Group Title: Department of Computer and Information Science and Engineering Technical Reports
Title: A Retrospective analysis of time concepts in temporal databases
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Title: A Retrospective analysis of time concepts in temporal databases
Series Title: Department of Computer and Information Science and Engineering Technical Reports ; 92-044
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Language: English
Creator: Kim, Seung Kyum
Chakravarthy, Sharma
Publisher: Department of Computer and Information Sciences, University of Florida
Place of Publication: Gainesville, Fla.
Copyright Date: 1992
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University of Florida
Computer and Information Sciences


SDepartment of Computer and Information Sciences
c"' Computer Science Engineering Building
SUniversity of Florida
Gainesville, Florida 32611


A Retrospective Analysis of Time
Concepts in Temporal Databases

Seung-Kyum Kim and Sharma Chakravarthy
email: {skk,sharma}@cis.ufl.edu

UF-CIS-TR-92-044
(Submited for publication)

(This work was supported in part by the NSF Research Initiation Grant
(IRI-9011216) and by a Grant from the Florida High Technology and Industrial
Council (UPN# 900911013))










Abstract
In this paper, we propose a formal definition of temporal validity which we believe
is the first and the most comprehensive quantification of that notion. The temporal
validity is developed into the notion of sort of validity with which the confusion among
various time concepts introduced for temporal databases can be dispelled. Further, we
discuss the problem of preserving multiple past states of a temporal database, which
leads to the identification of a maximal set of time concepts and multidimensional
representations. It is shown that the time concept, event time is needed to properly
model retroactive and proactive updates, as it is not possible to model them using the
valid and transaction times as thought earlier. We also show the adequacy of three
time concepts (valid time, event time and transaction time) for completely preserving
different past states generated by retroactive and proactive updates, error corrections,
and delayed updates.


1 Introduction


The temporal database which incorporates time concept into the conventional database has been

investigated by many researchers over a decade [Soo91]. The most predominant direction of the

previous research has been the extension of the relational data model to a temporal one and/or

the extension of the relational query languages SQL and Quel to temporal versions. Work on

this area includes [C('1., Ari86, .... .;, C;..ls, NA89, Sar90, SS91]. Besides the emphasis on

the relational model, recently temporal extensions to the ER (or semantic) model have begun to

emerge in the literature [Klo83, EW90, SS91, TL91]. The formalization of temporal data model

[Ser80, CW83, TC90, C;.I'il] and the physical data organization for temporal databases [LDE+84,

SK86, Ahn86, EWK90, EJK92] have also been investigated.

In our view, earlier attempts at clarifying various time concepts introduced for enriching the

semantics of temporal databases have only added more confusion. Early on, Snodgrass and Ahn

[s-\ ".] proposed three time concepts, valid time, transaction time, and user-defined time, based on

the classification of temporal databases into rollback databases, historical databases, and temporal

databases. However, the confusion concerning time concepts still persists and leads to the following

problems:

1. Classification of time concepts into a dichotomy of reality vs. representation (or real-world vs.

database-world).

2. Employing inappropriate time concepts to capture retroactive and proactive updates.










In this paper, we develop the notions of temporal validity and sort of validity to clarify (1). With

these notions we are able to infer that there is no absolute real-world validity but everything is

interpretation-dependent. Also we show that there may be multiple interpretations for temporal

behavior (validity) of a class of data objects. This implies that the notion of valid time, irrespective

of its name, is enough only as far as the validity is concerned.

As another aspect of time concept, we demonstrate that valid and transaction times are not

sufficient to model retroactive and proactive updates (problem (2)), while they are still adequate

for error corrections and delayed updates. In fact, the event time bearing a revised meaning is

required to capture, in conjunction with the valid time, the precise meaning of retroactive and

proactive updates. We discuss this issue in the context of preservation of multiple past states and

argue that the preservation problem is independent of the temporal validity.

Combining the previous arguments, we conclude that only valid time is needed for a single sort

of validity (one interpretation) and multiple valid times can be employed if multiple interpretations

of temporal validity are required. Apart from this, the valid time needs to be augmented to two-

or three-dimensional time by incorporating the event time and/or the transaction time to preserve

multiple past states of databases. In summary, the valid, event, and transaction times are practically

a maximal set of times needed to completely model the temporal database.


The rest of this paper is organized as follows. In section 2, we give a critical review of previous

work dealing with time notions. In section 3, the notions of temporal validity and sort of validity

are presented. In section 4, we discuss the problem of preserving multiple past states. Finally, in

section 5, we make concluding remarks about our work.


2 Problems with previous work


Of numerous time concepts proposed to date, we select some and highlight the confusion among

them.

Copeland and Maier [C(.I14] introduced two types of time, event time and transaction time.

