Csci6330 Spatial Database Management

Yan Huang

[email protected]

http://www.cs.unt.edu/~huangyan/6330

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Today’s Outline

Introduction Syllabus Course description Next class’s topic 2

Csci6330 Spatial Database Management

Instructor

: Yan Huang Office: NTRP F251 Office Tel: 940-369-8353

Email:

[email protected]

Office Hour:

T, Th 8:55-9:55AM, or by appointment.

Class Time and Location:

TR, 10:00am-11:20am, NTRP B190

Class Website:

http://www.cs.unt.edu/~huangyan/6330 3

Self Introduction

Name Major Previous study and working experiences Computer background: hardware, software Why taking this course Expectation for this course 4

Course Syllabus

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Textbook

Text Book (required):

Spatial Databases : A Tour

• By Shashi Shekhar and Sanjay Chawla • Available in the Book Store

Reference Book

Spatial Databases: with application to GIS

• By Philippe Rigaux, Michel Scholl, and Agnes Voisard

GIS: A Computing Perspective, Taylor and Francis

• By M.R. Worboys 6

Background

Prerequisite: csci4350 or csci5350 Entity-Relationship Model File Organizations Index Structures for Files Basic Queries in SQL Query Processing Query Optimization 7

Course Description

This course treats a specific advanced topic of current research interest in the area of

spatial

data and information.

The main objective of this class is to study research methods and literature in spatial database systems. Core research skills of literature analysis, innovation, evaluation of new ideas, and communication are emphasized via homework assignments and projects. 8

Topics

Introduction to Spatial Databases Spatial Concepts and Data Models Spatial Query Languages: SQL3, OGIS Spatial Storage and Indexing: R-tree, Grid files, Query Processing and Optimization Strategies for range query, nearest neighbor query Spatial joins (e.g. tree matching), cost models for new strategies, impact on rule based optimization. Spatial Networks: Query language for graphs, graph algorithms, access methods.

Introduction to Spatial Data Mining Spatial auto-correlation, co-location patterns, spatial outliers, classification methods Trends in Spatial Databases: mobile wireless applications, spatio temporal data. 9

Homework

Assignments Include research paper critique and presentation Assignments will be due at the beginning of the class on the due date All assignments must have your name/number name , student ID You are encouraged to type your answers and course Late homework Submit to my office on paper 30% penalty for the first 24-hour 70% penalty for >= 24 hour 10

Examinations/Grading

There will be no exams for this course Grading Class participation and 10 quiz 20% Paper analysis and presentation 30% Final Project 50% • Presentation 20% • Final Report 30% 11

Paper Review and Presentation

Each student is expected to write a critique of TWO paper P and Q Present their overview in the class This homework will help us learn literature analysis skills towards starting a thesis or to simply understand a research paper. 12

Steps for Paper Review and Presentation

A1. Identify the paper P and Q you would present. Make a home page for the course assignment. Identify paper R and S you would like to lead discussion A2. Submit the narratives and slides of the paper P and Q. Electronic copy will be linked to the web-page for the course to make those available to the audience. The written critique should be about 2000 words long and the oral presentation should consist of about 30 transparencies addressing the following questions: Problem statement List the major contributions of the paper What are the key concepts behind the approach in this paper What is the validation methodology List the assumptions made by the authors If you were to rewrite this paper today, what would you preserve and what would be revise? Briefly justify. A3. Make a 45-minutes oral presentation in the class on paper P and Q.

A4. Lead the group discussion when paper R and S are presented.

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Term Paper/Project

P0. Identify a team of two for your project and create a webpage for the project P1. Define possible questions P2. Identify key sources, types of evidence P3. Summary of key readings P4. Possible outline or overall structure for paper / project. P5. Formal proposal: Includes questions or thesis, key resources, and proposed structure revised with feedback from peers and the instructor. P6. Rough draft. Follow the outlines from the research papers covered in the course. For example, the draft may have 5 sections: Problem Statement, Significance of the problem Related Work and Our Contributions Proposed Approach Validation of listed contributions (experimental, analytical) Conclusions and Future Work P7. Oral presentation in the class using 20 transparencies 14

Honor System

All work is to be done under the provisions of the University of North Texas Honor System

. Students are encouraged to discuss the interpretation of an assignment However, the actual solution to problems must be one's own In research report, related work should be properly cited 15

Helpful Comments

Read the textbook and papers before class Participate class discussion Review textbook and papers regularly after class Start working on the assignments soon after they are handed out.

