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Introduction to Spatial Database Research. Donghui Zhang CCIS Northeastern University. What is spatial database?. A database system that is optimized to store and query spatial objects: Point: a hotel, a car Line: a road segment Polygon: landmarks, layout of VLSI. Road Network.

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introduction to spatial database research

Introduction to Spatial Database Research

Donghui Zhang

CCIS

Northeastern University

what is spatial database
What is spatial database?
  • A database system that is optimized to store and query spatial objects:
    • Point: a hotel, a car
    • Line: a road segment
    • Polygon: landmarks, layout of VLSI

Road Network

Satellite Image

VLSI Layout

are spatial databases useful
Are spatial databases useful?
  • Geographical Information Systems
    • e.g. data: road network and places of interest.
    • e.g. usage: driving directions, emergency calls, standalone applications.
  • Environmental Systems
    • e.g. data: land cover, climate, rainfall, and forest fire.
    • e.g. usage: find total rainfall precipitation.
  • Corporate Decision-Support Systems
    • e.g. data: store locations and customer locations.
    • e.g. usage: determine the optimal location for a new store.
  • Battlefield Soldier Monitoring Systems
    • e.g. data: locations of soldiers (w/wo medical equipments).
    • e.g. usage: monitor soldiers that may need help from each one with medical equipment.
slide5

Driving directions as you go.

  • Find nearest Wal-Mart or hospital.

NN Query

slide6

Range query

ArcGIS 9.2, ESRI

are spatial databases useful1
Are spatial databases useful?
  • Geographical Information Systems
    • e.g. data: road network and places of interest.
    • e.g. usage: driving directions, emergency calls, standalone applications.
  • Environmental Systems
    • e.g. data: land cover, climate, rainfall, and forest fire.
    • e.g. usage: find total rainfall precipitation.
  • Corporate Decision-Support Systems
    • e.g. data: store locations and customer locations.
    • e.g. usage: determine the optimal location for a new store.
  • Battlefield Soldier Monitoring Systems
    • e.g. data: locations of soldiers (w/wo medical equipments).
    • e.g. usage: monitor soldiers that may need help from each one with medical equipment.
are spatial databases useful2
Are spatial databases useful?
  • Geographical Information Systems
    • e.g. data: road network and places of interest.
    • e.g. usage: driving directions, emergency calls, standalone applications.
  • Environmental Systems
    • e.g. data: land cover, climate, rainfall, and forest fire.
    • e.g. usage: find total rainfall precipitation.
  • Corporate Decision-Support Systems
    • e.g. data: store locations and customer locations.
    • e.g. usage: determine the optimal location for a new store.
  • Battlefield Soldier Monitoring Systems
    • e.g. data: locations of soldiers (w/wo medical equipments).
    • e.g. usage: monitor soldiers that may need help from each one with medical equipment.
are spatial databases useful3
Are spatial databases useful?
  • Geographical Information Systems
    • e.g. data: road network and places of interest.
    • e.g. usage: driving directions, emergency calls, standalone applications.
  • Environmental Systems
    • e.g. data: land cover, climate, rainfall, and forest fire.
    • e.g. usage: find total rainfall precipitation.
  • Corporate Decision-Support Systems
    • e.g. data: store locations and customer locations.
    • e.g. usage: determine the optimal location for a new store.
  • Battlefield Soldier Monitoring Systems
    • e.g. data: locations of soldiers (w/wo medical equipments).
    • e.g. usage: monitor soldiers that may need help from each one with medical equipment.
slide12

NN(Bob) = George

George

John

Bob

Bill

Mike

slide13

Who will seek help from me?

RNN(Bob) = {John, Mike}

George

John

Bob

Bill

Mike

RNN query

and beyond the space
And beyond the “space” …
  • 2004 NBA dataset*: each player has 17 attributes
  • “Spatial Data”: an object is a point in a 17-dimensional space
  • Who are the best players?
    • i.e. not “dominated” by any other player.

Skyline query

* www.databaseBasketball.com

and beyond the space1
And beyond the “space” …
  • 2004 NBA dataset*: each player has 17 attributes
  • “Spatial Data”: an object is a point in a 17-dimensional space
  • Who are the best players?
    • i.e. not “dominated” by any other player.

Skyline query

* www.databaseBasketball.com

and beyond the space2
And beyond the “space” …
  • 2004 NBA dataset*: each player has 17 attributes
  • “Spatial Data”: an object is a point in a 17-dimensional space
  • Who are the best players?
    • i.e. not “dominated” by any other player.

Skyline query

* www.databaseBasketball.com

and beyond the space3
And beyond the “space” …
  • 2004 NBA dataset*: each player has 17 attributes
  • “Spatial Data”: an object is a point in a 17-dimensional space
  • Who are the best players?
    • i.e. not “dominated” by any other player.

Skyline query

* www.databaseBasketball.com

and beyond the space4
And beyond the “space” …
  • 2004 NBA dataset*: each player has 17 attributes
  • “Spatial Data”: an object is a point in a 17-dimensional space
  • Who are the best players?
    • i.e. not “dominated” by any other player.

Skyline query

* www.databaseBasketball.com

and beyond the space5
And beyond the “space” …
  • 2004 NBA dataset*: each player has 17 attributes
  • “Spatial Data”: an object is a point in a 17-dimensional space
  • Who are the best players?
    • i.e. not “dominated” by any other player.