According to their description, the event time is the time an event, which gives rise to a change










to an object, happens in real world. And the transaction time is the time the change is recorded

in a database. Although not explicitly mentioned, to our best comprehension, it seems that they

were trying to explain two kinds of temporal validity. One is a real-world validity modeled by

the event time. The other, an approximation of the former, is a database-world validity modeled

by the transaction time. For example, let's assume that a faculty member, Smith was promoted

to associate professor in Aug. 1'1", and the fact was recorded in a database in Sep. 1'1". If we

adopt the event time, Smith's associate professorship becomes valid from Aug. 1'' "-. If we take the

transaction time, as they actually did, it becomes valid from Sep. 1',". We will call, tentatively,

the former validity effective validity and the latter ,i. '-i.li,,I .-,-.. validity.


Lum and others [LDE+84] proposed other kinds of time, 1,,;lal time and physical time. Basically,

the logical time is the same as the event time in the sense that both are time of real world, i.e.,

the true time with which an object changes. However, the physical time is different from the

transaction time of [(':.I14], even though the two times have been treated identically as both are

the recording time of data. The physical time was intended to be a reference time for the logical

time to model retroactive and proactive updates. For example, suppose Smith received a promotion

to associate professor in Aug. 1'1"., but the promotion was retroactive from Jan. 1'1,-" Then, the

fact is recorded with logical time of Jan. 1' ". and physical time of Aug. 1',". if indeed recorded

then. We should be able to notice the problem with this approach. What if the fact were recorded

in Sep. or later instead of Aug.? Does that mean Smith received a retroactive promotion in Sep.

or later? In addition, the validity dealt with in [LDE+84] is in essence the same with the effective

validity of [(' .14], provided that the event time and the logical time are for modeling of the same

real-world validity. The difference is that in [LDE+84] they used an additional time to capture the

semantics of retroactive and proactive updates. We will discuss this problem further in section 4.


The valid time and transaction time proposed by Snodgrass and Ahn are also confusing [SA, .]

They distinguished the two times based on the classification of temporal databases into rollback

databases, historical databases, and temporal databases1. That is, the valid time is the time
1This "temporal database" has a narrow meaning in their work that it is a database made by consolidating the
rollback database and the historical database. This term should not be confused with our general meaning of temporal










employed by the historical database, which is described as a database storing history of relations

as it is best known. The transaction time is described as the time employed by the rollback

database, which is a collection of all the snapshot databases. The historical and rollback databases

are distinguished by error corrections also. That is, errors in historical databases can be rectified,

whereas such corrections are not allowed in rollback databases. Here, we can see that the distinction

between historical databases and rollback databases is not so clear in the sense that a collection of

snapshot databases can be regarded as a history of relations. Further, prohibiting error correction

in rollback databases is not meaningful because erroneous database states should be able to get

corrected anyhow. Accordingly, the definitions of valid and transaction times as attributes to

historical and rollback databases are also unclear.

On the other hand, the role of transaction time in the rollback database is different from that of

their temporal counterpart, which employs both valid and transaction times to support retroactive

and proactive updates as well as preservation of all past database activities. The transaction time

in rollback databases is equivalent to the transaction time of [('C.I14] in that rollback databases are

in fact temporal databases using the registration-based validity. In contrast, the transaction time

in their temporal databases is the same with the physical time of [LDE+84] in the sense that both

times are used to model retroactive and proactive updates.

Also, it is noticeable in ['.\'".] that the user-defined time was treated just as a data type

requiring only input/output functions. However, as we shall show in section 3, the user-defined

time models another validity just as effective or registration-based validity.


Yet another confusion arises in the work of Gadia and Yeung [GY-"], in which the valid and

transaction times of [.\' -"] were adopted for the demonstration of their n-dimensional, symmetric

time concept. However, the valid and transaction times of [GY'-] were in fact not those of [SA" .],

but the event and transaction times of [(':.184], respectively.


To summarize, we identify several problems with the previous approaches. First, we think there

has been a dichotomous classification of time concepts, i.e., reality vs. representation (or real-world

database.










vs. database-world) [SA. ".] That dichotomy, together with the absence of a proper quantification of

temporal validity, presumably misled us to the point that there are one genuine real-world validity

and other possible database-world validities, if any, such as the registration-based validity. Second,

we can see that the problem of preserving multiple past states has been intermingled with the

validity problem. They need to be separated. Lastly, unlike the previous view [LDE+84, SA. .],

combination of valid and transaction times cannot model retroactive and proactive updates.


3 Temporal validity


In this section, we present the notions of temporal validity and sort of validity. With these notions,

the validity problem partially discussed in the previous section can be clearly understood.

Throughout this paper we assume an equi-distant discrete time domain T which is used to

represent the temporal validity and is isomorphic to the Gregorian calendar time domain. We call

the time domain valid time domain and call time in the domain valid time. A time interval is

defined to be consecutive time points in T. Now represents the current time and oo denotes a

sufficiently remote future time point.

3.1 A simple data model

Before presenting the formal definition of temporal validity, we first define a simple data model

from which some necessary concepts are drawn. The simple data model is not a complete one, its

sole objective being to give a set-theoretic definition of data value.


Object An object is an entity or a relationship among entities.


