IETF media feature registration WG Graham Klyne
Internet draft Content Technologies Ltd.
5 May 1998
Expires: 5 November 1998
An algebra for describing media feature sets
<draft-ietf-conneg-feature-algebra-01.txt>
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Copyright (C) 1998, The Internet Society
Abstract
A number of Internet application protocols have a need to provide
content negotiation for the resources with which they interact [1].
A framework for such negotiation is described in [2]. Part of this
framework is a way to describe the range of media features which
can be handled by the sender, recipient or document transmission
format of a message. A format for a vocabulary of individual media
features and procedures for registering media features are
presented in [3].
This document describes an algebra which can be used to define
feature sets which are formed from combinations and relations
involving individual media features. Such feature sets are used to
describe the media feature handling capabilities of message
senders, recipients and file formats.
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Table of contents
1. Introduction.............................................2
1.1 Structure of this document ...........................3
1.2 Discussion of this document ..........................4
1.3 Ammendment history ...................................4
1.4 Unfinished business ..................................4
2. Terminology and definitions..............................4
3. Media feature values.....................................5
3.1 Complexity of feature algebra ........................5
3.2 Sufficiency of simple types ..........................6
3.2.1 Unstructured data types..........................6
3.2.2 Cartesian product................................6
3.2.3 Disciminated union...............................7
3.2.4 Array............................................7
3.2.5 Powerset.........................................8
3.2.6 Sequence.........................................8
4. Feature set predicates...................................8
4.1 An algebra for data file format selection ............9
4.1.1 Describing file format features..................10
4.1.1.1 Feature ranges 10
4.1.1.2 Feature combinations 11
(A) Additional predicates 11
(B) Meta-features as groupings of other features 12
4.1.2 Content, sender and recipient capabilities.......12
4.2 Conclusion and proposal ..............................12
5. Indicating preferences...................................13
5.1 Combining preferences ................................13
5.2 Representing preferences .............................14
6. Feature set representation...............................15
6.1 Text string representation ...........................16
6.2 ASN.1 representation .................................17
7. Security considerations..................................18
8. Copyright................................................19
9. Acknowledgements.........................................19
10. References..............................................19
11. Author's address........................................21
1. Introduction
A number of Internet application protocols have a need to provide
content negotiation for the resources with which they interact [1].
A framework for such negotiation is described in [2]. A part of
this framework is a way to describe the range of media features
which can be handled by the sender, recipient or document
transmission format of a message.
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Descriptions of media feature capabilities need to be based upon
some underlying vocabulary of individual media features. A format
for such a vocabulary and procedures for registering media features
are presented in [3].
This document defines an algebra which can be used to describe
feature sets which are formed from combinations and relations
involving individual media features. Such feature sets are used to
describe the media handling capabilities of message senders,
recipients and file formats.
The feature set algebra is built around the principle of using
feature set predicates as mathematical relations which define
constraints on feature handling capabilities. The idea is that the
same form of feature set expression can be used to describe sender,
receiver and file format capabilities. This has been loosely
modelled on the way that the Prolog programming language uses Horn
Clauses to describe a set of result values.
In developing the algebra, axamples are given using notation drawn
from the C and Prolog programming languages. A syntax for
expressing feature predicates is suggested, based on LDAP search
filters.
1.1 Structure of this document
The main part of this draft addresses the following main areas:
Section 2 introduces and references some terms which are used with
special meaning.
Section 3 discusses constraints on the data types allowed for
individual media feature values.
Section 4 introduces and describes the algebra used to construct
feature set descriptions with expressions containing media
features. The first part of this section contains a development of
the ideas, and the second part contains the conclusions and
proposed algebra.
Section 5 introduces and describes extensions to the algebra for
indicating preferences between different feature sets.
Section 6 contains a description of recommended representations for
describing feature sets based on the previously-described algebra.
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1.2 Discussion of this document
Discussion of this document should take place on the content
negotiation and media feature reagistration mailing list hosted by
the Internet Mail Consortium (IMC):
Please send comments regarding this document to:
ietf-medfree@imc.org
To subscribe to this list, send a message with the body 'subscribe'
to "ietf-medfree-request@imc.org".
To see what has gone on before you subscribed, please see the
mailing list archive at:
http://www.imc.org/ietf-medfree/
1.3 Ammendment history
00a 11-Mar-1998
Document initially created.
01a 05-May-1998
Mainly-editorial revision of sections describing the
feature types and algebra. Added section on indicating
preferences. Added section describing feature predicate
syntax. Added to security considerations (based on fax
negotiation scenarios draft).
1.4 Unfinished business
. Array values: are they needed? (section 3.2.4)
. Use of unknown data types for feature values (section 6)
. Is a test for presence of a feature required? (section 6)
. Should ASN.1 representation be pursued? If so, should it be
aligned with LDAP filter representation? (section 6.2)
2. Terminology and definitions
Feature Collection
is a collection of different media features and
associated values. This might be viewed as describing a
specific rendering of a specific instance of a document
or resource by a specific recipient.
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Feature Set
is a set of zero, one or more feature collections.
Feature set predicate
A function of an arbitrary feature collection value which
returns a Boolean result. A TRUE result is taken to mean
that the corresponding feature collection belongs to some
set of media feature handling capabilities defined by the
predicate.
Other terms used in this draft are defined in [2].