Come to my office hours for questions and project discussion 16

Chapter 1: Introduction to Spatial Databases

1.1 Overview 1.2 Application domains 1.3 Compare a SDBMS with a GIS 1.4 Categories of Users 1.5 An example of an SDBMS application 1.6 A Stroll though a spatial database 1.6.1 Data Models, 1.6.2 Query Language, 1.6.3 Query Processing, 1.6.4 File Organization and Indices, 1.6.5 Query Optimization, 1.6.6 Data Mining 17

Learning Objectives

Learning Objectives (LO) LO1 : Understand the value of SDBMS • Application domains • Users • How is different from a DBMS?

LO2: Understand the concept of spatial databases LO3: Learn about the Components of SDBMS Mapping Sections to learning objectives LO1 1.1, 1.2, 1.4

LO2 1.3, 1.5

LO3 1.6

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Value of SDBMS

Traditional (non-spatial) database management systems provide: Persistence across failures Allows concurrent access to data Scalability to search queries on very large datasets which do not fit inside main memories of computers Efficient for non-spatial queries, but not for spatial queries Non-spatial queries: List the names of all bookstore with more than ten thousand titles.

List the names of ten customers, in terms of sales, in the year 2001 Use an index to narrow down the search Spatial Queries: List the names of all bookstores with ten miles of Minneapolis List all customers who live in Tennessee and its adjoining states List all the customers who reside within fifty miles of the company headquarter 19

Value of SDBMS – Spatial Data Examples

Examples of non-spatial data Names, phone numbers, email addresses of people Examples of Spatial data Census Data NASA satellites imagery - terabytes of data per day Weather and Climate Data Rivers, Farms, ecological impact Medical Imaging Exercise: Identify spatial and non-spatial data items in A phone book A Product catalog 20

Value of SDBMS – Users, Application Domains

Many important application domains have spatial data and queries. Some Examples follow: Army Field Commander: Has there been any significant enemy troop movement since last night?

Insurance Risk Manager: Which homes are most likely to be affected in the next great flood on the Mississippi?

Medical Doctor: Based on this patient's MRI, have we treated somebody with a similar condition ?

Molecular Biologist:Is the topology of the amino acid biosynthesis gene in the genome found in any other sequence feature map in the database ?

Astronomer:Find all blue galaxies within 2 arcmin of quasars. Exercise: List two ways you have used spatial data. Which software did you use to manipulate spatial data?

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Due and Next Class

Paper review selection and webpage Team formation and project webpage Prepare on a open book quiz with 2 questions based on today’s topic Read chapter 1.1,1.2,1.4 if you have not Next class: chapter 1.3,1.5,1.6 of the book 22

Agenda Today

Quiz with two questions Paper selection and team formation Two papers to present and two papers to lead discussion Team of two or one Topics Concepts of spatial database Components of a spatial database Summary of chapter 1 Next class’s topic 23

Quiz

Give a scenario and a query in your life which involves spatial data.

Give a possible reason why spatial analysis using spatial queries on customer data may be important to vendors such as best buy and walmart.

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Learning Objectives

Learning Objectives (LO) LO1 : Understand the value of SDBMS LO2: Understand the concept of spatial databases • What is a SDBMS?

• How is it different from a GIS?

LO3: Learn about the Components of SDBMS Sections for LO2 Section 1.5 provides an example SDBMS Section 1.1 and 1.3 compare SDBMS with DBMS and GIS 25

What is a SDBMS ?

A SDBMS is a software module that can work with an underlying DBMS supports spatial data models, spatial abstract data types (ADTs) and a query language from which these ADTs are callable supports spatial indexing, efficient algorithms for processing spatial operations, and domain specific rules for query optimization Example: Oracle Spatial data cartridge, ESRI SDE can work with Oracle 8i DBMS Has spatial data types (e.g. polygon), operations (e.g. overlap) callable from SQL3 query language Has spatial indices, e.g. R-trees IBM: Spatial Option Informix: Spatial Datablade 26

SDBMS Example

Consider a spatial dataset with: County boundary (dashed white line) Census block - name, area, population, boundary (dark line) Water bodies (dark polygons) Satellite Imagery (gray scale pixels) Storage in a SDBMS table: create table census_blocks ( name area population boundary string, float, number, polygon ); Fig 1.2

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Modeling Spatial Data in Traditional DBMS

•A row in the table census_blocks • Question: Is Polyline (Figure 1.3) datatype supported in DBMS?