Object set An object set is a set of objects. However, not all arbitrary object sets are meaningful

to us. We are interested in only certain object sets which are pre-classified from the universal object

set, consisting of all conceivable objects in the world, according to some criteria.

For example, we may have three object sets, S1, S2, and S3, each of which is a subset of

the universal object set U. The subsets might be pre-classified and named Person, Faculty, and

Furniture, respectively. Then, an unrelated object, such as a stone, will not be included in set S1.










We call such a name a property of the object set. From now on, whenever we say an object set we

mean an object set with property. Also, it is assumed that object sets are pre-classified or identified

by database designers.


Name and value A name is regarded in our work as an identifier for a property. Accordingly,

two different properties have different names. However, an object set may happen to have several

properties, each of which is identified by a distinct name. Further, we do not make any distinction

between name and data value in the simple data model. A value such as 2 may be treated as a

name, and a name such as Smith treated as a value.


Property The definition of property is given below.

A property P of a given object set A is a subset (including empty set) of A, i.e., P C A.2

An object e, e E A, has a property P if e E P.


Property set A property set, P is a meaningful set of properties that can be associated with a

given object set A.

A property set is called a .1'-;.,',.I property set when no element (property) in the set overlaps

the others. That is, if e E Pi, then e Pj, for any P, Pj E P such that i 5 j. If a property set is

not disjoint, it is called an ,,.-, ;1,/,/','/ property set.


For example, given an object set, Person = {el,e2,e3,e4,e65, 6, e7}, three properties can be

defined as follows.

Faculty = {e, e2, e3, e4}, Student = {e5, e6}, Unknown = {e7}.

The property Faculty (an object set) is further specialized into two ways as follows.

Instructor = 0, Assistant = {el}, Associate = {e2, e3}, Full = {e4}, Tenured = {e3, e4}.

Jane = {el}, Smith = {e2}, John = {e3}, Miller = {e4}.
2To be more specific, property P is a name of a subset A' C A. However, such a subtle distinction between a
name of the set and the set itself is not so critical as long as the name is an identifier of the set.










The object set Person has a property set, Poccupation = {Faculty, Student, Unknown}, while the

object set Faculty has two property sets, PF_Rank = {Instructor, Assistant, Associate, Full, Tenured}

and PFName = {Jane, Smith, John, Miller}.3 An object e2 in object sets, Person and Faculty, has

three properties, Faculty, Associate, and Smith, each of which belongs to property sets, POccupation,

PFRank, and PF_Name, respectively. PF_Name is a disjoint property set, whereas PF_Rank is an

overlapping one. Actually, a property set corresponds to a domain of attribute in terms of the

traditional definition.

3.2 Definition of temporal validity

Informally, the temporal validity of a data value for a given object is the object's possession of the

value along the time dimension. For example, the validity of associate professorship for Smith's

rank is a state of whether or not Smith possesses the associate professorship with time.


Temporal validity The temporal validity of property P in an object set A is a characteristic

function of P,

VP: (Ax T) {0,1}

given by

VP, t) If1 ifeeP at t
0VP(et if e P at t

where e is an object in A, t is a time point in the time domain T, P is in P, and P is a property

set of A.

If (e, t) is mapped onto 1 by a characteristic function VP, then property P is said to be valid

for object e at time t by VP. It is assumed that for each property Pi in property set P of object

set A, there exist one or more characteristic functions, such as V1P,, V2P,, and so on.

The use of a characteristic function provides a flexible mechanism for defining temporal validity.

Noted that in a characteristic function the way to decide if e is in P does not matter, as far as the

temporal validity is concerned. The only necessary assumption is that such a function does exist.
3It should be noted that the property Faculty (also an object set) is different from the property set PF_-Name
denotationally, as well as semantically. That is, the former is denoted by {e1, 62, e3, 64} while the latter by
{{e1 }, {2}, {3}, {4}}.









Valid period A valid period, T of property P for object e, characterized by a characteristic

function VP, is defined to be a set of time points t, t E T, such that

{t | VP(e,t)= 1}.

In other words, a valid period is a time interval or a set of disjoint time intervals over which

a given property is valid. A valid period can be pictorially represented as shown in Figure 1.

Figure l(a) shows a valid period of a property, associate professorship for an object, Smith, while

Figure l(b) shows a recurring valid period of a property, $100 (which is in a property set, Stock-

price) for an object, say IBM stock.


1/4/" 6/5/89

Associate

(a)



5/3 5/6 5/9 5/13 5/15
I -----l I-I
$100.00 $100.00 $100.00

(b)


Figure 1: Valid periods


Due to the possibility of multiple characteristic functions for a property, an object may have

several different valid periods for a property. To exhibit this aspect, we define two characteristic

functions, V1P1 and V2P1, for a property P1, where P1 is associate i,'If --", -1. '

ViP(e,t) :

1. Map to 1 if e receives a promotion letter to associate professor and t is equal to the

promotion date written on the letter.

2. Map to 0 if e receives a promotion letter to other rank than associate professor or a

letter of dismissal, and t is equal to the date written on either letter.









