The document discusses a linked-data model aimed at representing semantic sensor data streams to enhance the efficiency of queries on large-scale annotated data. It proposes a solution that centralizes static attributes and facilitates their association with dynamic observations, utilizing a clustering approach for storage and retrieval. Evaluation focuses on data representation, identification, and the efficiency of clustering methods in various scenarios.

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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