies that received highratings from that user can be selected in the movie space.Since we have relaxed the traditional constraint that brushingapplies to the same item in different views, the underlyingrelationship is customizable. For example, a researcher maywant to see which groups did not like a specific movie. Inthis case, the researcher can filter the relationship betweenthe two spaces, making this pattern clearly visible.
The position of a visual mark is often the most salientfeature in a visualization. In ParallelSpaces, any multidimen-sional property can be an axis. However, the relationshiptable is only visible when a user selects some entities.To make the relation between entities more clear, we alsoallowed the user to organize the 2D layout of points by theirsimilarity. The more similar the entities are, the closer theywill be placed.
Given that each user has ratings over m possible movies,each user is represented as an m-vector. Similarly, eachmovie is an n-vector representing users and their ratings.Finding a position for each entity in a 2D space thusbecomes a projection (or dimensionality reduction) problemwhere m- or n-dimensional vectors are projected onto a two-dimensional space. Naturally, there are many approaches toachieving this goal: principal component analysis (PCA),multidimensional scaling (MDS), singular vector decompo-sition (SVD), and probabilistic Latent Semantic Analysis(pLSA) are some of the choices. In our implementation, we
Entity Feature Visual VariableMovie Number of ratings SizeUser Number of ratings SizeMovie Average ratings Opacity + brightnessUser Average ratings Opacity + brightness
Table ISELECTION OF SALIENCE FEATURES AND THE MATCHING VISUALVARIABLES FOR PARALLELSPACES IN THE MOVIEVIS PROTOTYPE
IMPLEMENTATION.
choose PCA as our solution, but other alternatives—evenhybrid ones—are possible and within context of the overallParallelSpaces method.
This similarity positioning feature provides starting pointfor the analysis, because the meaning of each cluster canmean a market segment, which shares similar preferencepatterns. For example, selecting a region in the movie spaceat similarity axis, the people who liked the movies can beselected. Further, by interactively changing axes of the userspace, we can explore if there are particular patterns in age,gender, job or location dimensions.
Because the number of nodes can be high, we need todifferentiate the visibility of nodes according to their impor-tance. In our MovieVis implementation of ParallelSpaces formovie preference, we selected the setup listed in Table I torepresent salience.
In ParallelSpaces, we use colors to represent set member-ships when highlighting items. Also transparency is applied,so that when an item is part of two or more selections, thecolors are mixed to represent its memberships. The benefitof this approach is that it does not change the size of themark. However, the drawback is that color transparency andblending are more difficult to perceive, particularly for manyselections.
B. Showing Distribution: Contour Plot
Perceiving the distribution and density of a large numberof visual points in a substrate is negatively affected byboth scale and overplotting (which leads to occlusion).Meanwhile, being able to assess the distribution of a groupof entites corresponding to a brushed selection in anotherspace is an important analytical task. There exist severaldifferent approaches to address this problem, such as:
• Smaller marks, yielding less overplotting;• Transparency, to mitigate occlusion;• Contour plots, to represent the density pattern; or• 3D mesh gradient, to characterize the distribution.Smaller marks may affect user interaction because the
users will have difficulties in selecting them. This can beparticularly problematic for touch-based tablets or mobilephones with screens. Furthermore, transparency is alreadyassigned to data salience.
Our design choice is therefore to dynamically constructcontour plots for each visualization space to show data
density. More specifically, we use kernel density estimation(KDE) to smooth and quantify the underlying group ofpoints. Basically, the idea is to construct visual represen-tations of KDE clusters around the selected group of pointsto communicate their distribution. While a 3D mesh repre-sentation may also have been useful, we prefer to choosevisual representations that fit within our 2D visual design.
KDE algorithms generally have two tunable factors: thebandwidth and the kernel. The bandwith determines thesize of each kernel, which indirectly yields the degree ofsmoothness of the resulting image. If it is low, it may causenoisy patterns, which are hard to identify. When it is toohigh, on the other hand, it can create a distribution patternthat is too smooth and carries little meaningful information.Previous work shows that the optimal bandwidth can bedetermined by signal characteristics [27]. In our work, theuser can interactively change the bandwidth. For bivariateKDE, the kernel parameter can also have an effect on theaccuracy.
In our MovieVis prototype for ParallelSpaces, we supporttwo types of contour plots (Figure 3):
• Density mode: Show the density of selected entities inthe particular space (the common approach). All kernelswill have the same height, regardless of their weight(i.e., movie rating).
• Amplitude mode: Modulate the kernel height by thecorresponding entity ratings, causing higher values inareas with high ratings and less influence from areaswith lower ratings. While conveying more than justpoint density, this approach has the drawback of con-founding density with weights.
If the analyst merely wants to see which movies a useror group of users rated, the density contour plot can providethat information. However, the amplitude contour plot willalso show information on the individual ratings that theselected users gave to these movies. Comparing the two plotsmay yield interesting new insights.