3. Map to 1 if e is an associate professor at t 1 and t is different from the date of (2)

if e gets either letter.

V22Pl(e,t) :

1. Map to 1 if e receives a promotion letter to associate professor and t is equal to the

date the letter was ";,v, .

2. Map to 0 if e receives a promotion letter to other rank than associate professor or a

letter of dismissal, and t is equal to the date either letter was signed.

3. Map to 1 if e is an associate professor at t 1 and t is different from the date of (2)

if e gets either letter.

For the two different characteristic functions shown above, we may have different valid periods of

Smith's associate professorship as shown in Figure 2.


1/4/<" 6/5/89
VlP1 : I -I
Associate

12/29/-8 6/7/89
V2Pa : I I
Associate


Figure 2: Two different valid periods of associate-professorship


3.3 Sort of validity

While the temporal validity deals with each individual property, the sort of validity concerns the

totality of all properties in a property set. A sort of validity is, in effect, an interpretation of

temporal behavior of data objects.


Definition: Given a disjoint property set, P = {Pi, P2, P,} and an object set A, a sort of

validity, Sp for P is defined to be a set of characteristic functions,

Sp = {VP1,VjP2, --, VkPn}

such that if ViPm(e,t) = 1, VIPm E Sp, then for all VPw E Sp, VPw # VlPm, VPw(e, t) = 0.










As shown in the definition, for a set of characteristic functions to be a legitimate sort of validity,

a couple of conditions are required to be satisfied.

1. At most one characteristic function maps (e, t) onto 1.

2. The property set P is disjoint.

Condition (1) is necessary to prevent an object from having more than one property simultane-

ously, which belong to a property set. For example, in general it does not make sense for a faculty

member to have two different ranks, say, associate and full, at the same time. In fact, this condition

is the minimum requirement that the characteristic functions defined should satisfy.

Condition (2) is imposed because if the property set is indeed overlapping, the first condition

cannot be satisfied semantically. For example, if the property set is 'PF_Rank defined previously, an

object with property Tenured most likely possesses another property, Associate or Full, too. Thus,

if the characteristic functions are defined so as to fulfill the first condition over such an overlapping

property set, the resultant validity will be semantically wrong. The second condition is actually a

necessary condition for the first one.

In addition, it should be noted that none of the characteristic functions in a sort of validity may

map (e,t)'s to 1. In that case, it is assumed that the object e does not exist at time t.4


Single-sort/multisort validities The definition of sort of validity and the possibility of multiple

characteristic functions for a property lead to multiple sorts of validity. For example, assume that

given a property set P = {P1, P2}, four characteristic functions, V1iP, V2P1, VIP2, and V2P2 are

defined. Then, there may be four sorts of validity out of the characteristic functions, provided that

each combination satisfies the conditions of sort of validity. Two of them are given below.

81 = {VIP1,V 1P2}

82- = {V2P1,V2P2}

When an object set has multiple sorts of validity, as shown above, we say the object set has

multisort validity. If only one sort of validity is defined, the object set is said to have a -' ,i1. -sort
4This assumption might be an over-simplification in that the existence of an object does not depend on the fact
whether the object has a certain property. The simplification, however, keeps the discussion manageable at the
expense of modeling accuracy.









validity. For example, if we define VIP2 and V2P2 in a similar manner to V1P1 and V2P1, respectively,

where P2 is full 1,,rf, --, -. then we may have two different sequences of valid periods for two

sorts of validity, Sip and S2p, as shown in Figure 3.

1/4/',, 6/5/89 6/6/89 5/31/92
$ 1i : I I I
Associate Full


12/29/Sf 6/7/89 6/8/89 5/_s/92
82 I I
Associate Full


Figure 3: Two sorts of validity



On the other hand, either of the remaining combinations, {V2P1, V1p2} and {Vi1P, V2p2} are not

likely to be a sort of validity since as shown in Figure 4 during some time period an object may

have more than one property at the same time.

12/29/S-, 6/7/89
{V2P, V1P2}: I I
Associate
6/6/89 5/31/92
I I
Full


Figure 4: A disallowed combination of characteristic functions


Lastly, we want to emphasize that the sort of validity (as well as the temporal validity) is

developed for an object set. In other words, a sort of validity defined for an object set, in general,

does not apply to other object sets. For example, the sort of validity Sp is not meaningful for an

object set Furniture.


4 Preservation of multiple past states

As the temporal database is intended to capture all past database states including the current one,

preserving past states is essential. However, as we shall see, the preservation of past states is not










guaranteed by just not deleting previously entered data. There are two independent causes that

have a bearing on the preservation. The first is the retroactive update, and the second is the error

correction and delayed update. The reason why the preservation can be incomplete is that these

operations generate multiple pasts. If a temporal database system cannot record and retrieve all

the past states, we will get to lose some past states, that is, the preservation of the past will be

incomplete.

