Fast Object Search on Road Networks

1. Fast Object Search on Road Networks Speaker: Ping-Lin Chang 2009/04/12

2. Outline Introduction ROAD Framework Operation Designed Empirical Results Conclusions Fast Object Search on Road Networks

3. Outline Fast Object Search on Road Networks Introduction ROAD Framework Operation Designed Empirical Results Conclusions

4. Introduction Fast Object Search on Road Networks • Location-based services (LBSs) • Blooming nowadays because of • the thriving development of mobile devices • the ubiquitous wireless communication networks • high precision geo-positioning technology • The core application of LBSs • To answer user queries with respect to user-specified location

5. Introduction Fast Object Search on Road Networks • The technological trend of LBSs • Dynamic combination of content and map services • Content providers • Stores, average users, etc. • Map service providers • Google Maps, MapQuest, MS Virtual Earth, etc.

6. Introduction Fast Object Search on Road Networks • Location-dependent spatial queries (LDSQs) • A fundamental data access operations in LBSs • Query the spatial objects (location dependent info.) • k-nearest neighbor (kNN) search • Find the nearest bus station to the conference venue • Range search • Find hotels within 10-minutes walk from the conference venue

7. Introduction Fast Object Search on Road Networks • For an efficient LDSQ processing • Flexibly and efficiently accommodate diverse objects • Efficiently support various LDSQs • Effectively support different distance metrics • However the prior works did not perform well on such an issue

8. Introduction Fast Object Search on Road Networks • Review the deficiency of prior works • Network expansion based approaches • inefficient due to an almost blind scan over entire search space • slow node-by-node expansion towards all directions • Euclidean distance bound approaches • inefficient when paths are not in straight lines • not applicable to other network distance metrics, such as travel time or cost • Solution based approaches • completely impractical due to extremely high preprocessing and storage costs • adapting poorly to other query types, and to object and network changes

9. Introduction Fast Object Search on Road Networks • The proposed system framework • Route Overlay and Association Directory (ROAD) • Two basic operations in processing LDSQs • Network traversal (RO) • Object lookup (AD) • Principle concepts • Rnets, shortcuts, and object abstracts

10. Outline Fast Object Search on Road Networks Introduction ROAD Framework Operation Designed Empirical Results Conclusions

11. ROAD Framework Fast Object Search on Road Networks • Preliminaries • Φ = (N,E) • A road network can be modeled as a weighted graph Φconsisting of a set of nodes N and edges E • A node n ∈ N • represents a road intersection • An edge (n, n’) ∈ E • represents a road segment connecting nodes nand n’ • |n, n’| • denotes the edge distance, which can represent the travel distance, trip time or toll of the corresponding road segment • the value is positive

12. ROAD Framework Fast Object Search on Road Networks • Preliminaries (cont.) • A path P(u, v) • stands for a set of edges connecting nodes u and v and its distance |P(u, v)| =Σ(n, n’)∈P(u, v) |n, n’| • The shortest path SP(u, v) • among all possible paths connecting node u and node v, the one with the shortest distance is referred to as the shortest path • The network distance ||u, v|| between u and v • is the distance of their shortest path SP(u, v) • ||u, v|| = |SP(u, v)|

13. ROAD Framework Fast Object Search on Road Networks • Preliminaries (cont.) • Assume that objects reside on edges (road segments) in a network • objects at nodes (i.e., road intersections) can be treated as they are located at the end of the edges • O(n, n’) • represents a set of objects on edge (n, n’) • δ(o, n) and δ(o, n’) • represents the distance from an object o ∈ O(n, n’) to the nodes n and n’

14. ROAD Framework Fast Object Search on Road Networks • Basic idea • Search space pruning • to skip some search subspaces that do not contain objects of interest • We need • a hint about whether or what objects are on the path • an artifact at n1 connecting n5 • A shortcut between two ending nodes is the shortest path between them

15. ROAD Framework Fast Object Search on Road Networks • Closed paths are usually short in road networks • The performance gained by bypassing closed paths is limited • Regional sub-networks (Rnets) is introduced • each Rnet encloses a subset of edges and is bounded by a set of border nodes

16. ROAD Framework Fast Object Search on Road Networks • Definition 1. Rnet • In a network N = (N, E), an RnetR = (NR, ER, BR) represents a search subspace, where NR, ERand BRstand for nodes, edges and border nodes in R, and • (1) ER ⊆ E • (2) NR = { n | (n, n’) ∈ ER ∨ (n’, n) ∈ ER} • (3) BR = NR ∩ { n | (n, n’) ∈ E’ ∨ (n’, n) ∈ E’ }, where E’ = E − ER

17. ROAD Framework Fast Object Search on Road Networks • Definition 2. Object Abstract • The object abstract of an RnetR,O(R),represents all the objects residing on edges in ER • O(R) = ∪e ∈ ERO(e) • Definition 3. Shortcut • The shortcut, S(b, b’), between border nodes b and b’(∈ BR) of an RnetR bears the shortest path SP(b, b’) and its distance ||b,b’|| • It is noteworthy that the edges that contribute to SP(b, b’) might not necessarily be included in ER

