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Graph-based Relational Data Visualization

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Relational databases are rigid-structured data sources characterized by complex relationships among a set of relations (tables). Making sense of such relationships is a challenging problem because users must consider multiple relations, understand their ensemble of integrity constraints, interpret dozens of attributes, and draw complex SQL queries for each desired data exploration. In this scenario, we introduce a twofold methodology; we use a hierarchical graph representation to efficiently model the database relationships and, on top of it, we designed a visualization technique for rapidly relational exploration. Our results demonstrate that the exploration of databases is profoundly simplified as the user is able to visually browse the data with little or no knowledge about its structure, dismissing the need of complex SQL queries. We believe our findings will bring a novel paradigm in what concerns relational data comprehension.

Published in: Data & Analytics
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Graph-based Relational Data Visualization

  1. 1. 17th International Conference Information Visualization GGrraapphh--bbaasseedd RReellaattiioonnaall DDaattaa VViissuuaalliizzaattiioonn DDaanniieell MMáárriioo pdf at: www.icmc.usp.br/pessoas/junio ddee LLiimmaa JJoosséé FFeerrnnaannddoo RRooddrriigguueess JJrr.. AAggmmaa JJuuccii MMaacchhaaddoo TTrraaiinnaa <<ddaanniieellmm@@iiccmmcc.. uusspp..bbrr>> <<jjuunniioo@@iiccmmcc..uusspp..bbrr>> <<aaggmmaa@@iiccmmcc..uusspp..bbrr>> Instituto de Ciências Matemáticas e de Computação Universidade de São Paulo 15, 16, 17 and 18 July 2013 SOAS, University of London ● London ● UK pdf at http://www.icmc.usp.br/~junio/PublishedPapers/Lima-et_al_IV-2013.pdf
  2. 2. pdf at: www.icmc.usp.br/pessoas/junio OOuuttlliinnee 1. Introduction 2. Method 3. Experiments 4. Conclusions
  3. 3. 11.. IInnttrroodduuccttiioonn pdf at: www.icmc.usp.br/pessoas/junio
  4. 4. IInnttrroodduuccttiioonn • Large datasets are common • unstructured: text • semi-structured: XML, RDF, sensor data • structured: relational (DBMS), network (graph-like) • Analysis Process • Data Representation / Transformation • Storage / Retrieval • Statistics • Visualization • Analysis pdf at: www.icmc.usp.br/pessoas/junio Iterate
  5. 5. IInnttrroodduuccttiioonn • How to spot interesting facts in the relationships of large relational databases? • How are the entities on the database related to each other? • How are the entities distributed over the relations of the database? • How do the several attributes of the database influence the relationships of the entities? • How do we quickly and intuitively browse the relational database, considering its complex structure? pdf at: www.icmc.usp.br/pessoas/junio
  6. 6. OOuurr aapppprrooaacchh • Use graph representation • Graph-partitioning techniques • Graph-processing • Interactive Visualization Database  Graph  Partitioning  Visualization  Analysis pdf at: www.icmc.usp.br/pessoas/junio
  7. 7. 22.. MMeetthhoodd pdf at: www.icmc.usp.br/pessoas/junio
  8. 8. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis RReellaattiioonnsshhiippss aass GGrraapphhss Author Publish Work pdf at: www.icmc.usp.br/pessoas/junio
  9. 9. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis RReellaattiioonnsshhiippss aass GGrraapphhss Author Publish Work pdf at: www.icmc.usp.br/pessoas/junio Alice A Bob B Charles C … A 1 B 2 C 3 A 2 … 1 Optic Fiber 2 Networks 3 Cryptography … 11 22 33 AA BB CC
  10. 10. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis RReellaattiioonnsshhiippss aass GGrraapphhss Author Publish Work pdf at: www.icmc.usp.br/pessoas/junio Alice A Bob B Charles C … A 1 B 2 C 3 A 2 … 1 Optic Fiber 2 Networks 3 Cryptography … 11 22 33 AA BB CC
  11. 11. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis RReellaattiioonnsshhiippss aass GGrraapphhss Author Publish Work pdf at: www.icmc.usp.br/pessoas/junio Alice A Bob B Charles C … A 1 B 2 C 3 A 2 … 1 Optic Fiber 2 Networks 3 Cryptography … 11 22 33 AA BB CC
  12. 12. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis GGrraapphh PPaarrttiittiioonniinngg pdf at: www.icmc.usp.br/pessoas/junio
  13. 13. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis GGrraapphh PPaarrttiittiioonniinngg pdf at: www.icmc.usp.br/pessoas/junio
  14. 14. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis GGrraapphh PPaarrttiittiioonniinngg pdf at: www.icmc.usp.br/pessoas/junio
  15. 15. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg pdf at: www.icmc.usp.br/pessoas/junio
  16. 16. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg subgraph 1 subgraph 2 cut 0 pdf at: www.icmc.usp.br/pessoas/junio
  17. 17. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg subgraph 1 subgraph 2 cut 0 pdf at: www.icmc.usp.br/pessoas/junio
  18. 18. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg subgraph 1 subgraph 2 cut 0 pdf at: www.icmc.usp.br/pessoas/junio
