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Efficient and Continuous Skyline Monitoring in Two Tier Streaming Settings

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[Paper Study] …

[Paper Study]
Hua Lu, et al., Aalborg University, Denmark
2013 Elsevier Volume 38.

Published in: Technology, Business

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  • 1. Elsevier: Information Systems, Volume 38, 2013 Author: Reported by: Hua Lu, et al. Aalborg University, Denmark Tzu-Li Tai National Cheng Kung University, Taiwan High Performance Parallel and Distributed Systems Lab
  • 2. A. Background Knowledge B. The Problem: Efficient Continuous Skyline Monitoring C. The Approach: Two-Phase Monitoring D. Personal Feedback HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 3. Background Knowledge Before anything else…… What is a skyline? HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 4. Background Knowledge 𝑡𝑝 𝐴 = 𝑡𝑝 𝐵 = 𝑝1 , 𝑝2 , … , 𝑝 𝑛 𝑝1 , 𝑝2 , … , 𝑝 𝑛 Definition of “tuple A dominates tuple B”: A is not worse than B for all attributes, and A is better than B for at least one attribute Notation: 𝑡𝑝 𝐴 ≻ 𝑡𝑝 𝐵 HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 5. Background Knowledge Price and Rating of Hotels 6 5 Rating 4 𝑡𝑝 = 3 𝑝𝑟𝑖𝑐𝑒, 𝑟𝑎𝑡𝑖𝑛𝑔 2 1 0 0 1000 2000 3000 4000 5000 6000 7000 Price HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 6. Background Knowledge Price and Rating of Hotels 6 A 5 𝑡𝑝 𝐴 = 5, 4000 𝑡𝑝 𝐵 = 2.5, 5000 Rating 4 3 B ⇒ 𝑡𝑝 𝐴 ≻ 𝑡𝑝 𝐵 2 1 0 0 1000 2000 3000 4000 5000 6000 7000 Price HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 7. Background Knowledge Price and Rating of Hotels 6 5 A Rating 4 𝑡𝑝 𝐴 = 4, 1500 𝑡𝑝 𝐵 = 4, 4500 B 3 ⇒ 𝑡𝑝 𝐴 ≻ 𝑡𝑝 𝐵 2 1 0 0 1000 2000 3000 4000 5000 6000 7000 Price HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 8. Background Knowledge Price and Rating of Hotels 6 5 4 Rating 𝑡𝑝 𝐴 = 2, 2000 𝑡𝑝 𝐵 = 4, 4500 B 3 A 2 1 0 0 1000 2000 3000 4000 5000 6000 7000 ⇒ 𝑡𝑝 𝐴 ⊁ 𝑡𝑝 𝐵 ⇒ 𝑡𝑝 𝐵 ⊁ 𝑡𝑝 𝐴 Price HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 9. Background Knowledge Price and Rating of Hotels 6 5 Rating 4 Definition of Skyline: 3 The subset of all tuples that are not dominated by any other tuple. 2 1 0 0 1000 2000 3000 4000 5000 6000 7000 Price HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 10. Background Knowledge Price and Rating of Hotels 6 5 Rating 4 Definition of Skyline: 3 The subset of all tuples that are not dominated by any other tuple. 2 1 0 0 1000 2000 3000 4000 5000 6000 7000 Price HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 11. Background Knowledge Now that we know what a skyline is…… What is a two-tier streaming setting for continuous skyline monitoring? HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 12. Background Knowledge Central Server (Query Interface) Data Sites HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 13. Background Knowledge HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 14. The Problem: Efficient Continuous Skyline Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 15. The Problem: Efficient Continuous Skyline Monitoring Problem Statement: Concerning a geographically distributed computing environment characterized by a central server and multiple data sites, there is a demand for a more efficient method for continuous skyline monitoring. HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 16. The Approach: Two-Phase Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 17. The Approach: Two-Phase Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 18. The Approach: Two-Phase Monitoring Initialization phase • • Obtain initial query result by merging all local skylines Categorize all tuples based on their membership in the local skyline and global skyline Maintenance phase • Continuously monitor global skyline by referring to formalized cases of possible skyline changes HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 19. The Approach: Two-Phase Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 20. The Approach: Two-Phase Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 21. The Approach: Two-Phase Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 