spatial temporal data mining

Spatial Temporal Data MiningSpatial Temporal Data Mining

Wei Wang

Data Mining Lab, UCLA

January 21, 1999

OutlineOutline

• Introduction

• Statistical Clustering

• User-defined Trigger

• Spatial Index Structure for High Dimensional Point Data

• Temporal Spatial Pattern Detection– ongoing research

IntroductionIntroduction

• Spatial data mining has been an active research area during recent years.– For some well know problem, e.g., clustering, many

existing algorithms are not efficient enough.• There is still room for improvement.

– There are a lot of interesting problems remaining uninvestigated.

– We classify a subset of problems and try to solve them efficiently.

OutlineOutline

• STING: a statistical information grid approach to spatial data mining

• STING+: an approach to active spatial data mining

• PK-tree: a spatial index structure for high dimensional point data

• Temporal spatial pattern detection

STINGSTING• Spatial database is usually huge.

– Efficiency of the data mining algorithm is crucial.

• Example: each person is an object– Query: Find high income area within California

• high income: salary > $50,000

• area > 4 square miles

– Traditional Method• Step 1: Select out all person whose salary are high.

• Step 2: Do clustering analysis on those persons selected out.

• Step 3: Form the region that each cluster occupies.

• Step 4: Return those regions larger than 4 square miles.

– If “high income” is defined as: 80% persons have salary > $50,000• then the previous method can not even answer the query.

• STING was proposed to solve such problem efficiently.

STINGSTING

• Region Query Example:– Select the maximal regions that have at least 100

houses per unit area and at least 70% of the house prices are above $400K and with total area at least 100 units with 90% confidence.

SELECT REGIONFROM house-mapWHERE DENSITY IN (100, )AND price RANGE (400000, )

WITH PERCENT (0.7, 1)AND AREA (100, )AND WITH CONFIDENCE 0.9

STINGSTING

• Objects are represented by points, each of which has associated spatial attributes, its location, and non-spatial (numerical) attributes.

• Space is recursively divided into smaller rectangular cells until certain level is reached.– A hierarchical structure is employed.

– The average number of objects in a leaf cell is in the range from several dozens to several thousands.

• Preprocess data– capture the statistical information

STINGSTING

1st layer(root)

(i-1)th layer

ith layer

(i+1)th layer(leaf layer)

STINGSTING• For each cell, we have

– attribute-independent parameter• n: number of objects

– attribute-dependent parameters (for each numerical attribute)• mean: mean value of the attribute• std: standard deviation of the attribute value• min: the minimum value of the attribute• max: the maximum value of the attribute• distribution: the type of distribution that best fits the attribute value (can be

NONE)

• Bottom-up generation when the data is loaded into the database.– Linear compilation time

– Only has to be done once not for each query.

STINGSTING

• Take advantage of the statistical information captured.

• Only go through relevant cells at each level.– Root is relevant.

– For each relevant cell, we exam its children at next level by statistical test and label them as relevant or not relevant.

– Form regions from relevant leaf level cells.

• Do not need to access full database.– It is very efficient.

STINGSTING

• The computational complexity of STING is linearly proportional to the number of leaf cells.

• We used the SEQUOIA 2000 benchmark as the data set to compare the performance of STING with other approaches.

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• STING is a query-independent approach.– The statistical information exists independently of queries.

• STING has a much smaller response time compared to other approaches– The computational complexity is linearly proportional to the number of

leaves.– I/O cost is low.

• STING can support different resolution of query result.• Regions returned by STING approach that returned by

DBSCAN when the granularity approaches zero.• Parameters in the hierarchical structure can be maintained

efficiently by incremental update.

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STING+STING+

• Moreover, since objects evolve, interesting patterns may emerge or disappear over time.

• Example: – Trigger: Do bandwidth reallocation when the average call length is greater

than 10 minutes within the region where at least 10 cellular phones are in use per squared mile.

• This can not be supported by traditional database triggers efficiently– due to the fact that the class membership of an object is not only determined

by its non-spatial attributes but also by the attributes of objects in its neighborhood.

