Evaluating queries over route collections
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Evaluating Queries over Route Collections. Panagiotis Bouros, PhD defense. Outline. Introduction Route collections examples Query evaluation challenges Evaluating path queries Dynamic Pickup and Delivery with Transfers Most Trusted Near Shortest Path Conclusions Future work.

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Evaluating queries over route collections

Evaluating Queries over Route Collections

Panagiotis Bouros, PhD defense


Outline

Outline

  • Introduction

    • Route collections examples

    • Query evaluation challenges

  • Evaluating path queries

  • Dynamic Pickup and Delivery with Transfers

  • Most Trusted Near Shortest Path

  • Conclusions

  • Future work

PhD defense


Routes as data

Routes as data

  • Several applications involve storing and querying large volumes of sequential data

  • Route, a sequence of spatial locations

    • POIs, waypoints etc.

  • Route collection

    • Routes as first-class citizens

    • Frequently updated

      • New routes added

      • Existing routes deleted or modified

PhD defense


Example 1 sightseeing and activities

Example 1: Sightseeing and activities

  • People visit Athens

    • GPS devices

    • Track sightseeing

    • Touristic routes

  • Route collections online

    • www.ShareMyRoutes.com

    • www.TravelByGPS.com

  • Updates

    • Add new interesting routes

    • Remove existing routes, not interesting any more

PhD defense


Example 1 sightseeing and activities1

Example 1: Sightseeing and activities

  • Traditional graph queries

    • REACH: Is there a sequence of POIs from Academy to Zappeion?

    • PATH: Find a sequence of POIs from Academy to Zappeion

    • PATH more general

  • Graph-based solution

    • Searching

      • Low maintenance cost

      • Slow

    • Compressing TC

      • Fast

      • High maintenance cost

  • This thesis

    • Combine pros and cons

    • Reachability within routes

PhD defense


Example 1 sightseeing and activities2

Example 1: Sightseeing and activities

  • Traditional graph queries

    • REACH: Is there a sequence of POIs from Academy to Zappeion?