[i ..i'] is regarded as the first comprehensive treatment of the preservation of past states,

although the aspect of retroactive update was mentioned earlier in [LDE+84]. A common problem

in their work is that they made use of the transaction time (the physical time) to deal with

retroactive and proactive updates. In this section, we show that retroactive and proactive updates

cannot be captured by the transaction time, and define another time concept, event time to model

these operations.

4.1 Event time

An event can be viewed as an abrupt l.I,,g.. of database statess. In our work the event has a

slightly tailored meaning that it generates certain relevant facts, or more specifically, it is a cause

of changes to properties of database objects. Thus, when an event happens, it is naturally assumed

that the event changes certain properties of database objects. For example, a promotion event for

a faculty member generates a new fact, i.e., a new rank, and a relocation event generates a new

address. In other words, the two events change properties of a faculty member, to new ones. Now

we are interested in the time when such an event happens. We call the time an event happens event

time.

Incidentally, it should be pointed out that a fact (property) is not necessarily generated by

only one event. That is, two or more different events can generate the same fact. For example, an

address, Gainesville, may be generated by an event of district adjustment such as annexation, as

well as an ordinary relocation event. As a matter of fact, the heterogeneity of events may interfere

with the interpretation of events recorded in a database. However, we do not pursue that issue any

'Such a change need not be an instant one. An event may happen through a fairly long period of time. Or a
seemingly instant event may be viewed as a long term one if the time granularity is sufficiently magnified.










further in our current work. We simply assume that all events changing a property of an object

are of the same kind.


4.1.1 Time difference between an event and its accompanying facts

Although it is preferred that facts generated by an event become valid as soon as the event happens,

there may be situations in which the time a fact becomes valid differs from the time the event

happens (i.e., event time). Based on this time difference, we can classify events into three categories,

on-time events, retroactive events, and proactive events. When a database is updated with a fact

generated by a retroactive event, we call the update a retroactive update. Similarly, if a fact is

generated by a proactive event, we call the update a proactive update.


4.1.2 Classification of events

On-time events This is the most general case. For an event classified into this category, its

relevant facts become valid immediately after the event happens. Alternatively, it may be thought

of as facts become valid simultaneously with the event. For example, if a promotion event which

promotes a faculty member Smith to an associate professor happened on 11/89, unless otherwise

stated, his rank of associate professor would become valid on 11/89.6


Retroactive events For some events, the time a fact becomes valid may be prior to the time

at which the fact is generated, i.e., the time an event generating the fact occurs. We call such

events retroactive events. For example, Smith's promotion might be a retroactive one so that the

promotion became valid from 8/89 while the promotion itself was proclaimed in 11/89. The time

difference is depicted in Figure 5(a). It should be noted that even though the time the fact becomes

valid precedes the time of the event, the fact is never known until the event occurs.


Proactive events In contrast to retroactive events, a fact may become valid at a later time than

the time at which the event generating the fact occurs. We call such events proactive events. As

an example, when it is decided that a research grant is to be awarded to an institute (i.e., an event
6Note that the interpretation concerning when the event happened is determined by a characteristic function
discussed in section 3.















8/89
E .- Valid Time
(a) Associate **. Full
"11/89
1|- Event Time
Promotion Event

8/89
(b) I-
Associate Full

11/89
--------- ( ~ 10/89)
Associate
(c)
8/89
[I (11/89 co)
Associate Full
Event Time

Figure 5: Retroactive event


has now happened), the grant usually will not be effective until a later time. The time difference

for the proactive event is shown in Figure 6. Other examples for the proactive event may be drawn

from a temporal database for weather forecasing. Whenever a forecasting is made (or a fact is

generated), the fact will not be valid until some time later.


4.1.3 Multiple pasts generated by the time difference

The time difference inherent to a retroactive event is one reason for the generation of multiple

pasts. Figure 5(a) depicts a retroactive promotion of Smith. Until 8/89, Smith's rank is no doubt

Associate. After the promotion on 11/89, his rank is Full. But, what about from 8/89 to 10/89?

Before the promotion, the rank during that period was Associate, whereas after the promotion,

the rank was revised to Full. As a result, we have two different pasts during that time period.

Figure 5(b) and (c) show possible representations of the retroactive update.

Figure 5(b) represents the resultant database state after the update, and is in fact the most










7/92
----------IM-----
Not Effective" Effective

10/9"1

Grant-Award Event

Figure 6: Proactive event


reasonable representation for one-dimensional temporal databases. A shortcoming of this represen-

tation is that the intermediate database state, Associate, during 8/89 through 10/89 disappears

and cannot be retrieved. Moreover, the fact that the promotion was retroactive cannot be recalled.

Figure 5(c) shows an alternative in which two histories are kept, one each for the updated

and the previous state. In this approach, the updated state can be retrieved as usual, and the

previous state also can be retrieved if necessary. In addition, the fact that the recorded event was

a retroactive one can be represented in some way and recalled.

