SERIAL ARRIVAL PREDICTION CODING

A SERIAL PREDICTIVE MODEL FOR USE BY SYSTEM DESIGNERS

AUDREY N. GR~XCH

University of Minnesota Libraries, Research and Development Dept., Minneapolis, MN 55455, U.S.A

(Received 30 September 1975)

Abstract-This article describes a model conceived as an aid to the designer of serials management systems for structuring the serial arrival prediction, check-in, and claiming functions of these systems. The model is described with examples of its application to known serial issue arrival cases in a table of examples.

INTRODUCTION

Many different serials management systems have been devised and programmed in which the computer is instructed to produce expected serial issue arrival information. This data is then used to produce input to the system denoting receipt of the specific serial issue or its lack of receipt, thus alerting system operators of the necessity of claiming from the publisher. In batch systems this data takes the form of a prepunched arrival card or a list of issues expected with preparation of corresponding transaction cards or other keyed input. In an on-line system, the terminal would display the information for an operator’s action.

In some systems of this type the coding scheme can predict series, volume, issue and part numbers and their corresponding calendar date labels for given regular publication cycles. In simpler systems prediction is restricted to identifying the number of expected issues for a given serial with the corresponding specific piece labelling added upon receipt or claim by the operator. Depending upon the library’s need and system design resources automatic prediction of arriving issues ranges from the simple to the sophisticated.

Although some operational systems have included documentation in either article or report form on their specific method of controlling the predictive portion of their systems, this investigator has not seen a single specific model statement of the problem. It would be useful to the system designer to be able to see the problem defined in the form of a flexible model so that an appropriate level of capability can be easily built into a specific library’s system. Moreover, the individual designer may also need to weigh how much sophistication is needed based on a number of factors affecting the individual system such as:

(a) on-line or batch mode operation, (b) size of the current serial data base, (c) user desires and experiences, and (d) system development resources and support.

For example, it may be that for on-line operational mode serials management systems less prediction precision may be needed as the system operator can alter the information provided by the system easily if it is incorrect or imprecise. However, on the other hand it may be demonstrated that great precision is needed if automatic claiming features are to be built into an on-!ine system.

The serial predictive model presented here is comprised of two parts: Part 1. Predictive

frequency code and library arrival adjustment factor; Part 2. Holdings previously predicted labels and values. Basic assumptions and presentation of the model follows with examples of the use of the scheme to portray some examples of actual serial arrival conditions to be found in this publication field.

THE SERIAL PREDICTIVE MODEL BACKGROUND DESCRIPTION

The model assumes that the Predictive Frequency Code and its corresponding value on output produces as near to equal the number of issues or issues and parts as announced or determined by past receipt history of the serial as is possible. If the serial is published on a regularly delayed

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basis-i.e. issues labelled January normally arrive in March-then the Library Arrival Adjustment Factor is employed to alter the physical time for the computer to produce the predictive frequency as it is coded for the publication and as it is labelled or announced. The adjustment so made may be a certain number of weeks or months depending upon which unit value is chosen.

The Holdings Previously Predicted are composed of L,uhels and V&es. The Label determines the proper terminology to be applied to the issue’s identifying numbering scheme. The Values determine the proper increment to be given to the Labels corresponding to volume, issue, part, etc. of a serial. The Values Increment is stored to determine the number of subunits which occur in a nested hierarchical series of Labels, i.e. n volumes within a series, n issue numbers within a volume, n parts within an issue number, n pages to a signature. The Lubels may be any terms to be associated with the given hierarchy of the numbering scheme.

Detailed description Purt I. Predictive frequency code und library arrivul udjustment factor. This portion of the

model is shown in Fig. I. as a fourteen byte field with byte 0 used to define the broad frequency category, bytes l-12 defining specific codes controlling the predictions to be made for each broud frequency category. and byte 13 containing a single byte code for the library arrival adjustment

0 1 2 3 4 5 6 7 8 9 10 11 12 13

t‘ v ‘t

L

I.___-_

Byte 13 Library Arrival Adjustment Factor which may be Implemented delaying the predIctIon of the coded frequency in bytes O-12 by monthly or weekly increments

Bytes 1-12 contain values for each Broad Frequency Category as defined I” Table 1

Byte 0 contains Broad Frequency Category as defined I” Table 1

Fig. 1, The serial predictive model Part I. Predictive frequency code and Ilbrar); arrival adjustment factor

factor. In a simple system byte 0 could be used alone merely to determine the required number of transactions to be expected for the specific title. In a more sophisticated system bytes l-12 could be used to provide wide flexibility in the labels and values predicted. The addition of the library arrival adjustment factor in byte I3 would provide the utmost in flexibility for a system to synchronize its predictions to the actual time in which a given library normally receives an issue.