    • PATH: Find a sequence of POIs from Academy to Zappeion

    • PATH more general

  • Graph-based solution

    • Searching

      • Low maintenance cost

      • Slow

    • Compressing TC

      • Fast

      • High maintenance cost

  • This thesis

    • Combine advantages

    • Reachability within routes

PhD defense


Example 2 pickup and delivery

Example 2: Pickup and delivery

  • A courier company offering pickup and delivery services

  • Static plan

    • Set of requests

    • Transfers between vehicles

    • Collection of vehicles routes

  • Pickup and Delivery with Transfers

    • Create static plan

  • Updates

    • Ad-hoc requests

    • Modify vehicle routes to satisfy new requests

PhD defense


Example 2 pickup and delivery1

Example 2: Pickup and delivery

  • Query

    • Pickup object from ns and delivery at nt

    • Minimize company’s expenses

    • dynamic Pickup and Delivery with Transfers

  • Non-graph solution

    • Two-phase local search

  • This thesis

    • First work target dPDPT

    • Cost metrics

      • Company’s viewpoint, extra traveling or waiting time

      • Customer’s viewpoint, delivery time

    • Dynamic two-criterion shortest path problem

PhD defense


Example 2 pickup and delivery2

Example 2: Pickup and delivery

  • Query

    • Pickup object from ns and delivery at nt

    • Minimize company’s expenses

    • dynamic Pickup and Delivery with Transfers

  • Non-graph solution

    • Two-phase local search

  • This thesis

    • First work for dPDPT

    • Cost metrics

      • Company’s viewpoint, extra traveling or waiting time

      • Customer’s viewpoint, delivery time

    • Dynamic two-criterion shortest path problem

PhD defense


Example 3 driving data

Example 3: Driving data

  • Group of people driving through the city

    • Track their driving

    • Vehicle routes

      • Sequence of road network intersections

  • Collection of vehicle routes

    • A trusted and familiar way of driving

    • People consult collection

  • Updates

    • New routes added - driving to unknown locations

    • Existing routes modified – new ways to reach known locations

PhD defense


Example 3 driving data1

Example 3: Driving data

  • Query

    • Driving directions from ns to nt

  • Graph-based solution

    • Shortest path

    • Time-dependent shortest path

  • This thesis

    • Capture how people actually drive

      • Tend to reuse roads

      • Consult friends

      • Prefer a trusted over the fastest way

    • New graph query

      • Most Trusted Near Shortest Path

    • Cost metrics

      • Unknown time, time outside routes

      • Length, total time

    • Path with lowest unknown time and length at most a times larger than SP

PhD defense


Example 3 driving data2

Example 3: Driving data

  • Query

    • Driving directions from ns to nt

  • Graph-based solution

    • Shortest path

    • Time-dependent shortest path

  • This thesis

    • Capture how people actually drive

      • Tend to reuse roads

      • Consult friends

      • Prefer a trusted over the fastest way

    • Cost metrics

      • Unknown time, time outside routes

      • Length, total time

    • New graph query

      • Most Trusted Near Shortest Path

      • Path with lowest unknown time and length at most a times larger than SP

PhD defense


Query evaluation

Query evaluation

  • Frequent updated route collections available

  • Challenge for query evaluation

    • Path queries

      • Sequence of locations contained in routes

    • Evaluate queries directly on routes

      • Is it faster?

      • Route as a set of precomputed answers

PhD defense


Outline1

Outline

  • Introduction

    • Route collections examples

    • Query evaluation challenges

  • Evaluating path queries

  • Dynamic Pickup and Delivery with Transfers

  • Most Trusted Near Shortest Path

  • Conclusions

  • Future work

PhD defense


Evaluating path queries

Evaluating path queries

PhD defense


Evaluating path queries1

Evaluating PATH queries

  • Query

    • PATH(ns,nt)

  • Solution

    • Answer: a sequence of locations in routes from ns to nt

    • Indexing route collections

    • Route traversal paradigm

    • Link traversal paradigm

    • Methods for index maintenance

PhD defense


Indexing route collections

Indexing route collections

  • R-Index

    • Associates each location of the collection with the routes containing it

  • T-Index

    • Captures all possible transitions between routes via links

      • Links are shared nodes

PhD defense


Indexing route collections1

Indexing route collections

  • R-Index

    • Associates each location of the collection with the routes containing it

  • T-Index

    • Captures all possible transitions between routes via links

      • Links are shared nodes

PhD defense


Traversal paradigms

Traversal paradigms

  • Route traversal paradigm

    • Traverse collection similar to depth-first search

      • For each route, push all locations after current n in search stack

    • Access indices on routes to terminate search

      • RTS: current location and target on same route (R-Index)

      • RTST: current location on route connected to route of target (T-Index)

  • Link traversal paradigm

    • Traverse collection similar to depth-first search on links

      • R-Index+

      • For each route, push first link after current n in search stack

    • Access indices to create target list T

      • LTS: routes containing target (R-Index+)

      • LTST: routes connected to routes containing target (T-Index)

      • LTS-k: routes connected to routes containing target via first k links before target (R-Index+)

PhD defense


Traversal paradigms1

Traversal paradigms

  • Route traversal paradigm

    • Traverse collection similar to depth-first search

      • For each route, push all locations after current n in search stack

    • Access indices on routes to terminate search

      • RTS: current location and target on same route (R-Index)

      • RTST: current location on route connected to route of target (T-Index)

  • Link traversal paradigm

    • Traverse collection similar to depth-first search on links

      • R-Index+

      • For each route, push first link after current n in search stack

    • Access indices to create target list T

      • LTS: routes containing target (R-Index+)

      • LTST: routes connected to routes containing target (T-Index)

      • LTS-k: routes connected to routes containing target via first k links before target (R-Index+)

PhD defense


Traversal paradigms cont d

Traversal paradigms (cont’d)

