Introduction to Moving Objects Databases

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Introduction to Moving Objects Databases

• With acknowledgements to
• R.H. Güting and M. Schneider
• for the figures

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Mobile Services

• Mobile services are services that
• are provided to mobile (moving) user
• take data on other mobile (moving) objects into
  consideration
• Example
• Weather or traffic information services to
• users walking with PDAs or moving in their
  cars
• Mobile services are expected to become widely
  spread (and generate a lot of movement data),
  because of continuous advances in (and price
  reduction of)
• Mobile devices
• Positioning technology
• Wireless communications
• Mobile services are based on spatio-temporal
  queries to moving objects databases
• Ex., When the user X will enter the area being
  affected by hurricane Katrina?

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Moving Objects Databases

• Moving objects database is a database that can
  represent moving objects
• Moving object is a geometry that changes over
  time continuosly, for example
• People, animals, sattelites, spacecraft, planets,
  taxi cabs, trucks, airplanes, ships
• Forests, lakes, glaciers, storms, armies, cancer,
  continents, diseases, oil spils
• How to extend database technology to support
  moving object databases?

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Outline of this lecture

• Database Management Systems
• Spatial Databases
• Temporal Databases
• Moving Objects in Databases

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Database Management Systems

• Database management system (DBMS) is a piece of
  software that manages a database
• Database is a repository of interrelated data
  items that are central to the business of an
  enterprise/institution
• Physical level
• Data organization on storage media (files)
• Logical level
• Data model and query/data manipulation language
• E.g., relational data model and SQL
• External views
• Application-specific view of data from logical
  level

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Database Management Systems

• Classical database management systems were
  conceived for relatively simple business
  applications
• E.g., in the relational data model, simple data
  types (integers, floating-point numbers, short
  text strings)
• We would like to widen the scope
• Data Images, geographic maps, music, videos,
  data from scientific experiments, meteorological
  measurements
• Queries
• Retrieve images containing shapes similar to a
  given one
• Produce a map of rainfall over some terrain
• In order to formulate queries in a simple manner
  and to process queries efficiently, we need to
  extend data model and query language

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Limitations of Classical Databases

• No way to represent geometric shapes
• Representation of a region as a collection of
  coordinates in numeric attributes is difficult to
  handle
• No way to represent development of entities over
  time
• Past state of the world is not kept
• No way to represent objects moving right now
• Continuous updates are not feasible

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Outline of this lecture

• Database Management Systems
• Spatial Databases
• Temporal Databases
• Moving Objects in Databases

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Spatial Databases

• Spatial database (DBMS) allows representing and
  querying geometries in a natural way
• Entities to be stored in a spatial database
• Cities, rivers, road networks, landmarks,
  boundaries of countries, hospitals, subway
  stations, forests, corn fields
• Fundamental abstractions
• Single objects point, line, region
• Spatially related collections of objects
  partition, network

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Extending Data Model and Query Language the
ROSE Algebra

• Spatial data type (STD) is a structure (e.g.,
  region)
• Algebra is a collection of abstract data types,
  e.g., STDs, with related operations
• Algebra must be closed under its operations!
• The ROSE algebra
• STDs points, line, region
• Operations
• Intersection line x line ? points
• Minus region x region ? region
• Contour region ? line
• Length line ? real
• Inside points x region ? bool
• Adjacent region x region ? bool

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Extending Data Model and Query Language the
ROSE Algebra

• We embed STDs into a DBMS data model (e.g., into
  a relational model) in the role of attribute
  types
• Sample relations
• Cities (name string, population int, location
  points)
• Rivers (name string, route line)
• Highways (name string, route line)
• States (name string, area region)
• Sample queries
• What is the total population of cities in
  France?
• SELECT SUM(c.pop)
• FROM cities AS c, states AS s
• WHERE c.location inside s.area AND s.name
  FRANCE
• Return the part of the river Rhine that is
  within Germany
• SELECT intersection (r.route, s.area)
• FROM rivers AS r, states AS s
• WHERE r.name RHINE AND s.name GERMANY

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Extending Data Model and Query Language the
ROSE Algebra