Figure 3. Density (left) and amplitude (right) contour plots for all themovies rated by a male educator (age 47). The selection criteria was everymovie he liked. By comparing density mode KDE and amplitude modeKDE, we can spot the area where the users especially liked over the areathe users have transaction records.In this example, the red circled area willcontain the movies he rated more highly.
VI. PARALLELSPACES: INTERACTION DESIGN
ParallelSpaces relies heavily on interaction to supportvisual exploration. Below we review our interaction design.
A. Selection
One of the most frequent tasks of visual analytics iscomparing patterns between multiple entities. To support thisprocess, an ordinal color is given to each selection to showwhich items belong to the selection. Selections can consistof one or multiple entities defined by an enclosing border. Alasso tool allows selecting multiple entities. Hovering overan item shows a tooltip with the movie title and a link tothe IMDb page, where more information is available.
To support finding particular movies and users, we providea search toolbar with autocomplete support. When the useris looking for a specific movie, he or she can type a fewwords to find it. Selecting a movie from the search bar isequivalent to clicking it.
Because we regard each space independently, there aretwo modes of selection. When items are selected in themovie space, movies are selected and the selection prop-agates to the user space based on their relation. This ismovie mode selection. Similarly, when users are selectedin the user space, the corresponding movies will highlightedin the movie space. This is accordingly called user modeselection. Selection modes are simply switched by clickingin the opposite space. In the case of movie mode, selectinganother movie will add the selected movie to the selectionqueue to enable the comparison of the visual pattern withpreviously selected movies.
B. Relationship between Spaces
As the relationship between the parallel spaces is cus-tomizable, we provided a simple range slider to adjust therelationship to investigate. For example, when the rangeslider is in the 4 to 5 range, selecting a movie entity willhighlight all the users who gave that particular movie 4 to5 ratings. However, when the range slider is in the 0 to 1range, selecting a user entity will highlight all movies, whichthat particular user gave 0 to 1 ratings.
Our implementation also supports standard navigationtechniques such as zooming and panning using mousewheel and dragging. To reduce the effect of overplotting,we applied semantic zooming, where the points becomesmaller when zoomed in. We also use animated transitionsto maintain object constancy in the display and allow theuser to easily perceive state changes. This is particularlyimportant for the axis rotation, where points change position.
VII. IMPLEMENTATION NOTES
We have implemented two prototype instantiations ofthe ParallelSpaces techniques: MovieVis, for movie ratingsusing the MovieLens 100k dataset, and YelpVis, for busi-ness reviews from Yelp.com. In the case of YelpVis, the
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Figure 4. On the left, we compare two movies, Toy Story (1995), in blue, and Scream (1996), in orange, according to the age, location and similaritycriteria for users. Some notable observations are while the former is liked all around the U.S. by any age groups the latter is mostly popular in the easternpart and within a younger generation. On the right, we compare two users, a 19-year-old male student, in blue, and a 51-year-old male educator, in orangeaccording to the average, release date, and similarity criteria for movies. We observe that the older user tends to rate older films highly. In addition, hisaverage review tends to conform to the average ratings patterns of all users while the younger user seems to deviate from it.
relationship between words and business was the number ofoccurrence of the words for particular business. The wordspace contains words like ’fantastic’, ’good’, or ’bad’ forrestaurants and the frequencies at which certain words’ ap-pear vary for different restaurants. The rationale is that userscan easily discover the patterns in the reviews of restaurantsusing a set of such words. Both prototypes were built asweb-based JavaScript and SVG applications using the D3visualization toolkit [7]. We use the VisDock2 library (alsoJavaScript) for advanced cross-cutting interaction support forselection, query management, and annotation. An interactivedemonstration of the MovieVis prototype can be seen athttp://vistalk.herokuapp.com/movievis/, and the YelpVis pro-totype is available at http://vistalk.herokuapp.com/yelpvis/.
VIII. USAGE EXAMPLE
We give a usage scenario to explain how the Paral-lelSpaces tool can help someone with forming an initialhypothesis about the dataset. Let’s say a market researcheruses MovieVis to study the preference data of two moviesScream (1996) and Toy Story (1995). She selects these twomovies in the movie space using the search option providedby MovieVis. This visualizes the preference data on the userspace with both axes set to similarity by default. MovieVisprovides a drop-down menu to set the axes in the userspace to one of the seven quantities: Similarity, Age, Job,Location, Gender, Average Review, and Number of Reviews.She selects “Location” as the X-axis in the user space todisplay the users on a geographical map of United States.Figure 4 shows the visualization after applying the settingsabove to the user space. She observes from the contour
2https://github.com/VisDockHub/NewVisDock
plots in the user space that, while Scream is highly ratedby users on the East Coast, Toy Story is highly rated byusers all around the United States. Thus, she changes the Y-axis to “Age” while leaving the X-axis to the default setting,yielding the visualization in the bottom left of Figure 4. Shethen observes that while Scream is highly rated by users ofage groups 15 to 30, Toy Story is highly rated by users ofall age groups.