  • Expand path (s)

    • Consider every location after a in routes r1 and r3

    • Route trav.: PUSH w,a,g

    • Link trav.: PUSH a

PhD defense


Traversal paradigms cont d1

Traversal paradigms (cont’d)

  • Expand path (s)

    • Consider every location after a in routes r1 and r3

    • Route trav.: PUSH w,a,g

    • Link trav.: PUSH a

PhD defense


Traversal paradigms cont d2

Traversal paradigms (cont’d)

  • RTS, 5th iteration

    • POP d, r1 contains d before t

  • RTST, 3rd iteration

    • POP a, r2 connected with r1 containing t via d

  • LTS, TLTS = {r1, r5}, 4th iteration

    • POP f, r1 contains f before t

  • LTST, TLTST = {r1,r2,r3,r4,r5}, 2nd iteration

    • POP a, r2 connected with r1 containing t via link d

  • LTS-1, TLTS-1 = {r1,r4,r5}, 3rd iteration

    • POP c, r2 connected with r1 containing t via link d

PhD defense


Traversal paradigms cont d3

Traversal paradigms (cont’d)

  • RTS, 5th iteration

    • POP d, r1 contains d before t

  • RTST, 3rd iteration

    • POP a, r2 connected with r1 containing t via d

  • LTS, TLTS = {r1, r5}, 4th iteration

    • POP f, r1 contains f before t

  • LTST, TLTST = {r1,r2,r3,r4,r5}, 2nd iteration

    • POP a, r2 connected with r1 containing t via link d

  • LTS-1, TLTS-1 = {r1,r4,r5}, 3rd iteration

    • POP c, r2 connected with r1 containing t via link d

PhD defense


Traversal paradigms cont d4

Traversal paradigms (cont’d)

  • RTS, 5th iteration

    • POP d, r1 contains d before t

  • RTST, 3rd iteration

    • POP a, r2 connected with r1 containing t via d

  • LTS, TLTS = {r1, r5}, 4th iteration

    • POP f, r1 contains f before t

  • LTST, TLTST = {r1,r2,r3,r4,r5}, 2nd iteration

    • POP a, r2 connected with r1 containing t via link d

  • LTS-1, TLTS-1 = {r1,r4,r5}, 3rd iteration

    • POP c, r2 connected with r1 containing t via link d

PhD defense


Traversal paradigms cont d5

Traversal paradigms (cont’d)

  • RTS, 5th iteration

    • POP d, r1 contains d before t

  • RTST, 3rd iteration

    • POP a, r2 connected with r1 containing t via d

  • LTS, TLTS = {r1, r5}, 4th iteration

    • POP f, r1 contains f before t

  • LTST, TLTST = {r1,r2,r3,r4,r5}, 2nd iteration

    • POP a, r2 connected with r1 containing t via link d

  • LTS-1, TLTS-1 = {r1,r4,r5}, 3rd iteration

    • POP c, r2 connected with r1 containing t via link d

PhD defense


Traversal paradigms cont d6

Traversal paradigms (cont’d)

