Real Time Semantic Analysis of Streaming Sensor Data
•Download as PPTX, PDF•
2 likes•560 views
Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001. More at: http://wiki.knoesis.org/index.php/SSW Dissertation Advisor: Prof. Amit Sheth
Recommended
Recommended
More Related Content
What's hot
What's hot (16)
Viewers also liked
Viewers also liked (20)
Similar to Real Time Semantic Analysis of Streaming Sensor Data
Similar to Real Time Semantic Analysis of Streaming Sensor Data (20)
Recently uploaded
Recently uploaded (20)
Real Time Semantic Analysis of Streaming Sensor Data
- 2. x WEB DATA evolved over time Real-Time Sensor, Social, Multi-media data 2010’s Dynamic User Generated Content 2000’s Static Document and files 1990’s 2
- 3. x Properties of Streaming Data Huge Volume Rapid Continuous Information Overload!! Heterogeneous 3
- 4. x Some Statistics “A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data” - GigaOmni Media “Sensors Networks will produce 10-20 times the amount of generated by social media in the next few years” - GigaOmni Media “More data has been created in the last three years than in all the past 40,000 years” - Teradata Solution - “Meaningfully summarize this data” 4
- 5. 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. From Sensor Streams to Feature Streams in Real Time HarshalPatni Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University, Dayton, OH Part of Semantic Sensor Web @ Kno.e.sis
- 6. x Outline Introduction Architecture Linked Sensor Data Feature Streams Demonstration 6
- 7. x Domain Weather Domain Features Blizzard Flurry RainStorm RainShower 7
- 8. x Explaining the title Background Knowledge Blizzard Rain Storm ABSTRACTION Huge amount of Raw Sensor Data Features representing Real-World events 8
- 9. x Types of Abstractions Summarization across Thematic Dimension Summarization over the Temporal Dimension 9
- 10. x Types of Abstractions Summarization across Thematic Dimension Select Join Background Knowledge Analyze Features representing Real-World Events 10
- 11. x An example problem? 11 “Find the sequence of weather events observed near Dayton James Cox Airport between Jan 13th and Jan 18th?” Spatial Thematic Temporal Technologies required - Linked Sensor Data Feature Streams
- 12. x Outline Introduction Architecture Linked Sensor Data Feature Streams Demonstration 12
- 13. x System Architecture 13
- 14. x Outline Introduction Architecture Linked Sensor Data Feature Streams Demonstration 14
- 15. 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Technology1: Linked Sensor Data Find the sensor around Dayton James Cox Airport? Extract Data for the sensor near Dayton James Cox Airport? Harshal Patni, Cory Henson, Amit Sheth, 'Linked Sensor Data,' In: Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.
- 16. Sensor Discovery Application Weather Station ID Current Observations from MesoWest Weather Station Coordinates Weather Station Phenomena MesoWest – Project under Department of Meteorology, University of UTAH GeoNames – Geographic dataset 16
- 17. What is Linked Sensor Data Weather Sensors Sensor Dataset GPS Sensors Satellite Sensors Camera Sensors 17
- 18. What is Linked Sensor Data Recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Web using URIs and RDF GeoNames Dataset RDF – language for representing data on the Web locatedNear Sensor Dataset Publicly Accessible 18
- 19. Linked Sensor Data on LOD - First Sensor Dataset on LOD - Among the largest dataset on LOD 19
- 21. Average 5 sensors/weather station
- 22. Spatial attributes of the weather station
- 24. Observations generated by sensors described in LinkedSensorDataset20
- 25. Data Generation Workflow O&M2RDFCONVERTER 21
- 26. Workflow – Phase 1 22
- 27. Workflow – Phase 2 OGC (Open Geospatial Consortium) standard for encoding sensor observations 23
- 28. Workflow – Phase 3 W3C SSN ontology Ontology – formal representation of knowledge by a set of concepts and relationship between those concepts
- 29. Workflow – Phase 3 Figure 1: System Components and Architecture
- 30. Workflow – Phase 4 Open Source RDF store by OpenLink Software for storing RDF data PUBBY Linked Data Front End
- 32. MesoWest
- 33. Static + Dynamic
- 34. 20,000+ systems
- 35. MesoWest
- 36. ~Static
- 38. Geonames
- 40. 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Technology 2: Feature Streams What feature is currently being detected by sensor near Dayton Airport? Harshal Patni, Cory Henson, Amit Sheth, Pramod Ananthram, ‘From Real Time Sensor Streams to Real Time Feature Streams,' Kno.e.sis Technical Report, January 2011.