Table 1 gives the code values for Part I of the model-first for the broad frequency categories contained in byte 0 and then the specific codes to be used in bytes I-12 for each code used in byte 0. Table 2 shows examples of these codes for Part I of the model together with an explanation of the interpretation of the code example. Any convenient series of single byte codes to denote the number of weeks or months to delay prediction can be used by the system designer for the library arrival adjustment fuctor in byte 13. For this reason no code examples have been given in this

article for this portion of the model. Part 2. Holdings previously predicted labels and values. The second part of the model stores

the Labels associated with the nested hierarchy of the given numbering scheme used by the serial and the Values denoting the number of subunits required to consider a subunit complete.

Figure 2 shows the component parts of this portion of the model. These are the Term Pattern Identifier, the Volume Anulog, and the Term Levels.

The Term Pattern Identifier consists of a two byte code identifying the string of hierarchical Labels associated with the numbering scheme of the serial. In work done by the author, fifty-five distinct term patterns have emerged from sampling studies conducted from current serial subscriptions at the University of Minnesota Libraries. It is believed by this investigator that more term patterns may be identified in the future but that it would be unlikely that the size of such a table of values would more than double over a normal system lifetime. Therefore, this data would be easily stored in a computer table of IO0 entries not exceeding 6000 bytes.

Serial arrival prediction coding

Table I. Code values for Part 1. Predictive frequency code in Fig. 1

Byte 0. - Broad Frequency Category

Monthly (no more than 1 is- sue pre- dicted each month)

Monthly (no dates)

SemL - Monthly

Bi-Weekly

Fortnightly (every 10 days)

Weekly

Daily

Daily Except Sunday

Daily on Weekdays

NO predlcti ta be made

{tes used to encode the season will de- ermine which month in which the predic- ion will occur.

ytes used to encode months will deter- ine in which months predictions will 3CUT.

ame as above but no date Label pre- icted

ytes used will determue days of ,nth or days of week to be pre- icted.

me as semi-monthly. 3me as semi-monthly.

yte used will determine weekly issue abelling.

ytes used will determine periods in hich prediction is to be suspended for ne to n - months automatically.

3me as Daily.

Not used.

Bytes l-12 Specific Codes for Each Broad Frequency Category

Code A - Seasonal (Byte 0)

Blank - NO prediction for that month.

Bytes 1-12 correspond to months January - December,

values l-9 equal number of parts to the issue to be

predicted during that month.

Bytes l-3 produce predictron of issue labeled "Winter"

Bytes 4-6 '1 ,I II ,I "spring"

sytes 7-9 w ,1 II ,I "Summer"

Byte5 10-12 1, II 11 ,I "A"tum""or"Fall"

Codes B and C - Monthly (Byte 01

alank - NO prediction for that Month.

w l-12 correspond to months January - December.

Alphabetic codes for single and two part issues

and two-sir month combined issues are empl.oyed

with the byte position of the code determining

in which month a predIction will occur. "se of

the multiple issue number codes will cause two to

six integer increments in the issue numbering

predicted as below:

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The uolume unulol: permits the model to recognize which specific ret-m Lrcel in the hierarchy is considered the volume for binding or physical completeness purposes-even though that level may carry a label or hierarchical sequence different than volume, i.e. Lieferung, tom, Session, etc. and not necessarily be the highest level in the hierarchy.

The Term Levels are identified as A,~,C,~,~,~, where A is the highest level term in the hierarchy proceeding through the string until no further levels are needed. Through various sampling studies connected with serial system development at Minnesota the author has determined that six hierarchical levels will encode all known serial holdings Label and Value schemes. Figure 2 shows the term levels A.. . F in an example of a defined term pattern known to exist as a hierarchjcal numbering scheme, with the labels for each Term Lrwl and the V~l~~.s associated with the Turn Levels where appropriate.

In a system employing this model the actual holdings statements stored for each given issue or completed hierarchical string of issues would require defining the holdings scheme to consist of a term pattern identifier and the publisher’s numbering hierarchy stored in appropriate Term Level fields. These fields could be again defined as four byte fixed length subfields for each of the six

Serial arrival prediction coding

Table 2. Examples of Part I. Codes within the predictive model for bytes O-I?

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levels with the term pattern identifier a two byte fixed subfield giving a total field length maximum of 26 bytes. Of course not all term patterns use all six Term Leuels so that for many term patterns the holding statement field would vary from a minimum of 6 bytes to a maximum of 26 bytes.

From the table of term patterns stored by the system in this model, the system would under appropriate programming proceed to use a given defined field length corresponding to the term pattern specified. Conversely upon input, natural language form representations of the term pattern could be checked against the table and the system would automatically assign the term pattern and enter the actual holding L&MY in the appropriate Term Levels for that pattern. Upon a change in the term pattern the model system would convert and store the new term pattern and its actual holding values in a similar manner.

CONCLUSION

This theoretical system model has been developed to give serial system designers a view of how the complexities of the serial issue arrival prediction and associated holdings specification parameters can be handled in a system. Depending upon the scope and size of the serial data base the designer may choose to implement a simple version of the model or may choose some point on a continuum intermediate to the full model. And finally, the coding scheme associated with the model may be changed or adapted to reflect the specific computer hardware configuration upon which the system will be hosted.