Data Modeling in Telecoms - GraphConnect NY 2013

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In November 2013 Craig presented "Data Modeling in Telecoms" at the GraphConnect 2014 conference in New York. The video for the talk can be seen at http://vimeo.com/79390660.

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  • Two and a half years ago I was on safari in South Africa and I took this photograph. This is my nephew standing on the edge of a 100m high cliff
    Why is he doing that?
    The view is great! You can see families of giraffe in the valley.
    It is exciting, kids are attracted to excitement.
    My talk is about finding excitement in development by playing on the edge. Taking risks and thereby finding a more efficient route.
    Unless you fall off the edge, that is!
    (Two types of edge – cliffs and interfaces)
  • We made a key, revolutionary change. Look, the map is still there and the scripting console is still there, but what is that ferris wheel to the top right?
    Ported the data structures onto the graph database, Neo4j, made by Neo Technologies right here in Malmö. This is one of the very first screenshots of the prototype results of getting the network data onto neo4j.
  • The addition of neo4j lead to a revolution in thinking. Instead of data and analysis and reporting being different levels or different structures, they are all just graph structure in the database. This means you can analyse the results of previous analyses. Just keep adding structure to the database.
    All the views are direct maps onto the database:
    GIS traverses gis-next edges
    Tree traverses child edges
    Charts traverse aggregation-child edges
    Property tables display node properties
    Neo4j has added a whole new angle to the use of the word 'EDGE' :-)
  • Data Modeling in Telecoms - GraphConnect NY 2013

    1. 1. Data Modeling in Telecoms
    2. 2. AmanziTel / DingLi ● Customer Experience Management ● Cellular Telephone Networks ● Neo4j OEM since 2009 ● AmanziTel in Sweden ● DingLi in China
    3. 3. Who are we?
    4. 4. Playing on the Edge
    5. 5. AWE-2009 Neo4j – graph database ● extremely high performance on deep traversals and mining of complex data, ● rapid schema evolution for changing business requirements, and ● simplified development through perfect match between domain model and database schema.
    6. 6. AWE-2009
    7. 7. Splash-Neo4j = a1 + ' ' + a2 = sum(a1..a3)
    8. 8. Neo4j Spatial 2010
    9. 9. Critical Infrastructure 2011-2012
    10. 10. Data Collection Process
    11. 11. Graph Data Models ● Cellular Network Topology Tree ● Geographic/Spatial Index ● OpenStreetMap ● User Access Management ● Event Log Correlation ● Network Management / Critical Infrastructure ● Business Intelligence / Statistics
    12. 12. Cellular Network Topology
    13. 13. Cellular Network Topology ID Site Latitude Longitude 1 ABC 55.678 12.567 2 XYZ 55.890 12.123 3 PRQ 55.543 12.890 ID Sector SiteID Azimuth Beamwidth 1 1 1 0 100 2 2 1 120 100 3 3 1 240 100 4 1 2 0 90 5 2 2 120 110 6 3 2 240 90 7 1 3 0 180 8 2 3 180 180 O(N) O(ln(N)) O(1)
    14. 14. = a1 + ' ' + a2 = sum(a1..a3)
    15. 15. Spatial Index
    16. 16. Spatial Index
    17. 17. Spatial Index
    18. 18. OpenStreetMap
    19. 19. OpenStreetMap
    20. 20. OpenStreetMap
    21. 21. OpenStreetMap OSM RTree namic ayers Dynamic Layers
    22. 22. Data Mining OSM timestamp > 1207014810000 and timestamp < 1208310810000 and ( user = 'Zenon' or user = 'tomasCY' or user = 'muffu' or user = 'dcp' or user = 'cartOMike' or user = 'djanda' or user = 'Peter14' or user = 'toaster' or user = 'user_7363' or user = 'lyx' )
    23. 23. User Access Management
    24. 24. Event Log Correlation
    25. 25. Event Log Correlation
    26. 26. Event Log Correlation gps call sms
    27. 27. Network / Critical Infrastructure
    28. 28. Business Intelligence
    29. 29. Business Intelligence
    30. 30. Performance Implications ● Know your domain ● Know your queries ● Examples

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