tempDB.ppt

Outline
 Spatial Databases
 Indexing, Query processing
 Temporal Databases
 Spatio-temporal
 ….

Temporal DBs – Motivation
 Conventional databases represent the state of an enterprise at a single
moment of time
 Many applications need information about the past
 Financial (payroll)
 Medical (patient history)
 Government
 Temporal DBs: a system that manages time varying data

Comparison
 Conventional DBs:
 Evolve through transactions from one state to the next
 Changes are viewed as modifications to the state
 No information about the past
 Snapshot of the enterprise
 Temporal DBs:
 Maintain historical information
 Changes are viewed as additions to the information
stored in the database
 Incorporate notion of time in the system
 Efficient access to past states

Temporal Databases
 Temporal Data Models: extension of
relational model by adding temporal
attributes to each relation
 Temporal Query Languages: TQUEL, SQL3
 Temporal Indexing Methods and Query
Processing

Taxonomy of time
 Transaction time databases
 Transaction time is the time when a fact is
stored in the database
 Valid time databases:
 Valid time is the time that a fact becomes
effective in reality
 Bi-temporal databases:
 Support both notions of time

Example
 Sales example: data about sales are stored at the
end of the day
 Transaction time is different than valid time
 Valid time can refer to the future also!
 Credit card: 03/01-04/06

Transaction Time DBs
 Time evolves discretely, usually is associated with the
transaction number:
 A record R is extended with an interval [t.start, t.end).
When we insert an object at t1 the temporal attributes
are updated -> [t1, now)
 Updates can be made only to the current state!
 Past cannot be changed
 “Rollback” characteristics
T1 -> T2 -> T3 -> T4 ….

 Deletion is logical (never physical deletions!)
 When an object is deleted at t2, its temporal attribute
changes from [t1, now)  [t1, t2) (i.e. it updates its
interval)
 Object is “alive” from insertion to deletion time, ex. t1
to t2. If the value is “now” then the object is still alive
eid salary start end
10 20K 9/93 10/94
20 50K 4/94 *
33 30K 5/94 6/95
10 50K 1/95 *
time

1 2 4 8 10 15 16 17 25 28 30 33 41 42 45 47 48 51 53
u
b
f
c
d
g
p
j
k
i
m
e
Database evolves through insertions and deletions
id

 Requirements for index methods:
 Store past logical states
 Support addition/deletion/modification changes
on the objects of the current state
 Efficiently access and query any database state

 Queries:
 Timestamp (timeslice) queries: ex. “Give me all
employees at 05/94”
 Range-timeslice: “Find all employees with id
between 100 and 200 that worked in the
company on 05/94”
 Interval (period) queries: “Find all employees
with id in [100,200] from 05/94 to 06/96”

Valid Time DBs
 Time evolves continuously
 Each object is a line segment representing
its time span (eg. Credit card valid time)
 Support full operations on interval data:
 Deletion at any time
 Insertion at any time
 Value change (modification) at any time (no
ordering)

Valid Time DBs
 Deletion is physical:
 No way to know about the previous states of
intervals
 The notion of “future”, “present” and “past”
is relative to a certain timestamp t

Valid Time DBs
Iy
Iz
Ix
Iw
valid-time axis
previous collection
Iy
Ix
Iw
valid-time axis
new collection
The reality “best know now !”

Valid Time DBs
 Requirements for an Index method:
 Store the latest collection of interval-objects
 Support add/del/mod changes to this collection
 Efficiently query the intervals in the collection
 Timestamp query
 Interval (period) query

Bitemporal DBs
 A transaction-time Database, but each record is an
interval (plus the other attributes of the record)
 Keeping the evolution of a dynamic collection of
interval-objects
 At each timestamp, it is a valid time database

Bitemporal DBs
Iy
Ix
v
Iy Iz
Ix
v
Iy Iz
Ix
Iw
v
Iy
Ix
Iw
v
Iy
Ix
Iw
v
t
t1 t5
t4
t3
t2
C(t1) C(t5)
C(t4)
C(t3)
C(t2)

Bitemporal DBs
 Requirements for access methods:
 Store past/logical states of collections of objects
 Support add/del/mod of interval objects of the
current logical state
 Efficient query answering

Temporal Indexing
 Straight-forward approaches:
 B+-tree and R-tree
 Problems?
 Transaction time:
 Snapshot Index, TSB-tree, MVB-tree, MVAS
 Valid time:
 Interval structures: Segment tree, even R-tree
 Bitemporal:
 Bitemporal R-tree

Temporal Indexing
 Lower bound on answering timeslice and
range-timeslace queries:
 Space O(n/B), search O(logBn + s/B)
 n: number of changes, s: answer size, B
page capacity

tempDB.ppt

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tempDB.ppt