• Implementation strategy
• Data structures for STDs
• Algorithms for STDs operations
• Spatial index structures with appropriate access
  methods
• Spatial join methods
• Cost functions for all methods, for use by the
  query optimizer
• Statistics about the distributions of objects in
  space,
• needed for selectivity estimation
• Extension of the optimizer (e.g., translation
  rules)
• User interface extensions to handle presentation
  of spatial data and input of spatial values for
  querying
• Some of these have become available in commercial
  systems
• Good extensibility by attribute data types and
  operations
• Oracle, IBM DB2, Informix, MySQL
• Limited extensibility by index structures
• Limited extensions of the query optimizer

13
Outline of this lecture

• Database Management Systems
• Spatial Databases
• Temporal Databases
• Moving Objects in Databases

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Temporal Databases

• Standard DBMS describe the current state of the
  world
• A change in the world leads to update in the
  database
• Previous state is lost!
• Application can manage time itself
• Explicit attribute of type date or time
• Sample relation
• Employee (name string, department string,
  salary int, start date, enddate)
• Disadvantages of this approach
• Complex query formulation
• Inefficient query processing
• We need to
• integrate temporal concepts deeply into the DBMS
  data model and query language
• extend the implementation of the system to
  archieve efficient execution

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The Time Domain

• Time is a one-dimensional space extending from
  the past to the future
• Different models of time
• Bounded vs. infitite
• Discrete vs. continuous
• Discrete and continuous time
• With continuous time, points in time are
  isomorfic to real numbers
• With discrete time, atomic time intervals
  (chronons) are isomorfic to natural numbers
• Time also can be
• Absolute (anchored), e.g., January 22, 2002,
  1200 PM
• Relative (unanchored), e.g., three weeks
• Temporal data types
• Instant a chronon or a point in time
• Period an anchored interval
• Periods a set of disjoint anchored intervals
  (temporal elements)
• Interval an unanchored time interval of known
  length

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Time Dimension

• Two kinds of time
• Valid time is the time in the real world when an
  event occurs or a fact is valid
• Transaction time is the time when a change is
  recorded in the database or a time interval
  during which a particular state of the database
  exists
• Four types of databases
• Snapshot database
• Standard database
• Valid-time (historical) database
• Transaction-time (rollback) database
• Bitemporal database
• Supports both valid time and transaction time
• Temporal database (DBMS) is a database (DBMS)
  that supports valid time and/or transaction time

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Time Dimension Temporal Relations

• Snapshot relation
• Valid-time relation
• Transaction-time
• relation
• Bitemporal relation

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Extending Data Model

• General approach
• Database facts are associated with timestamps
• Alternatives
• Data model extended
• Relational vs. object-oriented
• Granularity of facts
• Tuples/objects vs. attributes
• Kind of timestamp
• a single chronon (instant)
• a single time period (period)
• a temporal element (periods)
• Time dimension
• Valid time
• Transaction time
• Both dimensions

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Extending Data Model BCDM model

• Data model extended relational
• Granularity of facts tuple
• Kind of timestamp a single chronon
• Time dimension both dimensions

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Extending Query Language TSQL2

• TSQL2
• is a superset of SQL92
• is based on BCDM model
• Sample data definition command
• CREATE TABLE prescription (
• name char(30),
• drug char(30),
• dosage char(30),
• frequency interval minute)
• AS VALID STATE DAY AND TRANSACTION
• Kinds of relations
• Snapshot
• Valid-time
• Transaction-time
• Bitemporal

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Extending Query Language TSQL2

• Sample queries
• What has ever been prescribed any drugs?
• SELECT SNAPSHOT name
• FROM prescription
• What drugs were prescribed to Lisa?
• SELECT drug
• FROM prescription
• WHERE name Lisa
• What drugs have been prescribed together with
  aspirin?
• SELECT p1.name, p2.drug
• FROM prescription AS p1, prescription AS p2
• WHERE p1.drug aspirin AND
  p2.drugltgtaspirin
• AND p1.name p2.name
• Which drugs was Lisa prescribed during 1999?
• SELECT p.drug
• VALID INTERSECT (VALID(p), PERIOD 1999
  DAY)
• FROM prescription AS p
• WHERE p.name Lisa
• What did the physician believe on September 10,
  1998, was Lisas prescription history?

