3. Syntaxes oriented mainly to represent documents

4. RDF/XML, N3, Turtle, JSON, etc.

5. Document-centric data-centricview

6. Redundancy

7. No structure(chunks)

8. Lackof metadata

9. sequentiality of theinformation

10. Use?

11. examples:

12. Billion Triple 2010 (~3200M triples, 318 gzippedchunks, ~27GB)

16. Metadata

17. Compactness

18. Efficient exchange

19. RDF compression

22. Phylosophy of publication and exchange,

23. Compact RDF representation

27. Source and providerinformation

28. Publication data

29. Data set statistics

30. Otherinformation

31. Information required to retrieve and process the represented data

39. the number of triples of G in which s occurs as subject

40. deg−(G), deg−(G)

41. partialout-degree, deg− −(s, p)

42. the number of triples of G in which s occurs as subject and p as predicate

43. deg− −(G), deg− −(G)

44. labeledout-degree, degL−(s)

45. the number of different predicates (labels) of G with which s is related as a subject in a triple of G

46. degL−(G), degL−(G)

47. subject-object ratio, αs−o

48. the proportion of common subjects and objects in the graph G

49. αs−o =|SG∩OG| / |SG∪OG|

54. Currently, in RDF formats:

55. namespaces and prefixes

56. Currently, in Triple Stores:

59. (1) Common subject-objects

60. (2) The non common subjects

61. (3) The non common objects

62. (4) Predicates

63. List of strings matching the mapping of the four subsets, in order from (1) to (4).

72. take advantage of the structure indexed in Bp and Bo

73. accessible by fast rank/ select operations.

74. sequence S of length n drawn from an alphabet Σ = {0,1}:

75. ranka(S,i): counts the occurrences of a symbol a ∈ {0,1} in S[1,i].

83. Subject-object JOINs resolution can profit from the common naming in the dictionary, as the elements are correctly and quickly localized in the top IDs.

84. Algorithm 1 can response basic ASK queries of SPARQL for patterns (s,p,o), (s,?p,?o) and (s,p,?o).

87. lack of structure, metadata information and native operations over the data

88. HDT addressestheseproblems (producer, consumer)

89. Header, Dictionary, and Triples

90. Triples PracticeImplementation

91. Compact (HDT-Plain)

92. Compress (HDT-Compress, outperforms universal compressors)

95. Open-source (soon at http://hdt.dcc.uchile.cl/)

96. RDF native storage

97. Dynamic structures on secondary memory

98. Solve SPARQL joins

99. Multi-Index (size tradeoff)

100. Extensions

101. N-Quads