A problem with the second approach shown in Figure 5(c) is that one time (or one-dimensional

time) is not sufficient to uniquely qualify a data value at a given time point. For example, at 10/89

Smith's rank may be interpreted as either Associate or Full. However, taking a closer look at the

multiple histories, we can see each history has its own '-.'o',.I effective period in terms of event

time. For example, until 11/89 in event time, only the history of the upper figure in Figure 5(c)

will be effective. Such an event time period is shown on the right side of each history. As such, the

event time is now able to be a qualifier to single a history out of multiple histories. Once a history

is selected, then we can examine it to get a data value at a specific time point. Consequently, if

two time values, one each in the valid time and the event time, are given, we are able to retrieve

a unique data value from a temporal database without losing past states generated by the time

difference of retroactive events.

4.2 Transaction time

In section 2, we discussed the meaning of transaction time and the different roles it played in

temporal databases. The transaction time, in our work, is simply the time at which data values

(facts, properties) are recorded in a database and its role is just to supplement the valid time to










preserve multiple pasts generated by error corrections and delayed updates.


4.2.1 Error corrections

The error correction operation may be the most conspicuous one among those operations generating

multiple pasts. Assume that Smith was promoted to associate professor on 1/' ., but the rank was

wrongly recorded as full professor. The error was found later and corrected on 9/' -. Figure 7(a)

illustrates that situation. The error correction induces two different pasts. During 1/t' through

8/- ". we have an incorrect past in which Smith's rank is viewed as full professor, whereas from 1/. -.

up to now we have a correct past7 where the rank is viewed as associate professor. Figure 7(b) and

(c) show two possible representations for the correction.

Figure 7(b) represents the corrected, most up-to-date state of database, but nothing else. What

we lost in the representation is the incorrect past, i.e., rank of full professor during 1/, -. through

8/ ", which cannot be retrieved after the correction. Moreover, there is no way to recall the fact

that an error was corrected, unless it is stored somewhere else. For many applications, this loss of

past information may not matter at all. However, applications requiring strict audit need to keep

even erroneously created states.

Figure 7(c) shows an alternative, similar to Figure 5(c), where both pasts are maintained. With

this approach we can retrieve the incorrect past information if needed, in addition to the corrected

one. Also, we can discern whether and when an error correction was made. Again, one-dimensional

time is not sufficient to uniquely qualify a data value at a given time point. For example, on 5/ ".

Smith's rank may be construed as either Full or Associate.


4.2.2 Delayed updates

As a fact is generated in real world and recorded in a database, there exists inevitable time delay

between the generation time and the recording time. This delay may vary from a few milliseconds

to several months or more. Due to the delay we, by no means, can avoid temporal inconsistency or

inaccuracy between real world and a modeled database-world, as long as information is retrieved
7Note that the currently correct past is not necessarily indeed correct. It may be corrected again later.










1/85


/


ValLid time


Valid time


Transaction time


Assistant Associate

Figure 7: Error correction


from the stored data. Suppose that Smith was promoted to full professor in 8/89 and the fact was

not recorded until 11/89. Then, until 11/89 we would never know the fact of his promotion, no

matter how ingenious methods were employed. The best we can do is to either minimize such a

delay or keep all the past incorrect database states so as to be brought back whenever an inspection

or audit is necessary.

Figure 8 shows such a delayed update. An event (or a fact) happened in 8/89, which promoted

Smith to full professor, is recorded in 11/89. As a result, during 8/89 through 10/89 the database

keeps an out-dated data value, Associate. When one records the delayed update as if the event

has been occurred just now, the resultant valid period will be Figure 8(b). The drawback of this

approach is obvious. The discrepancy between the intended valid period and the valid period of

stored data will be perpetuated.

Figure 8(c) is an alternative in which the fact is recorded as valid from 8/89 even if the recording

is carried out in 11/89. This is similar to the approach taken in error correction shown in Figure 7(b).

An advantage of this approach is that once recorded, the resultant database correctly reflects the

intended validity. However, as in the case of error correction, we will lose an incorrect past state of


I D
Assistant Full

1/85

Assistant "-Associate

.9/85
1 -
Corrected

1/85

Assistant Associate

1/85 9/85
I I------
Assistant Full

1/85


(a)







(b)





(c)









8/89
I. --
(a) Associate '-. Full
(a)
'.11/89

Updated
Updated


11/89

Associate Full

8/89


Associe
Associate Full


11/89
|-- ------
Associate
8/89
I -
Associate Full

Figure 8: Delayed updat'


Intended valid period







Delayed update 1



Delayed update 2



Incorrect valid period


Correct valid period


e


the database; i.e., the incorrect state during 8/89 through 10/89 will disappear after the update.

Figure 8(d) presents another alternative, in which we have two histories. We keep the incorrect

past state as well as the correct one. By doing so, we are able to alleviate, to a large extent, the

problem of delayed update. Until the delayed data is recorded, we cannot help getting incorrect

information from the database. However, once it is recorded, we will be able to retrieve the correct

one. In addition, we can always access and roll back to the incorrect past state. But, analogous

to the previous cases, one-dimensional time is not sufficient in this approach to uniquely qualify a

data value at a given time point. For example, in 9/89 Smith's rank may be interpreted as either

Associate or Full.