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Outline of this lecture

• Database Management Systems
• Spatial Databases
• Temporal Databases
• Moving Objects in Databases

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Moving Objects in Databases

• Moving object is a geometry that changes over
  time continuosly
• Moving objects database is a database that can
  represent moving objects
• Two perspectives on
• moving objects databases
• Location management perspective
• Spatio-temporal data perspective

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Location Management Perspective

• Location management database is a snapshot
  database that is used to
• maintain dynamically the locations of a set of
  currently moving objects
• ask queries about the current or near-future
  positions of
• the moving objects
• Main task
• Keeping the positions of a set of objects in a
  database,
• e.g., a set of taxi cars, up-to-date
• Scenario
• Moving objects send their current positions to
  the database and
• the database performes updates
• Trade-off
• If updates performed very often, then
• the error is small
• the update load is very high
• If updates performed less frequently, then
• the update load is low
• the error is large
• Solution
• Additionaly, store speed and direction of moving
  objects

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Spatio-Temporal Data Perspective

• Spatio-temporal database is a spatial database
  that keeps history of changes to spatial objects
• Two basic questions
• What kinds of data are stored in spatial-temporal
  databases?
• Point, line, region, network, partition
• Which kinds of change may occur?
• Discrete change any kind of data
• Continuous change point and region
• Moving object is a geometry that experience
  continuous changes
• Two most important abstractions of moving
  objects
• Moving point is an abstraction of a physical
  object moving around in the plane, for which only
  position is relevant
• Moving region is an abstraction of an entity in
  the plane that changes its position, extent, and
  shape

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Extending Data Model and Query Language
Temporal Database with STDs

• Idea
• Use a temporal DBMS and extend it with spatial
  data types (STDs).
• Example TSQL2/BCDM extended with the ROSE
  algebra
• Sample data definition command
• CREATE TABLE real_estate (
• owner char(30),
• area region)
• AS VALID STATE DAY
• Sample query
• Show the properties adjacent to the property
  of Charles Smith as of March 17, 1977
• SELECT r2.area
• FROM real_estate AS r1, real_estate AS r2
• WHERE r1.owner Charles Smith AND
• VALID(r1) OVERLAPS DATE 1977-03-17 AND
• r1.area ADJACENT r2.area
• This approach supports discrete changes, but does
  not support moving objects, i.e., continuous
  changes!

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Extending Data Model and Query LanguageSpatio-Te
mporal Data Types

• Idea
• Use spatio-temporal data types with suitable
  operations
• Spatio-temporal data types mpoint, mregion
• Operations
• Intersection mpoint x mregion ? mpoint
• Distance mpoint x mpoint ? mreal
• Min mreal ?
  real
• Trajectory mpoint ?
  line
• Deftime mpoint ?
  periods
• At_time mregion x instant ?
  region
• At_time mbool x instant ?
  bool
• Length line ?
  real
• Adjacent mregion x mregion ? mbool
• This approach can manage both discrete and
  continuous changes!

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Extending Data Model and Query Language
Spatio-Temporal Data Types

• Sample relations
• real_estate (owner char(30), areamregion)
• flight (id string, from string, to string,
  route mpoint)
• weather(id string, kind string, area mregion)
• Sample queries
• Show the properties adjacent to the property of
  Charles Smith as of March 17, 1977
• SELECT at_time(r2, instant(March 17, 1977))
• FROM real_estate AS r1, real_estate AS r2
• WHERE r1.owner Charles Smith AND
• at_time(adjacent(r1.area, r2.area),
  instant(March 17, 1977))
  true
• Find all flight from Düsseldorf that are longer
  than 5,000 km?
• SELECT id
• FROM flight
• WHERE fromDUS AND length(trajectory(route)) gt
  5000
• Retrieve any pairs of airplanes, which, during
  their flight, came closer to each other than 500
  meters
• SELECT f.id, g.id
• FROM flight AS f, flight AS g
• WHERE f.id ltgt g.id AND min(distance(f.route,g.
  route)) lt 0.5
• At what time was flight BA488 within the
  snowstorm with ID S16?

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Summary

• Moving object databases are databases that allow
  efficient representing and querying of moving
  objects
• Moving object is a geometry that changes
  continuously
• Classical data models and query languages (and
  their implementations) need to be extended to
  support moving objects
• Spatial extension
• Spatial databases STDs with operations (ROSE
  algebra)
• Temporal extension
• Temporal databases BCDM data model and TSQL2
• Spatio-temporal extensions
• Temporal databases with STDs
• Spatio-temporal data types with related
  operations
• Location management database