Figure 1.3

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Spatial Data Types and Traditional Databases

Traditional relational DBMS Support simple data types, e.g. number, strings, date Modeling Spatial data types is tedious Example: Figure 1.4 shows modeling of polygon using numbers Three new tables: polygon, edge, points • Note: Polygon is a polyline where last point and first point are same A simple unit sqaure represented as 16 rows across 3 tables Simple spatial operators, e.g. area(), require joining tables Tedious and computationally inefficient Question. Name post-relational database management systems which facilitate modeling of spatial data types, e.g. polygon.

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Mapping “census_table” into a Relational Database

Fig 1.4

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Evolution of DBMS technology

Fig 1.5

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Spatial Data Types and Post-relational Databases

Post-relational DBMS Support user defined abstract data types Spatial data types (e.g. polygon) can be added Choice of post-relational DBMS Object oriented (OO) DBMS Object relational (OR) DBMS A spatial database is a collection of spatial data types, operators, indices, processing strategies, etc. and can work with many post relational DBMS as well as programming languages like Java, Visual Basic etc.

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An Example : OO-Model

Class Highway tuple (highway_name: string, highway_type: string, sections: list(Section)) Class Section tuple (section_name: string, number_lanes: integer, city_start: City, city_end: City, geometry: Line) Class City Tuple (city_name: string, population: integer, geometry: Region) 33

An Example : OO-Model

Method Method length in class Line: real (Computes the length of a line.) Query: Length of Interstate 99 Sum (Select s. geometry->length() from h in All_highways, s in h.sections

where h.highway_name = ‘I99’) 34

How is a SDBMS different from a GIS ?

GIS is a software to visualize and analyze spatial data using spatial analysis functions such as

Search

Thematic search, search by region, (re-)classification

Location analysis

Buffer, corridor, overlay

Terrain analysis

Slope/aspect, catchment, drainage network

Flow analysis

Connectivity, shortest path

Distribution

Change detection, proximity, nearest neighbor

Spatial analysis/Statistics

Pattern, centrality, autocorrelation, indices of similarity, topology: hole description

Measurements

Distance, perimeter, shape, adjacency, direction GIS uses SDBMS to store, search, query, share large spatial data sets 35

How is a SDBMS different from a GIS ?

SDBMS focuses on Efficient storage, querying, sharing of large spatial datasets Provides simpler set based query operations Example operations: search by region, overlay, nearest neighbor, distance, adjacency, perimeter etc.

Uses spatial indices and query optimization to speedup queries over large spatial datasets.

SDBMS may be used by applications other than GIS Astronomy, Genomics, Multimedia information systems, ...

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Evolution of acronym “GIS”

Geographic Information Systems (1980s) Geographic Information Science (1990s) Geographic Information Services (2000s) Fig 1.1

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Three meanings of the acronym GIS

Geographic Information Services Web-sites and service centers for casual users, e.g. travelers Example: Service (e.g. AAA, mapquest) for route planning Geographic Information Systems Software for professional users, e.g. cartographers Example: ESRI Arc/View software Geographic Information Science Concepts, frameworks, theories to formalize use and development of geographic information systems and services Example: design spatial data types and operations for querying 38

Learning Objectives

Learning Objectives (LO) LO1 : Understand the value of SDBMS LO2: Understand the concept of spatial databases LO3: Learn about the Components of SDBMS • Architecture choices • SDBMS components: – data model, query languages, – query processing and optimization – File organization and indices – Data Mining Chapter Sections 1.5 second half 1.6 – entire section 39

Components of a SDBMS

Recall: a SDBMS is a software module that can work with an underlying DBMS supports spatial data models, spatial ADTs and a query language from which these ADTs are callable supports spatial indexing, algorithms for processing spatial operations, and domain specific rules for query optimization Components include spatial data model, query language, query processing, file organization and indices, query optimization, etc.

Figure 1.6 shows these components We discuss each component briefly in chapter 1.6 and in more detail in later chapters.

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Three Layer Architecture

Fig 1.6

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1.6.1 Spatial Taxonomy, Data Models

Spatial Taxonomy: multitude of descriptions available to organize space.

Topology models homeomorphic relationships, e.g. overlap, adjacent Euclidean space models distance and direction in a plane Graphs models connectivity, Shortest-Path Spatial data models rules to identify identifiable objects and properties of space Object model help manage identifiable things, e.g. mountains, cities, land-parcels etc.

Field model help manage continuous and amorphous phenomenon, e.g. temperature, satellite imagery, snowfall etc.

More details in chapter 2.

