Spatial Clustering to Uncluttering Map Visualization in SOLAP

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Spatial Clustering to Uncluttering Map Visualization in SOLAP
Ricardo Silva, João Moura-Pires - New University of Lisbon
Maribel Yasmina Santos - University of Minho

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Spatial Clustering to Uncluttering Map Visualization in SOLAP

  1. 1. Spatial Clustering To Uncluttering Map <br />Visualization in SOLAP<br />Authors:<br /> Ricardo Silva (1)<br /> João Moura-Pires (1)<br /> Maribel Yasmina Santos (2)<br />(1) Universidade Nova de Lisboa - Faculdade de Ciências e Tecnologias<br />Departamento de Informática<br />(2) Universidade do Minho <br />Departamento de Sistemas de Informação<br />
  2. 2. Content<br />Context and Motivation<br />Approach Overview<br />Details<br />Conclusions and Future Work<br />
  3. 3. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Spatial OLAP (SOLAP)<br /><ul><li> Business Intelligence (BI)
  4. 4. Analysis of huge amount of data
  5. 5. Different display modes (synchronized):</li></ul>Cartographic<br />Tabular<br />Statistical Diagrams<br />new display <br />for OLAP users<br /><ul><li>Better and faster perception of the query results
  6. 6. New and better way to assimilate knowledge </li></li></ul><li>Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />SOLAP: High interactivity<br /><ul><li> Dynamic queries:
  7. 7. The user can change on-the-fly:
  8. 8. Dimensions
  9. 9. Measures
  10. 10. The level of granularity
  11. 11. The user can:
  12. 12. Perform uni, bi, multivariate analysis
  13. 13. Slices, spatial slices
  14. 14. View contextual information</li></ul>Cartographic<br />Can we always ensure that the maps offer a better and faster perception of query results?<br />No!<br />Why?<br />
  15. 15. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Example of a cluttered map<br /><ul><li> One spatial attribute, one numerical measure
  16. 16. Analysis at a lower level of granularity</li></ul>Context:<br /><ul><li> Point data
  17. 17. Lot of data</li></li></ul><li>Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Example of a cluttered map<br />Context:<br /><ul><li> Analysis at a lower level of granularity
  18. 18. Few data
  19. 19. Point data
  20. 20. One spatial attribute
  21. 21. One numerical measure
  22. 22. One semantic attribute</li></li></ul><li>Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Example of a cluttered map<br />Context:<br /><ul><li>Not at a such lower level of granularity
  23. 23. Not so much data
  24. 24. One spatial attribute
  25. 25. One numerical measure
  26. 26. One semantic attribute
  27. 27. Polygon data</li></li></ul><li>How to approach this problem?<br />
  28. 28. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />How to approach this problem?<br />To avoid the representations overlapping<br />We need to summarize <br />the data from the query<br />How identify <br />the overlapping groups?<br />through<br />Spatial clustering<br />
  29. 29. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />But… there other contexts that already use spatial clustering<br />Examples<br />Not too complex problem<br />SOLAP context<br />More complex<br />
  30. 30. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Characteristics of the solution<br /><ul><li> Allow automatic detection for the need to summarize more the data
  31. 31. Able to handle with point data and polygon data
  32. 32. DBSCAN
  33. 33. P-DBSCAN
  34. 34. Maintain the synchronization between the displays (tables, maps)
  35. 35. Clusters represented on the map (depending on type of spatial objects)
  36. 36. Data at a multi-granularity (roll-up creation to each cluster)
  37. 37. The user is able to:
  38. 38. control the intensity of summarization
  39. 39. to constraint the clusters by a spatial hierarchy level
  40. 40. change the cluster representation
  41. 41. enable or disable the summarization process</li></li></ul><li>Synchronization between map and<br />tabular display<br />
  42. 42. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Synchronization between map and tabular display<br />A<br />Spatial clustering algorithm<br />A<br />E<br />D<br />5<br />B<br />G1<br />5<br />B<br />15<br />15<br />F<br />C<br />>25<br />25<br />Stores Total Profit<br />Stores Total Profit<br />A 5<br />A 5<br />B 5<br />B 5<br />Data aggregated<br />Group170<br />C 5<br />D 25<br />E 25<br />New representation<br />F 15<br />
  43. 43. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Synchronization between map and tabular display<br />The semantic attribute (Type attribute) is at a same or at a higher level than the spatial attribute (Store attribute )<br />Store Type Total Profit<br />Store Type Total Profit<br />Cluster 1 X,Y 15<br />A X 5<br />Close<br />objects<br />Cluster 2 W 65<br />B Y 5<br />C X 5<br />Store Type Total Profit<br />D W 25<br />Close<br />objects<br />Cluster 1 X 10<br />E W 25<br />B Y 5<br />F W 15<br />Cluster 2 W 65<br />Each cluster must share the <br />semantic attribute value<br />
  44. 44. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Synchronization between map and tabular display<br />The semantic attribute (Type attribute) is at a incomparable or at a lower level than the spatial attribute (County attribute )<br />The semantic attribute comes from other dimension <br /> Type<br /> Type<br /> X Y<br /> X Y<br />County Total Profit Total Profit<br />County Total Profit Total Profit<br />A 15 5<br />Cluster 1 45 17<br />Close<br />objects<br />B 5 10<br />C 25 2<br />Straight summarization<br />
  45. 45. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Synchronization between map and tabular display<br />Example<br />
  46. 46. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Synchronization between map and tabular display<br />Example<br />Concave<br />Hull<br />
  47. 47. Control the intensity of summarization<br />
  48. 48. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Control the intensity of summarization<br />Spatial clustering algorithm<br />G1<br />mmm… i want more clusters<br />G1<br />G2<br />Interact with the system<br />in an easy way<br />
  49. 49. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Control the intensity of summarization<br />Example<br />Moving to the right or to the left<br />
  50. 50. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />DBSCAN<br />Eps = radius<br />MinPts = 3<br />Sorted 3.distance neighborhood<br />Mapping each object to the distance<br />from its 3-th nearest neighbor<br />
  51. 51. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />DBSCAN/P-DBSCAN: Novel Heuristic<br />Aims to find more than one value for Eps<br />How?<br /><ul><li> It is created a 3.distance function
  52. 52. Looks for gaps </li></ul>in 3.distance function <br />
  53. 53. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Conclusions<br /><ul><li>A mechanism to control the map visualization in SOLAP context
  54. 54. Rely on spatial clustering technique
  55. 55. Takes into account the spatial information to be displayed
  56. 56. The possible overlapping between representations
  57. 57. Adhoc/Region-based clustering
  58. 58. Query-aware
  59. 59. Novel heuristic to estimate epsDBSCAN/P-DBSCAN algorithm
  60. 60. The user has the ability to control the existence, or not, of the</li></ul> post-processing stage<br />Help to maintain the benefits from map visualization in a <br />SOLAP environment<br />
  61. 61. Approach<br />Overview<br />Conclusions<br />and Future Work<br />Context and Motivation<br />Details<br />Future Work<br /><ul><li>Properevaluation of this work:
  62. 62. Comparative analysis between several spatial clustering algorithms
  63. 63. DBSCAN authors heuristic versus our novel heuristic
  64. 64. The level of users’ satisfaction
  65. 65. Heuristic to detect the need to summarize the data
  66. 66. To consider datasets with the line as a spatial object
  67. 67. Spatial clustering applied to the map representations </li></ul>instead real coordinate space<br />

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