18. ROAD Framework Fast Object Search on Road Networks • Rnet Hierarchy • Large Rnets at the upper levels enclose smaller Rnets at lower levels • At each layer, a network can be viewed as a layer of interconnected Rnets • Original Rnet as the level-0 Rnet • does not have border node and partition it into p1 • At each subsequent level i • partition each Rnet into pi child Rnets • As a result • at a level x (∈ [0, l]), the entire network is fully covered by xΠi=1pi • for an Rnet hierarchy of l levels, there is lΣh=0(hΠi=1pi)

19. ROAD Framework Fast Object Search on Road Networks

20. ROAD Framework Fast Object Search on Road Networks • Definition 4. Rnet partitioning • Partitioning of an RnetR = (N, E, B) where N, E, B are a set of nodes, edges and border nodes and B ⊆ N, forms p child Rnets, R1, R2, · · · Rp where p > 1 and Ri = (Ni, Ei, Bi) • here, N = ∪1≤i≤pNi, E = ∪1≤i≤pEi, B = ∪1≤i≤pBi • Also, the following three conditions must hold • edges of all child Rnets are disjointed • ∀i∀ji ≠ j ⇒ Ei ∩ Ej = ∅ • nodes in an Rnet are connected by edges in the same Rnet • ∀i∀(n, n’) ∈ Ei, n ∈ Ni ∧ n’ ∈ Ni • border nodes in an Rnet are common to its parent Rnet and some of its sibling Rnets • Bi = Ni ∩ [ B ∪ (∪j∈([1,p]−{i})Nj) ]

21. ROAD Framework Fast Object Search on Road Networks • An ideal network partitioning generating • Geometric approach • coarsely partitions a network into two with equal numbers of edges • KL algorithm • fine tunes the two result Rnets by exchanging edges between them • piis set to be power of 2 • recursively apply this binary partitioning until piRnets are formed

22. ROAD Framework Fast Object Search on Road Networks • Important property • Object abstracts and shortcuts are constructed in a bottom-up fashion • Shortcuts of a border node can be determined by adopting Dijkstra’s algorithm to explore paths for all other border nodes in the same Rnet • Shortcuts in Rnets at level i can be calculated based on those in Rnets at level i+1 • Explored shortcuts in Rnets can be used to determine other shortcuts of Rnets in the same level • Some shortcuts that are composed of other shortcuts in the same Rnets can be safely ignored

23. ROAD Framework Fast Object Search on Road Networks

24. Outline Fast Object Search on Road Networks Introduction ROAD Framework Operation Designed Empirical Results Conclusions

25. Operation Deesigned Fast Object Search on Road Networks • Data structures • Route Overlay (RO) • based on definition 4 that the border nodes in parent Rnets are always the border nodes in some of their child Rnets • Association Directory (AD) • based on definition 2 that can examine Rnets quickly and determine whether bypass those Rnets or not

26. Operation Deesigned Fast Object Search on Road Networks Route Overlay

27. Operation Deesigned Fast Object Search on Road Networks

28. Operation Deesigned Fast Object Search on Road Networks Association Directory

29. Operation Designed Fast Object Search on Road Networks Search algorithms

31. Operation Designed Fast Object Search on Road Networks • Object update • Simply changing the records in AD • Network update • Only affects RO • Filtering-and-refreshing approach is performed • in the filtering step, shortcuts that may be affected by the change are identified • in the refreshing step, the identified shortcuts are then updated

32. Operation Designed Fast Object Search on Road Networks • Network update (cont.) • Edge distance increased • only those shortcuts that cover (n, n’) might become invalid and need to be refreshed • Edge distance decreased • may contribute to paths shorter than some expisting shortcuts

33. Operation Designed Fast Object Search on Road Networks • Change of network structure • Addition of a new edge (n, n’) • judging whether the nodes n and n’ are in the same Rnet • Deletion of an existing edge (n, n’) • judging whether either n or n’ is border node • Incorporating with the schema of network update

34. Outline Fast Object Search on Road Networks Introduction ROAD Framework Operation Designed Empirical Results Conclusions

35. Empirical Results Fast Object Search on Road Networks • Experimental environment • Three real road network datasets, CA, NA, and SF • Organize network nodes by CCAM • Run on Linux 2.6.9 servers with Intel Xeon 3.2GHz CPU • All algorithms implemented in GNU C++ • All indices are stored on disk • the page size is fixed at 4KB • memory cache of 50 pages

36. Empirical Results Fast Object Search on Road Networks Evaluation parameters

37. Empirical Results Fast Object Search on Road Networks Index construction time and index size

38. Empirical Results Fast Object Search on Road Networks Index construction time and index size

39. Empirical Results Fast Object Search on Road Networks Index update time

40. Empirical Results Fast Object Search on Road Networks Index update time

41. Empirical Results Fast Object Search on Road Networks Query performance - kNN query

42. Empirical Results Fast Object Search on Road Networks Query performance - range query

43. Empirical Results Fast Object Search on Road Networks Impact of Rnet hierarchy level (l)

44. Outline Fast Object Search on Road Networks Introduction ROAD Framework Operation Designed Empirical Results Conclusions

45. Conclusions Fast Object Search on Road Networks • The proposed algorithm achieves clean separation between objects and network • Better system flexibility and extensibility • The strategy of search space pruning • Substantially speeds the object search • Range and kNN query for common LDSQs • Shows high performance • Incremental framework maintenance techniques • Update information in both efficiency and effectiveness

46. Thank You! Q A & Fast Object Search on Road Networks