- 41. x System Architecture Streams Integration based on feature composition Integrated Stream Analysis to check if the feature is being detected 30
- 42. x Feature Composition 31
- 43. x System Capability 32
- 44. x System Feature Integration SELECT JOIN 33
- 45. x System Architecture Integrated Stream Analysis to check if the feature is being detected 34
- 46. x Feature Definition RainStorm = HighWindSpeed(above 35mph) AND Rain Precipitation AND Temperature(greater than 32F) SPARQL query for RainStorm Temperature Rain Precipitation WindSpeed 35 Rain Storm NOAA definition
- 47. x Feature Analysis RDF Feature Stream 36
- 48. x Revisiting Abstractions Summarization across Thematic Dimension Select Join Background Knowledge Analyze Features representing Real-World Events 37
- 50. MesoWest
- 51. Static + Dynamic
- 52. 20,000+ systems
- 53. MesoWest
- 54. ~Static
- 56. Geonames
- 58. x Outline Introduction Architecture Linked Sensor Data Feature Streams Demonstration 40
- 59. x Demo 41 Feature Streams Demo http://knoesis1.wright.edu/EventStreams
- 61. WORKSHOP PAPERS Harshal Patni, Satya S. Sahoo, Cory Henson, Amit Sheth, Provenance Aware Linked Sensor Data, 2nd Workshop on Trust and Privacy on Social and Semantic Web,Co-Located with ESWC, Heraklion Greece, May 30th - June 3rd 2010 Harshal Patni, Cory Henson, Amit Sheth, Linked Sensor Data, In: Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010 TECHNICAL REPORT Harshal Patni, Cory Henson, Amit Sheth, and Pramod Ananthram. From Real Time Sensor Streams to Real Time Feature Streams, Kno.e.sis Center Technical Report, December 2009 Joshua Pschorr, Cory Henson, Harshal Patni, and Amit Sheth. Sensor Discovery on Linked Data, Kno.e.sis Center Technical Report, December 2009 JOURNAL PAPER (In Progress) Semantic Sensor Web: Design and Application towards weaving a meaningful sensor web Publications 43
- 62. Thank You Committee 44
- 63. Thank You Semantic Sensor Web 45
- 64. Demos, Papers and more at: http://wiki.knoesis.org/index.php/SSW Semantic Sensor Web @ Kno.e.sis QUESTIONS 46
Editor's Notes
- Good Morning Everyone. My name is Harshal Patni and I am here to present my thesis on Streaming Sensor Data but Before we begin lets have a look at how web data evolved over time
- Social media is the dominant source of streaming data now, however in future sensors would …Data needs to be reduced
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- Move this slide above
- Remove the precipitation (in) and also show the general streamAdd the image taken on the phoneRemove the stuff on left when you show select, join and analyze`
- Remove the precipitation (in) and also show the general streamAdd the image taken on the phoneRemove the stuff on left when you show select, join and analyze`
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsAdd linked Sensor Data when highlightThe output of these phases is called LSD and its added on LOD
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- Get all sensors using well known location names – Problem to be solveAssociate sensor descriptions to well know locations.
- Get all sensors using well known location names – Problem to be solve
- Say the numbers in the table
- RDF because of LOD
- Highlight the important points in MesoWest DataThe sensor data file just 3 linesMapping file - shorten
- Emphasize semantically annotated O&MAnd its an XMLTry to replace the cory/weather.owl
- Use the ssn ontologyAdd the image of ontology for the (Sensor Ontology)http://www.w3.org/2005/Incubator/ssn/wiki/Report_Work_on_the_SSN_ontology
- Add in block letters saying this is semantically annotated XML and RDF
- Add Pubby to show derefenced dataPubby should be large to show what it is
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- Replace Air Temperature with Non Freezing Temperature
- Replace Rain Precipitation with PrecipitationSame with airtempearure - temperature
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- Highlight the query with 3 boxes to show the temp,windspeed and precipitation streamHighlight the feature results too
- Talk about the observations and features storage
- Remove the precipitation (in) and also show the general streamAdd the image taken on the phoneRemove the stuff on left when you show select, join and analyze`
- Linked Data explodes
- To walk through the implementation, lets take a sample questionThe question might look trivial, but it contains 3 important partsMarket the datasets we added on LOD
- Linked Data explodes
- % of FeaturesThrow the text on the top for the statisticsMiddle of storm and hence we have 70 % data reductionElse it would be more