The document discusses multi-thematic spatial databases for efficiently storing, accessing, processing, and visualizing large volumes of geospatial data from multiple sources and sensors. It describes experience with designing databases to handle terabytes of temporal, multi-sensor data using spatial indexing. The goals are a unified approach for multi-thematic data storage, efficient data handling, and enabling searches across time, space and attributes while incorporating visualizations.

1. Multi-Thematic Spatial Databases
Experience designing and implementing
Dr. Conor Mc Elhinney
Dr. Paul Lewis
Postdoctoral Researcher
Mobile Mapping Group

2. What we do
Store

3. What we do
Store
Access

4. What we do
Store
Access
Process

5. What we do
Store
Access
Process
Visualise

6. What we do
Store
Access
Voluminous
Geospatial Data
Process
Visualise

7. Mobile Mapping Systems Group
1 Senior Researcher
2 Post-docs
2 PhDs
Computer Science, GIS, Surveying

8. Mobile Mapping Systems Group

9. Mobile Mapping Systems Group

10. Mobile Mapping Systems Group

11. Mobile Mapping Systems Group

12. i2maps Group
1 Senior Researcher
1 Post-doc
2 PhDs +1 PHD GeoCrowd
Computer Science, Maths, GIS
Dr. Alexei Pozdnoukhov, Dr. Christian Kaisler
Fergal Walsh

13. Learning from data Streams
i2maps What we do at NCG:
Scalable methods of spatial analytics

14. Learning from data Streams
i2maps What we do at NCG:
Scalable methods of spatial analytics
Machine learning and data mining

15. Learning from data Streams
i2maps What we do at NCG:
Scalable methods of spatial analytics
Machine learning and data mining
Stream Handler
{x, y} M
f (.) i K (., xi )
Analysis and i 1
Modeling
new
{ i} (.), ( xi ) H
Dictionary of models MapReduce
xOLD

16. Learning from data Streams
i2maps What we do at NCG:
Scalable methods of spatial analytics
Machine learning and data mining
Distributed approaches to spatial statistics
Stream Handler
{x, y} M
f (.) i K (., xi )
Analysis and i 1
Modeling
new
{ i} (.), ( xi ) H
Dictionary of models MapReduce
xOLD

17. What we have experience with
Handling and proccessing of TBs of

18. What we have experience with
Handling and proccessing of TBs of
Multi-thematic data

19. What we have experience with
Handling and proccessing of TBs of
Multi-thematic data
Temporal data

20. What we have experience with
Handling and proccessing of TBs of
Multi-thematic data
Temporal data
Multi-sensor data

21. What we have experience with
Handling and proccessing of TBs of
Multi-thematic data
Temporal data
Multi-sensor data
using spatial information

22. Data Handling

23. Data Handling

24. LiDAR
A laser scanning tech, laser radar

25. LiDAR
A laser scanning tech, laser radar
> 20Gbs an hour

26. LiDAR
A laser scanning tech, laser radar
> 30Gbs an hour after geocoding

27. LiDAR
A laser scanning tech, laser radar
> 30Gbs an hour after geocoding
> 6 attributes output from scanner

28. LiDAR
A laser scanning tech, laser radar
> 30Gbs an hour after geocoding
> 6 attributes output from scanner
Between 1 pt/m2 and 2000 pt/m2

29. LiDAR

30. LiDAR

31. LiDAR

32. LiDAR

33. LiDAR

34. Imagery
Developing trend to store as videos
/ blobs

35. Imagery
Developing trend to store as videos
/ blobs
Store metadata in SDB

36. Imagery
Developing trend to store as videos
/ blobs
Store metadata in SDB
Using HTML5 or queries can link
to frames

37. User Generated
Comments

38. User Generated
Comments Video / Imagery

39. User Generated
Comments Video / Imagery
Opinion

40. User Generated
Comments Video / Imagery
Opinion

41. Twitter

42. Twitter

43. Twitter
25%
contain links

44. Facebook
500 Million Active
Users

45. Facebook
250 Million Active
Mobile Users

46. Facebook
10 Million pieces of
content per day

47. Hard Drive Capacity

48. Hard Drive Capacity
Capacity is increasing
linearly

49. CPU vs HD speed
CS111 UCLA 2006

50. CPU vs HD speed
We can process more
than we can store
CS111 UCLA 2006

51. CPU vs HD speed
IDC - The Diverse and Exploding Digital Universe
CS111 UCLA 2006

52. What next
Process the data in real time

53. What next
Process the data in real time
Extract or compress to find a
model for the relevant data

54. What next
Process the data in real time
Extract or compress to find a
model for the relevant data
Store this model for future
processing