  19. 19. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg cut 0 pdf at: www.icmc.usp.br/pessoas/junio
  20. 20. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg cut 1 cut 0 cut 2 pdf at: www.icmc.usp.br/pessoas/junio
  21. 21. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis HHiieerraarrcchhiiccaall PPaarrttiittiioonniinngg cut 1 cut 0 cut 2 pdf at: www.icmc.usp.br/pessoas/junio subgraph 1-1 subgraph 1-2 subgraph 2-1 subgraph 2-2
  22. 22. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh SuperNode 1-1 cut 1 cut 0 cut 2 SuperNode 1-2 pdf at: www.icmc.usp.br/pessoas/junio subgraph 2-1 subgraph 2-2
  23. 23. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh SuperNode 1-1 pdf at: www.icmc.usp.br/pessoas/junio SuperEdge 1 SuperNode 1-2 subgraph 2-1 cut 0 cut 2 subgraph 2-2
  24. 24. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh pdf at: www.icmc.usp.br/pessoas/junio SuperEdge 2 SuperNode 2-1 SuperNode 2-2 cut 0
  25. 25. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh subgraph 1 subgraph 2 cut 0 pdf at: www.icmc.usp.br/pessoas/junio
  26. 26. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh SuperNode 1 SuperNode 2 cut 0 pdf at: www.icmc.usp.br/pessoas/junio
  27. 27. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh SuperNode 1 SuperNode 2 SuperEdge 0 pdf at: www.icmc.usp.br/pessoas/junio
  28. 28. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh pdf at: www.icmc.usp.br/pessoas/junio
  29. 29. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis SSuuppeerrGGrraapphh • Further details in the paper pdf at: www.icmc.usp.br/pessoas/junio
  30. 30. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg Paper Author Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName} pdf at: www.icmc.usp.br/pessoas/junio
  31. 31. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg Paper Author Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName} pdf at: www.icmc.usp.br/pessoas/junio
  32. 32. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg Paper Author PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio PP AA Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  33. 33. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg Paper Author PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio PP AA local Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  34. 34. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg Paper Author PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR BBRR local Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  35. 35. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year Paper Author PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR BBRR local Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  36. 36. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year local Paper Author PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR BBRR ’0’000-’-0’066 ’0’066-’-1’111 Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  37. 37. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year local Paper Author ‘9‘955++ ’0’022++ ’0’066++ ** PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR BBRR ’0’000-’-0’066 ’0’066-’-1’111 Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  38. 38. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year local age Paper Author ‘9‘955++ ’0’022++ ’0’066++ ** PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR BBRR ’0’000-’-0’066 ’0’066-’-1’111 Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  39. 39. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year local age Paper Author ‘9‘955++ ’0’022++ ’0’066++ ** PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR <<4400 >>4400 <<4400 >>4400 BBRR ’0’000-’-0’066 ’0’066-’-1’111 Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  40. 40. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year dept local age Paper Author ‘9‘955++ ’0’022++ ’0’066++ ** PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR <<4400 >>4400 <<4400 >>4400 BBRR ’0’000-’-0’066 ’0’066-’-1’111 Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  41. 41. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year dept local age Paper Author PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR <<4400 >>4400 <<4400 >>4400 BBRR ’0’000-’-0’066 IMIMEE ** ’0’066++ ** EEEESSCC ICICMMCC ‘9‘955++ ’0’022++ ’0’066-’-1’111 FFFFLLCCHH Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  42. 42. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year dept local age Paper Author ‘9‘955++ ’0’022++ PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR <<4400 >>4400 <<4400 >>4400 BBRR ’0’000-’-0’066 IMIMEE ** ’0’066++ ** ’0’066-’-1’111 FFFFLLCCHH Connectivity SuperEdges Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  43. 43. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis AAttttrriibbuuttee--bbaasseedd PPaarrttiittiioonniinngg year dept local age Paper Author ‘9‘955++ ’0’022++ PPaappeerr AAuutthhoorr pdf at: www.icmc.usp.br/pessoas/junio UUSS PP AA UUSS BBRR <<4400 >>4400 <<4400 >>4400 BBRR IMIMEE ** ’0’066++ ** FFFFLLCCHH Connectivity SuperEdges Left relation: Paper = {idPaper, country, year, title} Rght relation: Author = {idAuthor, age, dept, authorName}