  • RTS, 5th iteration

    • POP d, r1 contains d before t

  • RTST, 3rd iteration

    • POP a, r2 connected with r1 containing t via d

  • LTS, TLTS = {r1, r5}, 4th iteration

    • POP f, r1 contains f before t

  • LTST, TLTST = {r1,r2,r3,r4,r5}, 2nd iteration

    • POP a, r2 connected with r1 containing t via link d

  • LTS-1, TLTS-1 = {r1,r4,r5}, 3rd iteration

    • POP c, r2 connected with r1 containing t via link d

PhD defense


Index maintenance

Index maintenance

  • Indices as inverted files on disk

  • Lazy updates

    • Buffering phase

      • Update main memory indices

    • Flushing phase

      • Propagate changes to disk

  • Insertions

    • Buffering: mark new entries or changed entries in lists

    • Flushing: merge main memory information with disk-based indices

  • Deletions

    • No buffering: a list of deleted routes since last flushing

    • Flushing: rebuilding affected lists

PhD defense


Experimental analysis

Experimental analysis

  • Rival: DFS, depth-first search over links

  • Datasets

    • Synthetic route collections

      • Vary |R| = {20K, 50K, 100K, 200K, 500K}

      • Vary |Lr| = {3, 5, 10, 30, 50}

      • Vary |N| = {20K, 50K, 100K, 200K, 500K}

      • Vary α = {0.2, 0.4, 0.6, 0.8, 1}

  • Experiments

    • Index construction

    • Query evaluation (queries with/without answer)

      • RTS, RTST Vs LTS

      • DFS Vs LTS, LTS-k, LTST

    • Index maintenance

PhD defense


Rts rtst vs lts

RTS, RTST Vs LTS

Execution time

Execution time

PhD defense


Dfs vs lts lts k ltst

DFS Vs LTS, LTS-k, LTST

Execution time

Execution time

PhD defense


Dynamic pickup and delivery with transfers

Dynamic Pickup and Delivery with Transfers

PhD defense


Solving dpdpt

Solving dPDPT

  • Query

    • dPDPT(ns,nt)

  • Solution

    • Modify static plan

      • 4 modifications, called actions, allowed with/without detours

        • Pickup, delivery, transfer, transport

    • A sequence of actions, path p

      • Operational cost Op

      • Customer cost Cp

    • Dynamic plan graph

      • All possible actions

    • Answer: path p that primarily minimizes Op, secondarily Cp

    • Algorithms SP and SPM

PhD defense


Solving dpdpt cont d

Solving dPDPT (cont’d)

PhD defense


Solving dpdpt cont d1

Solving dPDPT (cont’d)

PhD defense


Solving dpdpt cont d2

Solving dPDPT (cont’d)

PhD defense


Solving dpdpt cont d3

Solving dPDPT (cont’d)

PhD defense


Solving dpdpt cont d4

Solving dPDPT (cont’d)

PhD defense


The sp and spm algorithms

The SP and SPM algorithms

  • The SP algorithm

    • Dynamic plan graph violates subpath optimality => path enumeration

    • Label <Via,p,Op,Cp> for each path to Via

    • At each iteration select label with lowest combined cost

    • Compute candidate answer – upper bound

      • Prune search space

      • Terminate search

  • The SPM algorithm

    • Modified dynamic plan graph

      • Break Op into Op* and OpR

      • Subpath optimality

    • Extends SP

      • Label <Via,p,Op*,OpR> for each path to Via

      • Most “promising” paths to every vertex

PhD defense


The sp and spm algorithms1

The SP and SPM algorithms

  • The SP algorithm

    • Dynamic plan graph violates subpath optimality => path enumeration

    • Label <Via,p,Op,Cp> for each path to Via

    • At each iteration select label with lowest combined cost

    • Compute candidate answer – upper bound

      • Prune search space

      • Terminate search

  • The SPM algorithm

    • Modified dynamic plan graph

      • Break Op into Op* and OpR

      • Subpath optimality

    • Extends SP

      • Label <Via,p,Op*,OpR> for each path to Via

      • Most “promising” paths to every vertex

PhD defense


The sp and spm algorithms cont d

The SP and SPM algorithms (cont’d)

  • INITIALIZATION

    • Pickup Es1a and Es3b

  • SP: Q = {<V1a, (Vs,V1a),6,16>, <V3b,(Vs,V3b),6,36>}

  • SPM: Q = {<V1a, (Vs,V1a),6,0>, <V3b,(Vs,V3b),6,0>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d1

The SP and SPM algorithms (cont’d)

  • POP <V1a, (Vs,V1a),…,…>

    • Transport E12a

  • SP: Q = {<V2a, (Vs,V1a,V2a),6,26>, <V3b,(Vs,V3b),6,36>}

  • SPM: Q = {<V2a, (Vs,V1a,V2a),6,0>, <V3b,(Vs,V3b),6,0>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d2

