Spatial Databases: Building Spatial DB Spring, 2017 Ki-Joune Li

Importance of Database Application of Spatial Databases (e.g. GIS) Garbage-In Garbage-Out About 70% of GIS Development Cost: DB Cost

Comparison with Software Lifecycle Requirement Analysis Modeling Schema Design DB Environments Data Collection and Input Quality Control Maintenance DB Life Cycle Requirement Analysis Functional Specification Design Development Environments Coding Test Maintenance Software Life Cycle – Waterfall Model

Requirement Analysis Analysis of Status Output of Analysis as it is and as it shall be. Output of Analysis Use-Case Diagram of UML: Workflow Analysis Data items that have been maintained and to be maintained Description of each item: Data Dictionary Relationships and Constraints on items Required accuracy Spatial Precision Temporal Precision Current State: As it is As it must be

Data Dictionary Definitions and Representation of Data Items such as Precise definition of data elements Integrity constraints or Constrains Stored procedures and trigger rules Specification of Producer and Consumer of data element Why it is so important? Common understanding on data items Consistency of databases Important input to data modeling

Data Modeling Data Modeling 4 steps Understanding the real world and application A very small piece of the real world According to viewpoint Determined by applications Drawing what you have understood in formal method Class Diagram in UML 4 steps Definition of Entities Attributes of each Entity Relationships Constraints

-- can optionally be described here. Class Diagram: Basic MyClassName +SomePublicAttribute : SomeType -SomePrivateAttribute : SomeType #SomeProtectedAttribute : SomeType +ClassMethodOne() +ClassMethodTwo() Responsibilities -- can optionally be described here. Multiplicity Customer 1 Simple Aggregation Class Abstract Class Rental Invoice Rental Item {abstract} 1..* 1 0..1 Composition (Dependency) Simple Association Generalization Checkout Screen DVD Movie VHS Movie Video Game

Definition of Entities Extract nouns from Problem statement Use-Case Diagram Delete unnecessary entities Duplication Attributes rather than entity ex. Loan amount Definition of Features Geographic Entity Granularity MyClassName

Definition of Features Meaningful Object of GIS in real world Must have a geometry Point, Line, Polygon, etc.. How to define the Granularity of Features Example How to define “a” coastal line? The highway from Pusan to Seoul is a long feature ? How to separate this long road?

Definition of Attributes Attributes of Feature Geometric type: Spatial Attribute Non-Spatial Attributes Geometric Type Different Levels of Detail (LOD) Building Polygon in 1/1,000 scale Point in 1/1,000,000 scale Road Polyline in 1/1,000,000 scale MyClassName +SomePublicAttribute : SomeType -SomePrivateAttribute : SomeType #SomeProtectedAttribute : SomeType +GeometricAttribute

Relationship Relationship Non-Spatial Relationship Spatial Relationship: Topology

Constraints Example Implementation No building on road surface More than 50 meters between two poles Implementation Internal Functions for checking constraints (or constructor) Spatial OCL (Object Constraint Language) More detail and complete constraint Better quality of DB

Quality Control for Data Modeling For the quality control, A Simulation with a pre-defined test scenario

Schema Design Automatic Conversion from Data Modeling to Schema Check Points: Performance Issues Materialization Index Geographic Distribution of DB: Clustering Based on Workload Analysis Distribution of operations Distribution of values

Materialization In SQL, view is a virtual table derived from a Select statement Eample CREATE VIEW ExcellentStudents AS SELECT Name, Department, Score FROM Students WHERE Score > 4.0 SELCT Name FROM ExcellentStudents Where Department=‘CS’ ExcellentStudents Materialization Invoke

Materialize or Not ? Materialization Duplication Not 3NF (BCNF) Cause an inconsistency between the original and derived tables Update: Overhead due to update propagation Extra Space Requirements Should be determined depending on the WORKLOAD Frequency of updates Cost for update propagation Especially when materialized view is geographically distributed

Spatial Index Index: Accelerate Search Spatial Index Spatial predicates: contain, overlapping, k-NN Much improves the query processing performance Has a performance overhead for insertion/deletion 2nd Phase Search Block Number Search Condition { Block# } Database on Disk 1st Phase

Clustering: Placement of records Vertical Fragmentation vs. Horizontal Fragmentation Vertical Fragmentation: Decomposition of table Horizontal Fragmentation: Placement of objects Consideration on Workload Vertical Fragmentation Horizontal Fragmentation

Clustering Clustering: Grouping objects so as to maximize Prob(a C, bC), when OK=a and OK+1=b for any two objects a and b of the same group C. Spatial Clustering Basic Assumption: If dist(a,b) < dist(a,c), Prob(OK=a, OK+1=b) > Prob(OK=a, OK+1=c) Two consecutive accesses a b c

Spatial Clustering Methods k-Means CLARANS in IEEE TKDE 2002, 14(5) BIRCH in proc. VLDB 1996 DBSCAN in proc. KDD 1996 SMTIN in proc. ACM-GIS 1997