IX. QUALITATIVE USER STUDY
The primary purpose of ParallelSpaces is to aid in gen-erating initial hypotheses for weighted bivariate graphs. Weconducted a qualitative user study to evaluate whether thesystem achieves this purpose.
A. Method
We recruited 11 (8 male, 3 female) paid participants touse the MovieVis and YelpVis systems for 20 minutes each.All participants were university students, and the averageage was 26, ranging from 20 to 34. Prior to using thesystems, participants were given 10 minutes of trainingin using the tools. During the exploration (two sessionsof 20 minutes), they were encouraged to write commentsabout their findings using an annotation feature embeddedin the tools. After completing the exploration sessions, theusers were asked to evaluate their experience in terms ofusefulness, enjoyability, and ease of use. We also collectedsubjective free-form feedback (comments and notes) as wellas basic demographic and technical information about theparticipants. A full user study session lasted approximatelyone hour (10 minutes of training, two 20-minute sessionsfor exploration, and 10 minutes for the post-test survey).
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Figure 5. The demographic survey shows that our participants were quitefamiliar with movie ratings, while their knowledge of Yelp business reviewswas on average lower and with higher variation. Participant expertise forinteractive visualization was also diverse.
B. Results
Figure 5 shows the demographic survey data for ourparticipants. In general, all 11 of our participants wereable to understand the MovieVis and YelpVis tools andto independently perform data exploration using them. Intotal, participants wrote 71 comments for MovieVis and52 comments for YelpVis using the embedded annotationmechanism in the tools, yielding an average of 6.5 (s.d. 4.8)and 4.7 (s.d. 3.8) comments per participant, respectively. Theoverall feedback for the tools was generally positive, butparticipants provided many specific points of improvementand criticism.
Figure 6 shows the post-study survey ratings on efficiency,ease of use, and enjoyability. The ratings for YelpVis werelower than for MovieVis. One explanation might be that theparticipants’ prior interest and knowledge of the datasets waslower for Yelp business reviews than for movies (Figure 5).This is supported by the fact that of the 11 participants,the five with low familiarity with Yelp reviews also gavesignificantly lower subjective ratings than the remaining sixwho were familiar with Yelp reviews. Interestingly, that samegroup of five gave MovieVis higher scores.
In the treatment below, we analyze our qualitative resultsfrom the study based on three basic aspects: efficiency(the perceived usefulness of the tools), enjoyability andmotivation (how well the tool guided and motivated the par-ticipants), and ease of use (usability or conceptual barriershindering the exploration). We also discuss several points ofimprovement that were raised by participants.
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Figure 6. Subjective ratings for the MovieVis and YelpVis tools for thequalitative user study.
Efficiency: Most participants expressed very positivefeedback on the efficiency of ParallelSpaces in terms ofgeneral usefulness and utility. MovieVis, in particular, waspreferred as highly useful, presumably due to the familiarityand interest bias of the datasets as discussed above. Severalof the comments were expressly derived from advancedfeatures of the system. For example, one participant stated“So Matilda and Contact are both good movies and bothliked by a lot of people from all ages, but they have a’far’ similarity because Contact has way more reviews thanMatilda and [is] closer to movies [...] like Star Wars...” Thesame participant used selections and graphical axes to spec-ulate how the number of reviews affect the similarity metricin the visualization, and also suggested a fragmentation inthe audience of these two movies that corresponds to theirdifferent genres.
Enjoyability and Motivation: Motivational factors playan important role in collective intelligence systems, whichrely on the voluntary efforts of individual users. In thefeedback from participants, several people provided positivefeedback, such as one participant noting that he did notnotice how 20 minutes had passed already, and anotherrequesting the URL of the tool to continue exploring afterthe study. However, a few participants did not seem to enjoythe experience even if this was not clear from their verbalor written feedback. We speculate that this is due to therelatively high analytic and conceptual thresholds in usingParallelSpaces effectively; one participant underscored thisby stating that “as a geek, I would like to play with this, butit is not for non-geeks.”
Ease of Use: The score for the ease of use wasof the lowest of the three. Several participants were con-cerned about the usability of the system, in particular forunderstanding the word business relationships in YelpVis.The stopwords for the general query was not adequate forYelpVis and resulted in many frequent words with littlemeaning, such as go and place.
Furthermore, the concept of similarity was not well-understood for some participants. They frequently reliedonly on the other concrete axes such as age, occupation,and average rating. In addition, participants rarely used thecontour plot in the user study, and even those that didexpressed confusion on its meaning, suggesting that thisfunctionality could be better integrated into the tool.
X. CONCLUSION AND FUTURE WORK
In summary, this work presents a novel visualizationtechnique called ParallelSpaces designed for business trans-action data often used for generating initial hypotheses forbusiness intelligence. We also reported on two concreteinstantiations of ParallelSpaces as web-based visualizationsdesigned for casual end-users as well as results from a qual-itative evaluation investigating the utility of the techniquefor users to aid the hypothesis generation. Our future work
will continue to explore visual mechanisms for businessintelligence analytics, focusing in particular on the type ofbusiness transaction data studied here.
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