The SP and SPM algorithms (cont’d)

  • POP <V2a, (Vs, V1a,V2a),…,…>

    • Transfer E25ac

  • Arr5c = 10 < 26 < Dep5c = 40

  • SP: Q = {<V3b,(Vs,V3b),6,36>, <V5c, (Vs,V1a,V2a,V5c),18,36>}

  • SPM: Q = {<V3b,(Vs,V3b),6,0>, <V5c, (Vs,V1a,V2a,V5c),6,12>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d3

The SP and SPM algorithms (cont’d)

  • POP <V3b, (Vs,V3b),6,36> and <V4b, (Vs,V3b,V4b),6,46>

    • Transport E34b and transfer E46bc

  • 46 > Dep6c = 40

  • SP: Q = {<V5c,(Vs,V1a,V2a,V5c),18,36>, <V6c,(Vs,V3b,V4b,V6c),24,52>}

  • SPM: Q = {<V5c,(Vs,V1a,V2a,V5c),6,12>, <V6c,(Vs,V3b,V4b,V6c),12,12>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d4

The SP and SPM algorithms (cont’d)

  • POP <V5c,(Vs,V1a,V2a,V5c),…,…>

    • Transport E56c

  • SP: Q = {<V6c,(Vs,V1a,V2a,V5c,V6c),18, 46>, <V6c,(Vs,V3b,V4b,V6c),24,52>}

  • SPM: Q = {<V6c,(Vs,V1a,V2a,V5c,V6c),6, 12>, <V6c,(Vs,V3b,V4b,V6c),12,12>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d5

The SP and SPM algorithms (cont’d)

  • POP <V5c,(Vs,V1a,V2a,V5c),…,…>

    • Transport E56c

  • SP: Q = {<V6c,(Vs,V1a,V2a,V5c,V6c),18, 46>, <V6c,(Vs,V3b,V4b,V6c),24,52>}

  • SPM: Q = {<V6c,(Vs,V1a,V2a,V5c,V6c),6, 12>, <V6c,(Vs,V3b,V4b,V6c),12,12>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d6

The SP and SPM algorithms (cont’d)

  • POP <V5c,(Vs,V1a,V2a,V5c),…,…>

    • Transport E56c

  • SP: Q = {<V6c,(Vs,V1a,V2a,V5c,V6c),18, 46>, <V6c,(Vs,V3b,V4b,V6c),24,52>}

  • SPM: Q = {<V6c,(Vs,V1a,V2a,V5c,V6c),6, 12>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d7

The SP and SPM algorithms (cont’d)

  • POP <V6c,(Vs,V1a,V2a,V5c,V6c),…,…>

    • Transport E67c

  • SP: Q = {<V7c,(Vs,V1a,V2a,V5c,V6c,V7c), 18, 56>, <V6c,(Vs,V3b,V4b,V6c),24,52>}

  • SPM: Q = {<V7c,(Vs,V1a,V2a,V5c,V6c,V7c), 6,12>}

  • pcand = null

T = 6

PhD defense


The sp and spm algorithms cont d8

The SP and SPM algorithms (cont’d)

  • POP <V7c,(Vs,V1a,V2a,V5c,V6c,V7c), …,…>

    • Delivery E7ec

  • FOUND pcand

  • SP: Q = {<V6c,(Vs,V3b,V4b,V6c),24,52>}

  • SPM: Q = {} END

  • pcand = (Vs,V1a,V2a,V5c,V6c,V7c)

  • Opcand = 24

  • Cpcand = 59

T = 6

PhD defense


The sp and spm algorithms cont d9

The SP and SPM algorithms (cont’d)