55. What next
Process the data in real time
Extract or compress to find a
model for the relevant data
Store this model for future
processing
Leads to the problem of what
do we model and store?

56. Enabling GeoSpatial Data

57. Processing data streams
Human activity on geo-referenced
communication networks

58. Processing data streams
Human activity on geo-referenced
communication networks

59. Processing data streams
Human activity on geo-referenced
communication networks
At least two categories we need to
understand:

60. Processing data streams
Human activity on geo-referenced
communication networks
At least two categories we need to
understand:
Dynamics of links

61. Processing data streams
Human activity on geo-referenced
communication networks
At least two categories we need to
understand:
Dynamics of links
Activity level at nodes

62. Enabling data speak for themselves
Air Quality Sensor Weather Measurements VGI Feed (e.g. Twitter) Surveillance Camera
SMS Web Page XML Video
Push Polling Stream Stream
Data Receiver Data Crawler Stream Handler Stream Handler
Static Data
Static Data
Static Data Analysis and
Spatial Database
Modeling
Spatio-Temporal Data
i2maps
Web Service
KML/CSV/etc GeoJSON Spatio-Temporal Queries
Interactive Spatio-Temporal
Information Visualiser

63. Enabling data speak for themselves
Air Quality Sensor Weather Measurements VGI Feed (e.g. Twitter) Surveillance Camera
SMS Web Page XML Video
Push Polling Stream Stream
Data Receiver Data Crawler Stream Handler Stream Handler
Spatial
Static Data
Static Data
Static Data Analysis and
Database Spatial Database
Dictionary of models Modeling
Spatio-Temporal Data
i2maps
Web Service
KML/CSV/etc GeoJSON Spatio-Temporal Queries
Interactive Spatio-Temporal
Information Visualiser

64. Storage

65. What exists
Files / DBs / SDBs

66. What exists
Files / DBs / SDBs
Files still extremely common

67. What exists
Files / DBs / SDBs
Files still extremely common
SDBs are what is needed

68. What exists
Files / DBs / SDBs
Files still extremely common
SDBs are what is needed
Multi-source, sensor, type data

69. Our Aims
Unified approach to storing multi-
thematic data

70. Our Aims
Unified approach to storing multi-
thematic data
Efficient data upload / access/
storage

71. Our Aims
Unified approach to storing multi-
thematic data
Efficient data upload / access/
storage
Searchable in Time/ Space / by
Attributes

72. Our Aims
Unified approach to storing multi-
thematic data
Efficient data upload / access/
storage
Searchable in Time/ Space / by
Attributes
Incorporating Visualisations into
all solutions

73. Our hardware
3 Processing Servers
8 Intel Xeons, 2.1- 2.8 GHz
72 GBs RAM

74. Our hardware
3 Processing Servers
8 Intel Xeons, 2.1- 2.8 GHz
72 GBs RAM
1 Storage Server
7TBs Raided Drives

75. Our Developed Systems
LiDAR / Image
based SDB

76. Our Developed Systems
LiDAR / Image
based SDB
GeoComputation
Platform

77. Database storage experience
Optimisation of upload of large (GBs)
spatial files to SDB.

78. Database storage experience
Database optimisation to suit system
architecture

79. Database storage experience
Storage of multiple data
types/sources

80. Watch out for
Spatial Index size V RAM

81. Watch out for
Spatial Index size V RAM
Expected no. of concurrent users

82. Watch out for
Spatial Index size V RAM
Expected no. of concurrent users
HD capacity V daily data
throughput