Skyline query

* www.databaseBasketball.com

subspace skyline queries
Subspace Skyline Queries

u3

u3

t2

t2

t4

t3

t4

t7

1 2 3 4 5 6 7 8

t3

t7

1 2 3 4 5 6 7 8

t5

t5

t5

t6

t1

t1

t6

u1

1 2 3 4 5 6 7 8 9

u2

Skyline in u1, u3

1 2 3 4 5 6 7 8 9

Skyline in u2, u3

  • In an online skyline processing system, the users may ask skyline queries on any subspace, i.e. a subset of attributes.
  • Different subspace skylines can be very different!

u1 u2 u3 u4

t1 3 4 2 5

t2 4 6 7 2

t3 9 7 5 6

t4 4 3 6 1

t5 2 2 3 1

t6 6 1 1 3

t7 1 3 4 1

Objects of 4-dimensions

straightforward solutions
Straightforward Solutions
  • On-the-fly computation
    • Slow query processing
  • Pre-compute and store all subspace skylines: high update costs
    • No update support
    • Waste of storage
the compressed skycube xz06
The Compressed Skycube [XZ06]
  • Compact storage
    • Represent all skylines in a very concise way, by preserving only essential information of subspace skylines.
  • Efficient query support
    • Efficiently answer arbitrary subspace skyline queries without accessing the original data.
  • Efficient update scheme
    • Avoid unnecessary data access and subspace skyline computation upon updates.
the complete pre computation
The complete pre-computation

Subspace Skyline

u1

t7

u2

t6

u3

t6

u4

t4 , t5 , t7

u1 , u2

t5 , t6, t7 , t9

u1 , u3

t1 , t5 , t6, t7 , t9

u1 , u4

t7

u2 , u3

t6

u2 , u4

t5 , t6

u3 , u4

t5 , t6

u1 , u2 , u3

t1 , t5 , t6, t7 , t9

u1 , u2 , u4

t5 , t6, t7

u1 , u3 , u4

t1 , t5 , t6, t7

u2 , u3 , u4

t5 , t6

u1 , u2 , u3 , u4

t1 , t5 , t6, t7

u1 u2 u3 u4

t1 3 4 2 5

t2 4 6 7 2

Skycube

t3 9 7 5 6

t4 4 3 6 1

t5 2 2 3 1

t6 6 1 1 3

Contains many duplicates, e.g. t6 appears 12 times

t7 1 3 4 1

t8 6 5 3 8

t9 2 2 3 7

minimum subspace mss
Minimum Subspace (mss)

Minimum Subspaces

t1u1, u3

t4u4

t5 u4, u1, u2, u1, u3

t6u2, u3

t7u1, u4

t9u1, u2, u1, u3

Subspace Skyline

  • Object t6 appears in the skylines of 12subspaces.
  • The number of minimum subspaces of t6 is only 2.

u1

t7

u2

t6

u3

t6

u4

t4 , t5 , t7

u1 , u2

t5 , t6, t7 , t9

u1 , u3

t1 , t5 , t6, t7 , t9

u1 , u4

t7

u2 , u3

t6

u2 , u4

t5 , t6

u3 , u4

t5 , t6

u1 , u2 , u3

t1 , t5 , t6, t7 , t9

u1 , u2 , u4

t5 , t6, t7

u1 , u3 , u4

t1 , t5 , t6, t7

u2 , u3 , u4

t5 , t6

u1 , u2 , u3 , u4

t1 , t5 , t6, t7

the compressed skycube csc
The Compressed Skycube (CSC)

CSC

Subspace Skyline

Minimum Subspaces

u1

t7

t1u1, u3

u2

t6

t4u4

u3

t6

t5 u4, u1, u2, u1, u3

u4

t4 , t5 , t7

t6u2, u3

u1 , u2

t5 , t9

t7u1, u4

u1 , u3

t1 , t5 , t9

t9u1, u2, u1, u3

  • Definition: The Compressed Skycube (CSC) consists of non-empty subspace U, such that an object t is stored in a subspace U if and only if U is a minimum subspace of t, i.e. U mss(t).
querying csc
Querying CSC

t6

Find the skyline in subspace u2, u3, u4.

t5

Only visit CSC, not whole dataset

  • Theorem 1: Given a query space Uq and an object t, if for any subspace Ui in mss(t), UiUq, then t is not in the skyline of Uq.
    • Search the subspaces which are subsets of the query space.
  • Theorem 2 (Local Comparison): To check a candidate t in a subspace V Uq, we only need to compare t with the objects within the same subspace.
    • Compare candidates within their own subspaces.

Output is non-blocking!

CSC

Subspace Skyline

u1 u2 u3 u4

u1

t7

t1 3 4 2 5

u2

t6

t4 4 3 6 1

u3

t6

t5 2 2 3 1

u4

t4 , t5 , t7

t6 6 1 1 3

u1 , u2

t5 , t9

t7 1 3 4 1

u1 , u3

t1 , t5 , t9

t9 2 2 3 7

updating csc
Updating CSC
  • sky(full): the skyline regarding to all dimensions.
  • t: the object to be updated.
  • Theorem: upon update, no need to access the original data if tsky(full).
  • Efficient algorithms in both cases.
performance
Performance
  • (Full-space) Dimensionality: 6
  • Object cardinality: [100K, 500K].
  • Distribution: Uniform

Update efficiency

Storage efficiency

Query efficiency

summary
Summary
  • Spatial database has many practical applications.
  • Spatial database research aims to design efficient algorithms for various queries.
  • The talk mentioned a few (range query, aggregation query, NN query, RNN query, optimal-location query, fastest-path query, and skyline query).
  • There are much more -- an on-going research field.