  • POP <V6c,(Vs,V3b,V4b,V6c),24,52>

  • Opcand = 24

  • SP: END

T = 6

PhD defense


Experimental analysis1

Experimental analysis

  • Rival: two-phase method, HTT

    • Cheapest insertion for pickup and delivery location, for every new request

    • After k requests perform tabu search

  • Datasets

    • Road networks, OL with 6105 locations, ATH with 22601 locations

    • Static plan with HTT method

      • Vary |Reqs| = {200, 500, 1000, 2000}

      • Vary |R| = {100, 250, 500, 750, 1000}

    • Stored on disk

  • Experiments

    • 500 dPDPT requests

    • HTT1, HTT3, HTT5

  • Measure

    • Total operational cost increase

    • Total execution time

PhD defense


Vary reqs

Vary |Reqs|

Operational cost increase

Execution time

OL road network

PhD defense


Vary r

Vary |R|

Operational cost increase

Execution time

OL road network

PhD defense


Most trusted near shortest path

Most Trusted Near Shortest Path

PhD defense


Identifying mtnsp

Identifying MTNSP

  • Query

    • MTNSP(ns,nt,α)

  • Solution

    • Known graph

    • Unknown graph

    • Two costs for a path p

      • Unknown time Up

      • Length Lp

    • Answer: path p with lowest unknown time Up and length Lp ≤ α dN(ns,nt)

    • Offline processing phase

      • Lipschitz Embedding

    • Online processing phase

      • The TRUSTME algorithm

PhD defense


The known and unknown graphs

The known and unknown graphs

Network graph

Unknown subgraph

Known subgraph

PhD defense


Offline processing phase

Offline processing phase

  • Embedding

    • For each node n in network graph, precompute shortest paths to every node nk in known graph

    • Store

      • dN(n,nk)

      • Uk lowest unknown time

  • Compute bounds

    • d≥N(ns,nt), d≤N(ns,nt)

    • U≥p, U≤p for p(ns,…,nt)

PhD defense


Offline processing phase1

Offline processing phase

  • Embedding

    • For each node n in network graph, precompute shortest paths to every node nk in known graph

    • Store

      • dN(n,nk)

      • Uk lowest unknown time

  • Compute bounds

    • d≥N(ns,nt), d≤N(ns,nt)

    • U≥p, U≤p for p(ns,…,nt)

PhD defense


Offline processing phase2

Offline processing phase

  • Embedding

    • For each node n in network graph, precompute shortest paths to every node nk in known graph

    • Store

      • dN(n,nk)

      • Uk lowest unknown time

  • Compute bounds

    • d≥N(ns,nt), d≤N(ns,nt)

    • U≥p, U≤p for p(ns,…,nt)

  • 12 ≤ dN(n2,nt) ≤ 14

PhD defense


Online processing phase

Online processing phase

  • The TRUSTME algorithm

    • Label-setting

      • Label <n,p,Up,Lp> for each path to n

      • Only the labels of most “promising” paths to every node n

    • At each iteration select label with lowest Lp

    • Compute an upper bound of the unknown time of the answer

      • Prune search space

      • Terminate search

    • Expansion:

      • Exploit d≤N, d≥N, U≤p,U≥p to prune search space

PhD defense


Online processing phase cont d

Online processing phase (cont’d)

  • INITITALIZATION

    • Q = {<ns, (ns), 0, 0}

    • L = d≤N(ns,nt) = 20

    • U = null

    • pcand = null

α = 1.3

PhD defense


Online processing phase cont d1

Online processing phase (cont’d)

  • POP <n1, (ns,n1), 3, 0>

    • Edges (n1,ns), (n1,n2), (n1,n5)

    • Edge (n1,n6)

      • p(ns,n1,n6), Lp = 17

      • Lp + d≥N(n6,nt) = 17 + 11 = 28 > α L = 26

      • Discard p

    • L = d≤N(ns,nt) = 20

    • U = null

    • pcand = null

α = 1.3

PhD defense


Online processing phase cont d2

Online processing phase (cont’d)

  • POP <n7, p(ns,n1,n2,n6,n7), 18,8>

    • Lp = 18 < dN(ns,nt)