• Naïve approach: re-evaluate condition periodically.– Not efficient.

STING+STING+• STING+ was an extension of STING to support user-defined

trigger. • In spatial databases, object insertion, deletion, and update are

primitive events.• Observation: Usually, only the cumulative effect of a set of

primitive events may cause the trigger condition to be true.

• We refer such set of primitive events to as a composite events.

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STING+STING+

• Condition-Action paradigm– In general, it is difficult or even impossible for user to specify all

possible composite events that may cause the trigger condition to be true.

• In general, evaluating a user-defined trigger T usually involves two aspects:– Find a set of composite events E(s) that may cause the trigger

condition CT to become true.

– Each time some composite event in E(s) occurs, check the status (false or true) of CT (given that CT was false previously).

STING+STING+

• Observation: As a side effect of the occurrence of some composite event, the set of composite events E(s) that could cause CT to transition from false to true might also evolve over time.

• Two set of composite events we need to consider:– the set of composite events E(s) that can cause CT to become true

• need to re-evaluate CT

– the set of composite events F(s) that can cause a change to E(s)• need to update E(s)

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STING+STING+

• Observation: In spatial databases, the effect of an event is usually local to its neighborhood.

• STING+ decomposes the user-defined trigger into a set of sub-triggers associated with individual cells in the hierarchical structure.– These sub-triggers are used to monitor composite events in E(s) and F(s)

and change accordingly when E(s) and F(s) evolves.

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Level 4 Level 3

STING+STING+

• Updates are suspended at some level in the hierarchy until such time that the cumulative effect of these updates might cause the trigger condition to become satisfied.

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Level 2 Level 1

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STING+STING+• Example: Trigger bandwidth reallocation when the total

area occupied by those regions in California where at least 10 cellular phones are in use per squared mile and the average length of phone calls is at least 15 minutes with total area at least 50 squared miles increases by at least 10 squared miles.

DEFINE TRIGGER exampleON cellular-phoneWHEN SELECT SIZE(REGION) INCREASE RANGE (10, )

WHERE DENSITY IN RANGE (10, )AND AVERAGE(length) IN RANGE (15, )AND AREA IN RANGE (50, )

LOCATION CaliforniaDO bandwidth-reallocation

STING+STING+

• Observation: Trigger condition CT is a conjunction of predicates P1 P2 … Pn and can not be true if one predicate is false.

– They can be evaluated in a certain order: the ith predicate is tested when all previous i -1 predicates are true.

– The evaluation order should be chosen in such a way that the total cost is minimum.

• STING+ evaluates CT in the order {location, density condition, attribute condition}, each of which is evaluated in a different phase.

– Location only needs to be evaluated once and the cost can be regarded as constant in the trigger evaluation process.

– If the location is fixed, unnecessary sub-triggers set on cells outside the location can be avoided and hence save the evaluation cost of other predicates.

– Sub-triggers set during an earlier phase will exist longer than those set in a later phase.

• It is better to first evaluate the predicate that takes less time to handle.• cost(density) < cost(attribute)

STING+STING+

• Average CPU cycles for handling each type of sub-trigger

Density condition Attribute condition Movementinsertion deletion inside outside expand shrink

Inter-mediatelevel

3812 3803 3789 3807 N/A N/A

Leaflevel 8055 5775 11212 8164 2126 2087

STING+STING+

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PK-treePK-tree

• As both the number of objects and the number of attributes are very large, it is essential to organize the set of objects by some dynamic indexing structure.

• Point index methods

Index MethodOverlapping

Siblings HeightBoundedNode Size

BoundedStorage

PR Quad-tree No Unbounded Yes NoK-D-B-tree No Unbounded Yes NoSR-tree Yes log(N) Yes YesX-tree Yes log(N) No YesPK-tree No log(N) Yes Yes

PK-treePK-tree

Spatial decomposition: Space is recursively divided until a level LD such that each cell contains at most one point.