22. The Approach: Two-Phase Monitoring Initialization Phase 𝑆𝐾 𝑙 = {𝑡𝑝1 , 𝑡𝑝3 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝1 , 𝑡𝑝3 } 𝑆𝐾 𝑓𝑝 = {∅} 𝑆𝐾 𝑔 = (1, 𝑡𝑝1 , 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} Site 1 𝑆𝐾 𝑙 = {𝑡𝑝1 , 𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝2 } 𝑆𝐾 𝑓𝑝 = {𝑡𝑝1 , 𝑡𝑝3 } 𝑆𝐾 𝑙 = {𝑡𝑝1 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝1 } Site 2 𝑆𝐾 𝑓𝑝 = {∅} Site 3 HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 23. The Approach: Two-Phase Monitoring HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 24. The Approach: Two-Phase Monitoring 𝒕𝒑 Maintenance Phase 𝑡𝑝 𝑡 → 𝑡𝑝(𝑡′) ⟹ 𝒕𝒑 𝒕 ∈ {𝑵𝑺, 𝑭𝑺, 𝑮𝑺} Site 1 ⟹ Dominance Relationship between 𝒕𝒑(𝒕) and 𝒕𝒑(𝒕′) Site 2 Site 3 HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 25. The Approach: Two-Phase Monitoring Question 1. Is 𝑡𝑝(𝑡′) not dominated by any global skyline point? If yes, 𝑡𝑝 𝑡 ′ is in the global skyline. Question 2. Does 𝑡𝑝 𝑡 ′ dominate any global skyline point? If yes, the dominated skyline point will be eliminated from the set of skyline points. Question 3. 𝑡𝑝(𝑡) was a global skyline point. If 𝑡𝑝(𝑡) solely dominates some non-skyline point, does 𝑡𝑝(𝑡′) stop dominating them? If yes, the previously non-skyline point will enter the set of skyline points. Question 4. Does 𝑡𝑝 𝑡 ′ stop being a false-positive global skyline points since it is now dominated by some other point? If yes, remove 𝑡𝑝 from the false-positive set from the data site side. HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 26. The Approach: Two-Phase Monitoring 𝑆𝐾 𝑙 = {𝑡𝑝1 , 𝑡𝑝3 } 𝒕𝒑 𝟐 is updated at 𝒕 = 𝒕′ 𝑆𝐾 𝑙𝑔 = {𝑡𝑝1 , 𝑡𝑝3 } 𝑡𝑝2 ∈ ? 𝑆𝐾 𝑓𝑝 = {∅} Site 1 𝒕𝒑 𝟐 ∈ 𝑵𝑺 Dominance? 𝒕𝒑 𝟐 𝒕 ∽ 𝒕𝒑 𝟐 (𝒕′) ⇒ 𝑪𝒂𝒔𝒆 𝟏 𝑆𝐾 𝑔 = (1, 𝑡𝑝1 , 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 27. The Approach: Two-Phase Monitoring 𝑆𝐾 𝑙 = {𝑡𝑝1 , 𝑡𝑝2 , 𝑡𝑝3 } ⇒ 𝑪𝒂𝒔𝒆 𝟏 𝑆𝐾 𝑙𝑔 = {𝑡𝑝1 , 𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑓𝑝 = {∅} Site 1 Consider Q1 and Q2 Q1: 𝑡𝑝2 ≻ 𝑡𝑝1 & 𝑡𝑝2 ~ 𝑡𝑝3 YES! 𝑆𝐾 𝑔 = (1, 𝑡𝑝1 , 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 28. The Approach: Two-Phase Monitoring 𝑆𝐾 𝑙 = {𝑡𝑝1 , 𝑡𝑝3 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝1 , 𝑡𝑝3 } 𝑆𝐾 𝑓𝑝 = {∅} Site 1 ⇒ 𝑪𝒂𝒔𝒆 𝟏 Consider Q1 and Q2 Q1: 𝑡𝑝2 ≻ 𝑡𝑝1 & 𝑡𝑝2 ~ 𝑡𝑝3 YES! Q2: 𝑡𝑝2 ≻ 𝑡𝑝1 YES! 𝑆𝐾 𝑔 = (1, 𝑡𝑝1 , 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 29. The Approach: Two-Phase Monitoring 𝑆𝐾 𝑙 = {𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑓𝑝 = {∅} Site 1 ⇒ 𝑪𝒂𝒔𝒆 𝟏 Consider Q1 and Q2 Q1: 𝑡𝑝2 ≻ 𝑡𝑝1 & 𝑡𝑝2 ~ 𝑡𝑝3 YES! Q2: 𝑡𝑝2 ≻ 𝑡𝑝1 YES! 𝑆𝐾 𝑔 = (1, 𝑡𝑝1 , 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 30. The Approach: Two-Phase Monitoring 𝑆𝐾 𝑙 = {𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑓𝑝 = {∅} Site 1 ⇒ 𝑪𝒂𝒔𝒆 𝟏 Consider Q1 and Q2 Q1: 𝑡𝑝2 ≻ 𝑡𝑝1 & 𝑡𝑝2 ~ 𝑡𝑝3 YES! Q2: 𝑡𝑝2 ≻ 𝑡𝑝1 YES! 𝑆𝐾 𝑔 = { 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 31. The Approach: Two-Phase Monitoring 𝑆𝐾 𝑙 = {𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑙𝑔 = {𝑡𝑝2 , 𝑡𝑝3 } 𝑆𝐾 𝑓𝑝 = {∅} Site 1 ⇒ 𝑪𝒂𝒔𝒆 𝟏 Consider Q1 and Q2 Q1: 𝑡𝑝2 ≻ 𝑡𝑝1 & 𝑡𝑝2 ~ 𝑡𝑝3 YES! Q2: 𝑡𝑝2 ≻ 𝑡𝑝1 YES! 𝑆𝐾 𝑔 = (1, 𝑡𝑝2 , 1, 𝑡𝑝3 , 2, 𝑡𝑝2 , (3, 𝑡𝑝3 )} HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 32. Personal Feedback I/O rate is increased dramatically The performance of the proposed approach still remains arguable due to the massive increase of I/O rates (as opposed to the traditional two-tier streaming setting). Keeping all skyline datasets in main-memory throughout the whole maintenance phase is a considerable option, but this will bring up fault-tolerance issues. HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 33. Personal Feedback Critical Path HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 34. Personal Feedback Critical Path HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 35. Personal Feedback Further enhancing real-time response for two-tier streaming settings Remote distributed shared memory datasets across data sites (clouds)? Is it possible? HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU
  • 36. HPDS Lab, Institute of Computer and Communication Engineering, Electrical Engineering - NCKU