4.2.3 Consolidation of error correction and delayed update

Even though the error correction and the delayed update may seem to have slightly different

temporal behavior, they have one thing in common. That is, the time of error correction and

the time of (delayed) update are the transaction time. Needless to say, the time when an error is


(b)



(c)





(d)
[


am









1/85 8/89
i |I Valid Time
Assistant :. Full Full
Associate
(a)

'9/85 1.1/89
i "- Transaction Time
Updated Corrected Updated (Delayed)


1/85 9/85
(b) I I --------------- ( 8/85)
Assistant Full

1/85 11/89
(c) I I---- (9/85 10/89)
Assistant Associate

1/85 8/89
(d) I I (11/89 co)
Assistant Associate Full
Transaction Time

Figure 9: Consolidation of error correction and delayed update


corrected or a data value is updated is the time when either operation is performed on the database.

This sharing leads to consolidation of error correction and delayed update over the transaction time,

as depicted in Figure 9(a).

Figure 9(a) shows a series of update, correction, and delayed update operations. In 1/'" the

rank was updated to a wrong value, Full. The wrong value was corrected to Associate in 9/' -

Again, the rank was changed to Full in 8/89, but recording of the value was delayed until 11/89.

Figure 9(b), (c), and (d) show multiple histories generated by the operations. Figure 9(b) and (c)

reflect the effect of the correction made in 9/', Before the correction the value (rank) is viewed as

Full. After the correction the value is rectified to Associate as shown in Figure 9(c). The effect of

the delayed update is reflected in Figure 9(c) and (d). Before the update at 11/89, the data value

is wrongly seen (from 8/89) as Associate. After the update a new history shown in Figure 9(d) is

obtained.

Similarly to the earlier discussion on event time, in Figure 9 we can see that each history has

its own .1'-.',,'I effective period in terms of transaction time. For example, during 9/p"'- through










10/89 in transaction time, only the history of Figure 9(c) will be effective. Such a transaction time

period is shown on the right side of each history. Thus, if two time points, one in the valid time

and the other in the transaction time are given, a single data value can be retrieved without losing

past database states generated by error corrections and delayed updates.

4.3 Orthogonality of event time and transaction time

It is interesting to see in previous work ['s.\'-., -..i';] that the retroactive update and the delayed

update have been treated in the same way; in other words, the two were not differentiated at all.

In their work, the time a fact is generated and the time it is recorded are indistinguishable and

represented by the transaction time. In this section, we have established the difference between

the two operations and shown why the notion of event time is required to model retroactive and

proactive updates.

To comprehend the inadequacy of transaction time, let's compare the examples shown in Fig-

ure 5 and Figure 8. In the first case, Smith got a promotion on 11/89, which was retroactively

effective since 8/89. In the second case, he got an on-time promotion in 8/89, but recording of the

fact was procrastinated until 11/89. It is obvious that these two cases, a retroactive event and an

on-time event but record-delayed, are not the same. Note that Figure 5(b) and Figure 8(c) are

seemingly identical although they have different semantics.

More dramatic example can be shown as follows. Consider the proactive event of Figure 6 in

which a research grant is awarded in 10/91 and the grant is effective from 7/92. And assume that

we are trying to model the event with valid time and transaction time. If the proactive event

happens to be recorded on 10/91, it might be regarded as a correct recording for the proactive

event. However, if the recording time happens to be 12/91, 7/92, or 10/92, then the recorded event

will be wrongly interpreted as another proactive event, on-time event, or even retroactive event,

respectively. Figure 10 illustrates that situation.

A more general situation is illustrated in Figure 11. We have four events, Ei, Ej, El, and

Ek, which are, respectively, an on-time event, a retroactive event, another on-time event, and a

proactive event. Facts generated by the events are Pi, Pj, P1, and Pk, respectively. The events










7/92
-----------j Valid Ti
Not Effective ..*.'.' Effective

10/91'
"I ". Event Tii
Grant-Award Event


I I I Transact
12/91 7/92 10/92

Figure 10: Misinterpretation of a proactive event


me



me


ion Time


3 5 10 15
I II I
P, ..P. Pi ..." Pk

3 7 10 12..''
i I'" -
SE, '"E.j "'E.. ':.Ek

3 ".9 '13. 15
1i "1 >I
T, T, Tk 1

Figure 11: A general situation of event-occurring


Valid Time



Event Time



Transaction Time


and its recording


are recorded by transactions, Ti, Tj, T1, and Tk, respectively. Integers represent time points. Note

that Ti is an error correction that adds a missing event El, which happened prior to Ek, to the

database.8

4.4 Classification and maximal set of time concepts


Based on the discussion so far, we can classify the previously proposed time concepts using the

notion of sort of validity and the supplemental time concepts needed to preserve multiple past

states.

As shown in Figure 12, the event time of [(':.IS4], the logical, valid, and user-defined times are

in essence the same time in the sense that they all represent an arbitrary single sort of validity; the

sort of validity is determined by characteristic functions, or more practically, by users' needs.