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PK-treePK-tree

Example of a PR Quad-tree:

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16 intermediate nodes, height = 3

PK-treePK-tree

root

Example of a PK-tree of rank 3:

a2 d1 b4d2 c3 e1 e2 f3 g2 h2 g3 a7 g5f6f5e5d8c8d7b8b7

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PK-treePK-tree

• PK-tree employs a concept of K-instantiable cell to eliminate unnecessary nodes.– Point cell: a non-empty cell at level LD

– A cell C is K-instantiable iff• C is a point cell, or

• there does not exist (K-1) or less K-instantiable sub-cells to cover all non-empty space in C

– Only K-instantiable cells serve as nodes in the PK-tree (expect the root).

– The parent-child relationship follows naturally from the cell-subcell relationship.

PK-treePK-tree

• Properties:– Bounds on node’s outdegree

• allows allocating one node to a page

– Bounded storage space– Existence and Uniqueness

• enables us to analyze the behavior of a PK-tree easier.

– Expected height• log(N) under some general condition

• guarantees efficiency of retrieval and update.

– No overlapping among sibling nodes• efficient retrieval

• Empirical studies shown that the PK-tree outperforms SR-tree and X-tree by a wide margin.

PK-treePK-tree

Dimension 2 4 8 16 32 64

PK-tree (u) 4 4 5 6 7 9

PK-tree (c1) 5 7 7 6 7 8

PK-tree (c2) 7 7 6 7 8 9

X-tree 4 4 4 4 5 6

SR-tree 4 4 5 5 6 7

2 4 8 16Dimension

u c1 c2 u c1 c2 u c1 c2 u c1 c2

PK-tree 1.8 1.9 1.9 2.8 2.8 2.8 4.9 4.8 4.9 9.4 9.3 9.4

X-tree 1.8 1.8 1.8 3.0 3.0 3.0 5.6 5.5 5.6 10.7 10.4 10.6

SR-tree 69 70 70 74 73 74 74 74 75 90 91 92

Height of generated trees on 100,000 points

Size of index in MB

PK-treePK-tree

KNN query on clustered data distribution

PK-treePK-tree

• Real data set: NASA Sky Telescope Data– 200,000 two-dimensional points (they are the coordinates of crater

locations on the surface of Mars)

height size KNNCPU

KNNI/O

RANCPU

RANI/O

PK-tree 5 3.7MB 4ms 4 3ms 4

X-tree 4 5.7MB 90ms 4 10ms 4

SR-tree 5 120MB 28ms 8 14ms 6

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Temporal Spatial Pattern DetectionTemporal Spatial Pattern Detection

• When the number of attributes is large and/or the value of attributes evolve frequently, the complexity of patterns and the number of potential patterns increase.– It is not desirable or even feasible to ask the user specify

interesting patterns.

– E.g., the user wants to know any possible patterns involving certain attributes such as salary, rent, cellular phone usage, etc.

– Existing association rule algorithm can not be applied.• Continuous attribute domain

• Temporal evolution

• Prior knowledge about relationships among attributes and objects


• Object represented by point– primitive attributes

• spatial attributes, i.e., coordinates of its position

• non-spatial attributes, e.g., name, weight, height, salary, rent

– derived attributes derived from primitive attribute(s)• environment attributes, e.g., distance to a hospital, average income in the

neighborhood area

• Consider a sequence of snapshots S1, S2, …, Sn

• Temporal Spatial Pattern– describes a possible relationship among evolution of attributes

• E.g., if the user want to know patterns involving salary and distance to big city, then one interesting pattern would be “people receiving a raise tends to move further away from the big city from 1987 to 1993.”.


• More complicated patterns– Patterns on clustering evolution

– Patterns of high order

– Patterns whose cause and consequence do not happen together• There is a delay for the consequence to show up.

– Patterns involving relationships among objects• e.g., people who live far away from any doctor tend to move to a

place closer to some doctor.

– Environment variables evolve independently over time.

spatial temporal data mining

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