    • Lp + dN(n7,nt) = 22 < 1.3 Lp = 23.4

    • FOUND upper bound for the unknown time of answer

    • L = d≤N(ns,nt) = 20

    • U = 12

    • pcand = null

α = 1.3

PhD defense


Online processing phase cont d3

Online processing phase (cont’d)

  • POP <nt, p(ns,n1,n2,n3,n4,nt),20,17>

    • Up > U = 12

    • Not an answer

    • L = d≤N(ns,nt) = 20

    • U = 12

    • pcand = null

α = 1.3

PhD defense


Online processing phase cont d4

Online processing phase (cont’d)

  • POP <nt, p(ns,n1,n5,n2,n6,n7,n4,nt),25,9>

    • Q = {}

    • END

    • pcand = (ns,n1,n5,n2,n6,n7,n4,nt)

    • Lpcand = 25

    • Upcand = 9

α = 1.3

PhD defense


Experimental analysis2

Experimental analysis

  • Rival: label setting SP-EUCLIDEAN

  • First computing shortest path

  • Considering euclidean distance as lower bound

  • Datasets

    • Road networks, OL with 6105 locations, TG with 18263 locations

    • Familiar neighborhoods

    • Vary |H| = {3, 4, ,5, 10, 30}

    • Vary α = {1.1, 1.2, 1.3, 1.4, 1.5}

    • Three strategies for creating known subgraph

      • S1: all locations in neighborhoods

      • S2: all locations on shortest path between neighborhoods centers

      • S3: combination

    • Stored on disk

  • PhD defense


    Strategy s1

    Strategy S1

    Execution time

    Execution time

    PhD defense


    Strategy s2

    Strategy S2

    Execution time

    Execution time

    PhD defense


    Conclusions

    Conclusions

    • Framework for evaluating path queries on frequently updated route collections

      • Indexing schemes

      • Evaluation algorithms

    • Three query cases

      • PATH query on large disk-resident collections

      • dynamic Pickup and Delivery with Transfers

      • Most Trusted Near Shortest Path

    PhD defense


    Future work

    Future work

    • Trip planning or optimal sequence like queries

      • Find a path passing through a Museum, then a Stadium and finally a Restaurant

    • Combine query evaluation with keyword search

      • Find a path passing through a Restaurant relevant to “sea food, lobster”

    • Adopt ideas from PATH query for dPDPT

      • Exploit R-Index/T-Index to identify a candidate answer sooner

    • Additional constraints for dPDPT

      • Vehicle capacity, time windows

    • Handle updates on embedding scheme for MTNSP

      • Inverted index on precompute shortest paths

    • Complexity analysis for dPDPT and MTNSP

    PhD defense


    Publications

    Publications

    • PATH

      • Evaluating Path Queries over Frequently Updated Route Collections, TKDE’11

      • Evaluating Path Queries over Route Collections, ICDE’10-PhD

      • Evaluating Reachability Queries Over Path Collections, SSDBM’09

      • Evaluating "Find a Path" Reachability Queries, ECAI’08-STRWS

    • dPDPT

      • Efficient Dynamic Pickup and Delivery with Transfers, TR KDBSL

      • Dynamic Pickup and Delivery with Transfers, SSTD’11

    • MTNSP

      • Most Trusted Near-Shortest Path, TR KDBSL

    PhD defense


    Other works

    Other works

    • Set-values

      • Efficient Answering of Set Containment Queries for Skewed Item Distributions, EDBT’11

    • Skyline queries

      • Caching Dynamic Skyline Queries, SSDBM’08

    • Managing and personalizing topic directories

      • Mining User Navigation Patterns for Personalizing Topic Directories, CIKM’07-WIDM

      • PatMan: A Visual Database System to Manipulate Path Patterns and Data in Hierarhical Catalogs, AVIVDiLib’05

      • PatManQL: A language to manipulate patterns and data in hierarchical catalogs, EDBT’04-PaRMa

    PhD defense


    Thank you

    Thank you!

    PhD defense


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