The transaction time of [('C.I14] and that of ['s-\"] used in rollback databases represent the

8As mentioned before, we regard all the events as being the same kind, such as events of promotion. Especially,
El should not be misunderstood as an event of error-discovering. Such an event, bearing a different semantics, should
have occurred somewhere between time points, 10 and 15.











Reference Time concept Representing sort of validity
[C('.I14] Event any sort of validity
Transaction registration-based validity
[LDE+84] Logical any sort of validity
Physical none (reference time for logical time)
['s. ".] Valid any sort of validity
Transaction in rollback DB registration-based validity
Transaction in temporal DB none (reference time for valid time)
User-defined any sort of validity


Figure 12: Classification of time concepts


registration-based validity discussed in section 2. The definition of characteristic functions for that

validity is perhaps the simplest. For any object e and any property Pi, VP(e, t) will be 1 if t is

equal to the time the recording is carried out, and will keep the value until recording of a different

property Pj is performed. In a real situation, this characteristic function is easily implemented by

automatically setting the start point of valid period of a data value to the system clock. Note that

the registration-based validity is a special case of a single sort validity represented by a valid time.

From the foregoing arguments we can conclude that the valid time alone (we have chosen the

terminology) is enough to represent the temporal validity of data objects unless the preservation of

multiple past states is not a concern. If an application requires a multisort validity, that requirement

can be easily fulfilled by providing multiple valid times in a temporal database system.

On the other hand, the physical time and the transaction time of [s.\- ".] used in their temporal

databases do not represent a sort of validity by themselves. They act as a reference time for the

valid (or logical) time to measure the time difference we discussed in this section. Employing the

two-dimensional time, consisting of valid and transaction times, we are able to preserve multiple

past states produced by error corrections and delayed updates (not by retroactive updates). Never-

theless, it should be noted that only one sort of validity is represented by the two-dimensional time.

For example, Figure 9 illustrates a sort of validity. In the figure, (d) depicts that validity most

precisely, while (b) and (c) show stale ones. As a consequence, supporting multisort validity and

preserving all database states by multidimensional time are two independent things. Accordingly,

it is possible that we have an one-dimensional time and a two-dimensional time to support two










3 5 10 15
I I. | >
P1 'P, P... Pk

3 ''. 9 '13'. 15
I 1 I I >-


Valid Tim



Transacti


T, T Tk T

Figure 13: Combination of valid and transaction times

3 5 10 15
I | | Valid Tim
: P, P PI Pk

3 ..7 10 12..'
I F t |- Event Tim
E, Ej El Ek

Figure 14: Combination of valid and event times


ie



on Time





ie



ie


sorts of validity, if necessary.

Using the time notions introduced in this paper, multidimensional time may be configured in

three ways:

1. Combination of valid and transaction times.

2. Combination of valid and event times.

3. Combination of valid, event, and transaction times.

Using (1), we can preserve multiple histories generated by error corrections and delayed updates.

But we lose incomplete pasts generated by retroactive updates. Of course, the database does not

keep any information concerning when events happened, much less the types of events. Figure 13

shows the roles of valid time and transaction time, which is a simplified view of Figure 11. As we

can see, no information about events exists in Figure 13. However, from the transaction time it is

possible to infer that transaction Ti was an error correction.

Using (2), we can preserve multiple histories generated by retroactive updates. But erroneous

pasts generated by error corrections and delayed updates cannot be preserved. Figure 14 illustrates

the situation simplified from Figure 11 by employing only the valid time and the event time. In

the figure we don't have any information regarding transactions. As a result, it is not possible to

infer that recoding of El was done by an error correction Ti.

Lastly, using all the three times (case (3)), all multiple pasts generated by those operations can










be preserved. This approach will give us the most general interpretation of temporal databases.

However, the generality needs to be weighed against the increased complexity in interpretation from

the users' viewpoint. While the implementation of three-dimensional time is possible, we do not

believe that four- (or higher) dimensional time is manageable and that benefits from the generality

of it would offset the far increasing intricacy of interpretation. From this practical standpoint, we

claim that the valid, event, and transaction times are a maximal set of times needed and can be

implemented in temporal databases.


5 Conclusions


In this paper, we have presented the notions of temporal validity and sort of validity. With these

notions, we have been able to dispel the confusion among various time concepts. In addition, we

have shown that retroactive and proactive updates cannot be modeled by the valid and transaction

times. In order to resolve this problem, we have proposed the event time. Also, we have clarified

the combinations of multidimensional times needed for preserving multiple pasts generated by

retroactive updates, error corrections, and delayed updates. Handling multidimensional times in

temporal databases requires a systematic interpretation methodology. We have extended this work

to two-dimensional representations of time which correspond to the combinations of valid and

transaction times and valid and event times [Kim92]. The notion of valid period is extended to a

valid pattern in the two-dimensional times. Currently, we are investigating the three-dimensional

representation using all the three time concepts.










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