Monthly Archive for April, 2006

I completed the script that performs the sequences extraction from the patterns of usage in the visual information retrieval experiment. The first matrix is the cumulative representation of the LSI method across the different tasks. The second matrix is, on the contrary, CNG. To understand the meaning of the cells the following key is needed: the columns are the events at time T+1, while the rows at time T. The first element is the case A, then B then C and D. These states are so summarised:

A: The user read a document and then select a document in the relative neighborhood of the first document;

B: The user read a document and then moves away from the cluster of the first document;

C: The user selects and stays in the cluster;

D: The user selects and moves away from the cluster.

[19, 132, 0 , 0]

[83, 588, 10, 90]

[4 , 49 , 0 , 0]

[14, 104, 0 , 2]

[5 , 88 , 0 , 1]

[48, 537, 17, 98]

[2 , 49 , 0 , 0]

[6 , 109, 1 , 2]

Next step is to verify whether these frequencies are different from the expected. On a first sight it will seem that the most frequent move is the ‘read + long jump’. Now we need to check whether this difference is significative.

P.S. To verify the move within the cluster we used the sequence determined by the Minimal Spanning Tree, which is extremely selective in defining the “in-cluster” movements.

Today I spent some more time exploring the Visual Information Retrieval experiment. Apparently, the users scored better using CNG for the second task (see the picture below). This task was much more complex than the first one because of the smaller number of correct results in the dataset (5 out of 1000). Surprisingly I found also another effect, the time required to select the relevant results. It seems that the users spent less time selecting the relevant results in the first task using CNG.

Discussing this results with my advisor, we agreed that the results I have are interesting but not sufficient for explaining the effects and to decide on which method gives better results.

Some other explorations are needed: I have to use the Wilcoxon test for repeated measure for binding the results of the first task with that of the second. Secondarily it will be interesting to compute the average distance between consequent read items for each algorithm. This might be a nice parameter to represent the ability of the algorithm to group together relevant or irrelevant results.

Ok, so far the analysis on the map interaction shows that there is no huge difference in the way the users interact with the map. LSI seems to spread the results all over the available space. On the contrary, CNG returns rankings which are much wider than LSI, with the resulting effect of pushing the points of the map on the top part of the quadrant.

The interesting fact which emerges is that CNG offers the relevant points much closer to the query origin, in the central lower part of the map. The subsequent analysis of the items selected shows exactly this point: while LSI mixes the most relevant items in the jungle of the other results, CNG keeps them much closer to the query origin in a space that is less populated by other results (see the map below).

So, where to go next? Two ideas so far: (a) Mapping the final selected points to see whether they are more dispersed with CNG or LSI; (b) Trace the interaction trails to assess if there are common patterns of exploration of the points in the map.

Apart from this extra analysis on the map then I am more concerned with the numerical results from the experiment. Our goal is always to check whether there is a difference between CNG and LSI in the user experience. Is there any outscore of one of the two methods concerning: (1) relevant results selected; (2) time needed to select the relevant results; (3) user perception of appropriateness; (4) number of query composed; (5) average time spent for each query; … 

Tags: information metric, information retrieval, information visualization, interaction design, map algorithms, maps, data mining

The journal “La Tribune de Genève” published today an article on STAMPS and the field trial we are organising. The article spans on the theme of location based services and why is so interesting to know where we are. The journalist, Emmanuel Grandjean, reports a few quotes of mine that I made during the interview. One of the main claims I raised is that communication is more and more fluid and LBSs add a new dimension to this flexibility.

Also, that communication tend to be maximally efficient. So, knowing where our partner is located can already prevent lots of different inferences on what is possible to communicate and how with our partner being in that location, and with us being in our location.

Tags: field tools, location awareness, Location Based Services, mobile learning, Symbian

I have been pocking around to generate a visualization that can help understand how the users interacted with the map in our virtual retrieval experiment. It was fun to discover that there were some zones more used than others in the map, namely the central upper quadrant and the central left and right quadrants. The image below shows the density of visualization of results.

The map has been subdivided into 9 quadrants. In each quadrant is possible to see three big numbers. The upper one is the number of results that fall into the sector (even if you see few points there might be super impositions between similar queries). The lower-left big number in blue is the number of the items that have been read by the users. The red is the number of items that have been selected by the users in that quadrant.

Tags: information metric, information retrieval, information visualization, Latent Semantic Analysis, map algorithms, maps, search engine, spatial clustering, spreading activation

R. M. Ripley, A. Sarma, and A. van der Hoek. Using visualizations to analyze workspace activity and discern software project evolution. ISR Technical Report UCI-ISR-06-1, University of California, Irvine, California, USA, 2006. [pdf]

———————–

In this paper, the authors presented a prototype of a 3D visualization for workspace activity. This visualization shows only active workspaces and artifacts and depicts activities as changes to artifacts. Each change to an artifact is denoted as a cylinder and stacks of such cylinders represent activities in a particular workspace (developer-centric) or activities carried on specific artifact (artifact-centric). They applied the visualization to several open-source projects and demonstrated pro ject evolution and other interesting situations.

The visualization has two primary modes: developer-centric and artifact-centric, which we will discuss shortly. Common to both modes, the visualization shows only active artifacts and workspaces, thereby eliminating the clutter of inactive entities. Stacks of cylinders represent workspace activities

(changes to artifacts), with each cylinder corresponding to a particular artifact in a particular workspace with the dimensions representing the size of the change (the bigger the change, the larger the cylinder).

In the developer-centric view (see the figure, inset), a stack of cylinders represents a developer’s workspace with each cylinder representing an artifact being changed in that workspace. Workspaces with many activities correspond to tall stacks of cylinders. The stacks of cylinders with the most recent changes are placed in the front of the view and, as time elapses, stacks for workspaces with “older” activity slowly start moving to the back, representing dormancy. Thereby a user is able to quickly discern the loci of activities from the height of the stacks and recency of these activities from their position. The artifact-centric view (see the figure, bottom) behaves similarly to the developer view, but instead each stack of cylinders represents a particular artifact and each cylinder in the stack represents changes to that artifact made by a particular workspace.

Copyright notice: the present content was taken from the following URL, the copyrights are reserved by the respective author/s.

Tags: collaboration tools, information visualization, software visualization