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1.6.2 Spatial Query Language

• Spatial query language • Spatial data types, e.g. point, linestring, polygon, … • Spatial operations, e.g. overlap, distance, nearest neighbor, … • Callable from a query language (e.g. SQL3) of underlying DBMS SELECT S.name

FROM Senator S WHERE S.district.

Area() > 300 • Standards • SQL3 (a.k.a. SQL 1999) is a standard for query languages • OGIS is a standard for spatial data types and operators • Both standards enjoy wide support in industry • More details in chapters 2 and 3 43

Multi-scan Query Example

•Find all senators who serve a district of are greater than 300 square miles and who own a business within the district • Spatial join example SELECT S.name FROM Senator S, Business B WHERE S.district.Area() > 300 AND Within(B.location, S.district) Fig 1.7

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Multi-scan Query Example

•Find the name of all female senators who own a business •Non-Spatial Join example SELECT S.name

FROM Senator S, Business B WHERE S.soc-sec = B.soc-sec AND S.gender = ‘Female’ Fig 1.7

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1.6.3 Query Processing

• Efficient algorithms to answer spatial queries • Common Strategy - filter and refine • Filter Step:Query Region overlaps with MBRs of B,C and D • Refine Step: Query Region overlaps with B and C Fig 1.8

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Query Processing of Join Queries

•Example - Determining pairs of intersecting rectangles • (a):Two sets R and S of rectangles, (b): A rectangle with 2 opposite corners marked, (c ): Rectangles sorted by smallest X coordinate value • Plane sweep filter identifies 5 pairs out of 12 for refinement step •Details of plane sweep algorithm on page 15 Fig 1.9

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1.6.4 File Organization and Indices

• A difference between GIS and SDBMS assumptions •GIS algorithms: dataset is loaded in main memory (Fig. 1.10(a)) •SDBMS: dataset is on secondary storage e.g disk (Fig. 1.10(b)) •SDBMS uses space filling curves and spatial indices •to efficiently search disk resident large spatial datasets •Memory access: 10ns, disk access 10,000,000ns (2000) Fig 1.10

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Organizing spatial data with space filling curves

•Issue: •Sorting is not naturally defined on spatial data •Many efficient search methods are based on sorting datasets •Space filling curves •Impose an ordering on the locations in a multi-dimensional space •Examples: row-order (Fig. 1.11(a), z-order (Fig 1.11(b)) • Allow use of traditional efficient search methods on spatial data Fig 1.11

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Spatial Indexing: Search Data-Structures

•Choice for spatial indexing: •B-tree is a hierarchical collection of ranges of linear keys, e.g. numbers •B-tree index is used for efficient search of traditional data •B-tree can be used with space filling curve on spatial data •R-tree provides better search performance yet!

•R-tree is a hierarchical collection of rectangles •More details in chapter 4 Fig 1.12: B-tree Fig. 1.13: R- tree 50

1.6.5 Query Optimization

•Query Optimization • A spatial operation can be processed using different strategies • Computation cost of each strategy depends on many parameters •Query optimization is the process of •ordering operations in a query and •selecting efficient strategy for each operation •based on the details of a given dataset •Example Query: SELECT S.name

FROM Senator S, Business B WHERE S.soc-sec = B.soc-sec AND S.gender = ‘Female’ •Optimization decision examples •Process ( S.gender = ‘Female’) before (S.soc-sec = B.soc-sec ) •Do not use index for processing ( S.gender = ‘Female’) 51

1.6.6 Data Mining

• Analysis of spatial data is of many types • Deductive Querying, e.g. searching, sorting, overlays • Inductive Mining, e.g. statistics, correlation, clustering,classification, … • Data mining is a systematic and semi-automated search for interesting non-trivial patterns in large spatial databases •Example applications include •Infer land-use classification from satellite imagery •Identify cancer clusters and geographic factors with high correlation •Identify crime hotspots to assign police patrols and social workers 52

1.7 Summary

SDBMS is valuable to many important applications SDBMS is a software module works with an underlying DBMS provides spatial ADTs callable from a query language provides methods for efficient processing of spatial queries Components of SDBMS include spatial data model, spatial data types and operators, spatial query language, processing and optimization spatial data mining SDBMS is used to store, query and share spatial data for GIS as well as other applications 53

Stuff Due and Next Class

Paper review selection and webpage Team formation and project webpage Prepare on a open book quiz with 2 questions based on today’s topic Next week: spatial concepts and data model, chapter 2 of the book Note: Sep 21, Two paragraphs describing the possible questions. Submit an electronic copy and link it to your project website 54