1. CPT-S 580-06 Advanced Databases 11 Yinghui Wu EME 49

2. Temporal databases 2

3. 3 Temporality in Databases Modelling temporal data Temporal operations Temporal integrity constraints Temporal Queries

4. Modelling temporal data 4

5. From Data to Big Data to Long Data Data: current raw facts Temporal data: Data anchored at a time Big data: Gartner’s 3V’s –High volume –High velocity –High variety Long data: Data with a “massive historical sweep” 5

6. 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

7. 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

8. Time Time points:,, … Intervals:,, Temporal element: union of intervals designating the time of events – Three types of data –stepwise constant: salary – valid for certain period of time, changes stepwise –Valid as time instance – sales –Continuous data - voice 8

9. Relations as Cubes 9 A complete history

10. Taxonomy of time Valid time –Time, a data value/fact becomes effective –Valid Transaction time –Transaction time is the time when a fact is stored in the database User defined time: –A user defined and interpreted time

11. 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

12. Time in relation Example of a temporal relation:

13. Temporal DBMS 13

14. Types of Temporal databases Snapshot Databases –A database with no time support Valid Time Database (historical database) –Added valid time dimension (current and past snapshots) Transaction time database (rollback, immutable) –Only new insertions; added transaction time Bi-temporal databases: validation and transaction time 14

15. 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 ….

16. Transaction Time DBs Deletion is logical (never physical deletions!) –When an object is deleted at t2, its temporal attribute changes from [t1, now)  [t1, t.t2) (lifetime) –Object is “alive” from insertion to deletion time, ex. t1 to t2. If “now” then the object is still alive eidsalarystartend 1020K9/1410/15 2050K4/14* 3330K5/146/15 1050K1/14* time

17. Transaction Time DBs Database evolves through insertions and deletions id

18. Transaction Time DBs 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

19. Transaction Time DBs Queries: –Timestamp (timeslice) queries: ex. “Give me all employees at 05/94” –Range-time slice: “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”

20. 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)

21. 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

22. 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

23. 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

24. Bitemporal DBs

25. 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

26. Temporal Queries 26

27. 27 Snapshot query: Query at time instant Temporal query: Query at every time instant Time travel: Going back to a previous time instant to view the database Temporal integrity constraints Temporal Queries

28. 28 Temporal integrity constraints Single state integrity constraints Multi-state integrity constraints –Synchronous multi-state integrity constraint Salary should be larger than X – all states –Asynchronous multi-state integrity constraint Salary is non-decreasing

29. 29 Time Specification in SQL-92 date: four digits for the year (1--9999), two digits for the month (1--12), and two digits for the date (1--31). time: two digits for the hour, two digits for the minute, and two digits for the second, plus optional fractional digits. timestamp: the fields of date and time, with six fractional digits for the seconds field. Times are specified in the Universal Coordinated Time, abbreviated UTC (from the French); supports time with time zone. interval: refers to a period of time (e.g., 2 days and 5 hours), without specifying a particular time when this period starts; could more accurately be termed a span.

30. 30 Temporal Query Languages Predicates precedes, overlaps, and contains on time intervals. Intersect can be applied on two intervals, to give a single (possibly empty) interval; the union of two intervals may or may not be a single interval. A snapshot of a temporal relation at time t consists of the tuples that are valid at time t, with the time-interval attributes projected out. Temporal selection: involves time attributes Temporal projection: the tuples in the projection inherit their time- intervals from the tuples in the original relation. Temporal join: the time-interval of a tuple in the result is the intersection of the time-intervals of the tuples from which it is derived. It intersection is empty, tuple is discarded from join.

31. 31 Temporal Query Languages (Cont.) Functional dependencies must be used with care: adding a time field may invalidate functional dependency A temporal functional dependency x  Y holds on a relation schema R if, for all legal instances r of R, all snapshots of r satisfy the functional dependency X  Y. 

32. 32 Temporal projection Temporal projection is similar to standard projection, except that the restriction applies to only the non- temporal attributes. Both timestamp columns cannot be excluded in the resultant history. After temporal projection, folding is enforced in order that adjoining intervals should be merged into a single interval in the resultant relation.

33. 33 Temporal selection Adds the following new construct to standard SQL: selection based on temporal comparisons of timepoints and intervals using terms in a WHEN clause. The WHEN clause is used to express the temporal part of a query. The temporal comparison in the WHEN clause has the following form: WHEN a interval_compare_operator b where a,b are intervals and interval_compare_operator can be one of the keywords: BEFORE, AFTER, DURING, EQUIVALENT, ADJACENT, OVERLAPS, PRECEDES, and FOLLOWS.

34. 34 Temporal join This join has the most special semantics: the valid- time intervals of the resultant table are created from the intersection of the overlapping valid-time elements of the tables specified in the join. Assumption: The valid time component in each temporal table must be well-defined before performing such joins.

35. 35 To perform joining two temporal tables: –first assemble the non-temporal columns. The columns are assembled by generating the cross product of the non-temporal columns from the operand tables, and then excluding rows that do not satisfy the conditions in the WHERE and WHEN. –Then, examine the source tuples for each candidate tuple in the reduced cross product to see if their valid time periods overlaps. - If they overlap, the candidate tuple is included and the result of the intersection of two valid time periods is used as the valid time period of the new tuple. - otherwise excluded from the result of the join. Temporal join

36. 36 An example of temporal join PROBLEMLIST Patient ProblemTSTE ---------------------------------------------------------------------- J. SmithP114/Feb/20151/Mar/2015 J. SmithP210/Mar/2015Now P. JonesP31/Apr/201512/May/2015 R. FranksP313/Feb/20151/Jun/2015 DRUGS PatientDrugVSVE ----------------------------------------------------------------------- J. SmithD120/Mar/201512/May/2015 P. JonesD11/Apr/20156/Jun/2015 R. FranksD24/Feb/201514/May/2015 “Show all problem and drug combinations for patient”

37. 37 TEMPORAL SELECT T1.Patient, T1.Problem, T2.Drug FROM PROBLEMLIST AS T1, DRUGS AS T2 WHERE T1.Patient = T2.Patient The resultant table: Patient Problem DrugTSTE ------------------------------------------------------------------------------ J. Smith P2 D1 20/Mar/2015 12/May/2015 P. Jones P2 D1 1/Apr/2015 12/May/2015 R. Franks P3 D2 13/Feb/2015 14/May/2015