These slides present the paper entitled "A linked data model for semantic sensor stream". It does not include all the details, but an effort is made to show the main contribution of the paper.

1. A Linked-data Model For
Semantic Sensor Streams
Authors: P. Barnaghi et al.
Presenter: Haroon Rashid
113/03/15

2. Problem
• Describe semantically sensor data streams
– Continuous observations and measurements
• Semantic representation of data streams
– Metadata increases the size of transferred data to
a greater extent
• Efficient Semantic Queries for large-scale
annotated data
213/03/15

3. Solution
• Use Linked data concept
– Store static, common attributes at one place
– Provide links to static data wherever needed
313/03/15

4. Approach
• Each observation is associated with
413/03/15

5. Approach
• Each observation is represented as
5
GEOHASH
SWEET ONT.
13/03/15

6. RDF Representation
Normal Representation Linked representation
613/03/15

7. Data stream Representation
Static Source Mobile Source
713/03/15

8. Publication, Storage, Access
Architecture
813/03/15

9. Data identification
9
No UNIQUE representation to identify data item within a stream
13/03/15

10. Data Distribution
• Clustering approach
– Store data on distributed repositories
– Results in fast query and resolution mechanisms
• Using K-means Clustering
– Involves both storing and fetching data in/from
different clusters
– Needs extensive training
1013/03/15

11. Evaluation
11
Size of different data stream representation in three different ways
13/03/15

12. Questions
1. Can we improve data identification which will
enhance data resolution/composition?
2. Size of the stream stream/series?
1. Depends on application requirements,
bandwidth, caching, freshness
3. On clustering, query efficiency not shown
4. Can we use sample data for showing
efficiency of a technique in paper?
1213/03/15

13. Cont..
5. Efficiency of clustering for mobile scenarios
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