  44. 44. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis RR--MMiinnee PPrroottoottyyppee • Based on the GMine System • Test platform with minimalistic design • SuperNode tree: • node-link, radial layout, partial focus • SuperEdge graphs: • node-link, bipartite layout, edge filtering • Leaf SuperNode graphs: typical node-link pdf at: www.icmc.usp.br/pessoas/junio
  45. 45. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis RR--MMiinnee PPrroottoottyyppee pdf at: www.icmc.usp.br/pessoas/junio
  46. 46. DDBB m m××mm GGrraapphh PPaarrttititioionniningg GGrraapphhTTrreeee VVisisuuaalilzizaattioionn AAnnaalylyssisis 33.. EExxppeerriimmeennttss pdf at: www.icmc.usp.br/pessoas/junio
  47. 47. TTyycchhoo UUSSPP ddaattaabbaassee • Data from several USP systems • Personnel, Supervisions, Publications, Events… pdf at: www.icmc.usp.br/pessoas/junio
  48. 48. TTyycchhoo UUSSPP ddaattaabbaassee • Using 5 entities and 5 relationships • 350k events • 380k examinations • 691k publications • 50k people • 26k supervisions • 1.5 million nodes total • 1.8 million edges (relationships) pdf at: www.icmc.usp.br/pessoas/junio
  49. 49. QQ11:: aaccttiivvee aauutthhoorrss • Which group of People (by age) have the largest number of recent publications? SQL: SELECT a.age, count(*) num FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 JOIN Person a ON a.id = x.author GROUP BY a.age ORDER BY num DESC pdf at: www.icmc.usp.br/pessoas/junio
  50. 50. QQ11:: aaccttiivvee aauutthhoorrss pdf at: www.icmc.usp.br/pessoas/junio
  51. 51. QQ11..bb:: aaccttiivvee aauutthhoorrss • Who are them? • SQL: SELECT a.name, p.title FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 JOIN Person a ON a.id = x.author WHERE a.age IN (SELECT age FROM (SELECT a.age age, count(*) num FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 JOIN Person a ON a.id = x.author GROUP BY a.age ORDER BY num DESC) T) pdf at: www.icmc.usp.br/pessoas/junio
  52. 52. QQ11..bb:: aaccttiivvee aauutthhoorrss pdf at: www.icmc.usp.br/pessoas/junio
  53. 53. QQ22:: ffaavvoorriittee ccoouunnttrriieess • Which country receives the largest number of recent publications from this group of people? • SQL: SELECT a.name, p.title FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 JOIN Person a ON a.id = x.author AND a.age BETWEEN 56 AND 63 WHERE p.country IN (SELECT country FROM (SELECT p.country country, count(*) num FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 JOIN Person a ON a.id = x.author AND a.age BETWEEN 56 AND 63 GROUP BY p.country ORDER BY num DESC) T) pdf at: www.icmc.usp.br/pessoas/junio
  54. 54. QQ22:: ffaavvoorriittee ccoouunnttrriieess pdf at: www.icmc.usp.br/pessoas/junio
  55. 55. QQ22:: ffaavvoorriittee ccoouunnttrriieess pdf at: www.icmc.usp.br/pessoas/junio
  56. 56. QQ33:: aaccttiivvee aauutthhoorrss ppeerr ccoouunnttrryy • Now in one specific country, which group of People is the most active recently? • SQL: SELECT a.name, p.title FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 AND p.country = ‘Estados Unidos’ JOIN Person a ON a.id = x.author WHERE a.age IN (SELECT age FROM (SELECT a.age age, count(*) num FROM PersonPublication x JOIN Publication p ON p.id = x.publication AND p.year >= 2008 AND p.country = ‘Estados Unidos’ JOIN Person a ON a.id = x.author GROUP BY a.age ORDER BY num DESC) T) pdf at: www.icmc.usp.br/pessoas/junio
  57. 57. QQ33:: aaccttiivvee aauutthhoorrss ppeerr ccoouunnttrryy pdf at: www.icmc.usp.br/pessoas/junio
  58. 58. QQ33:: aaccttiivvee aauutthhoorrss ppeerr ccoouunnttrryy pdf at: www.icmc.usp.br/pessoas/junio
  59. 59. PPeerrffoorrmmaannccee:: iinnddiivviidduuaall qquueerriieess 150 analytical questions: PostgreSQL × R-Mine pdf at: www.icmc.usp.br/pessoas/junio
  60. 60. PPeerrffoorrmmaannccee:: aaccccuummuullaatteedd ttiimmee 150 analytical questions: PostgreSQL × R-Mine pdf at: www.icmc.usp.br/pessoas/junio
  61. 61. PPeerrffoorrmmaannccee:: llooaaddiinngg ttiimmee SuperNode Load(s) Connectivity to all siblings (seconds) pdf at: www.icmc.usp.br/pessoas/junio SQL (seconds) (initial loading) 6.032 - - Person 0.057 5.847 7.349 Event 0.271 5.276 26.716 Publication 0.160 4.484 27.677 Total 6.520 15.607 61.742
  62. 62. 44.. CCoonncclluussiioonnss pdf at: www.icmc.usp.br/pessoas/junio
  63. 63. OOuurr aapppprrooaacchh • Can use the Relational information • To guide the partitioning • To give an initial context to the analyst • Faster than running SQL queries • Make neighborhood exploration easy • Interactive Visualization environment pdf at: www.icmc.usp.br/pessoas/junio
  64. 64. CCoonnssiiddeerraattiioonnss • Initial parameters • Which entities, relationships and attributes? • In which order? • How to define partitions? Ranges? • How many partitions? • Different interaction tasks • Ongoing usability evaluation pdf at: www.icmc.usp.br/pessoas/junio
  65. 65. TThhaannkkss pdf at: www.icmc.usp.br/pessoas/junio

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