3. Media feature values
This document assumes that individual media feature values are
simple atomic values:
. Boolean values
. Enumerated values
. Numeric values
More complex media feature values might be accommodated, but they
would (a) be undesirable because they would complicate the algebra,
and (b) are not necessary.
These statements are justified in the following sub-sections.
3.1 Complexity of feature algebra
Statement (a) above is justified as follows: predicates constructed
as expressions containing media feature values must ultimately
resolve to a logical combination of feature value tests.
A full range of simple tests for all of the data types listed above
can be performed based on just two fundamental operations: equality
and less-than. All other meaningful tests can be constructed as
predicates incorporating these two basic tests.
For example:
( a != b ) iff !( a == b )
( a <= b ) iff !( b < a )
( a > b ) iff ( b < a )
( a >= b ) iff !( a < b )
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If additional (composite) data types are introduced, then
additional operators must be introduced to test their component
parts: the addition of just one further comparison operator
increases the number of such operators by 50%.
3.2 Sufficiency of simple types
To justify statement (b), let us first review the range of
composite data types that might reasonably be considered.
In 1972, a paper "Notes on data structuring" by C. A. R. Hoare was
published in the book "Structured Programming" [4]. This was an
early formalization of data types used in programming languages,
and its content has formed a sufficient basis for describing the
data types in almost every programming language which has been
developed. This gives good grounds to believe that the type
framework is also sufficient for media features.
The data types covered by Hoare's paper are:
. Unstructured data types: (integer, real, enumeration, ordered
enumeration, subranges).
. Cartesian product (e.g. C 'struct').
. Discriminated union (e.g. C 'union').
. Array.
. Powerset (e.g. Pascal 'SET OF').
. Sequence (e.g. C string, Pascal 'FILE OF').
To demonstrate sufficiency of simple types for media features we
must show that the feature-set defining properties of these
composite types can be captured using predicates on the simple
simple types described previously.
3.2.1 Unstructured data types
The unstructured data types noted correspond closely to, and can be
represented by the proposed simple value types for media features.
3.2.2 Cartesian product
A cartesian product value (e.g. resolution=[x,y]) is easily
captured as a collection of two or more separately named media
features (e.g. x-resolution=x, y-resolution=y).
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3.2.3 Disciminated union
A discriminated union value is an either/or type of choice. For
example, a given workstation might be able to display 16K colours
at 1024x768 resolution, OR 256 colours at 1280x1024 resolution.
These possibilities are captured by a logical-OR of predicates:
( ( x-resolution <= 1024 ) &&
( y-resolution <= 768 ) &&
( colours <= 16384 ) ) ||
( ( x-resolution <= 1280 ) &&
( y-resolution <= 1024 ) &&
( colours <= 256 ) )
3.2.4 Array
An array represents a mapping from one data type to another. For
example, the availability of pens in a pen plotter might be
represented by an array which maps a pen number to a colour.
If the array index which forms the basis for defining a feature set
is assumed to be a constant, then each member can be designated by
a feature name which incorporates the index value. For example:
Pen-1=black, pen-2=red, etc.
Another example where an array might describe a media feature is a
colour palette: an array is used to associate a colour value (in
terms of RGB or some other colour model) with a colour index value.
In this case is is possible to envisage a requirement for a
particular colour to be loaded in the palette without any knowledge
of the index which maps to it.
In this case, the colour might be treated as a named Boolean
attribute: if TRUE then that colour is deemed to be available in
the pallette
Feature selection based on a variable array index is more
difficult, but it is believed that this is not required for media
selection.
[[I cannot think of any example of feature selection which involves
a variable index into an array. If such a feature is presented, an
array type could be added to the set of allowable media feature
types, and an array selection operator added to the algebra.]]
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3.2.5 Powerset
A powerset is a collection of zero, one or more values from some
base set of values. A colour palette may be viewed as a powerset
of colour values, or the fonts available in a printer as a powerset
of all available fonts.
A powerset is very easily represented by a separate Boolean-valued
feature for each member of the base set. The value TRUE indicates
that the corresonding value is a member of the powerset value.
3.2.6 Sequence
A sequence is a list of values from some base set of values, which
are accessed sequentially.
A sequence can be modelled by an array if one assumes integer index
values starting at (say) 1 and incrementing by 1 for each
successive element of the sequence.
Thus, the considerations described above relating to array values
can be considered as also applying (in part) to sequence values.
That is, if arrays are deemed to be adequately handled, then
sequence values too can be handled.
4. Feature set predicates
A model for data file selection is proposed, based on relational
set definition and subset selection, using elements of the Prolog
programming language [5] as a descriptive notation for this
purpose.
NOTE: The use of Prolog as a syntax for feature
description is NOT being proposed; rather, the Prolog-
like notation is used to develop the semantics of an
algebra. Once the semantics have been developed, they
can be mapped to some convenient syntax.
For the purposes of developing this algebra, examples are drawn
from the media features described in "Media Features for Display,
Print, and Fax" [6], which in summary are:
pix-x=n (Image size, in pixels)
pix-y=m
res-x=n (Image resolution, pixels per inch)
res-y=m
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UA-media= screen|stationary|transparency|envelope|
continuous-long
papersize= na-letter|iso-A4|iso-B4|iso-A3|na-legal
color=n (Colour depth in bits)
grey=n (Grey scale depth in bits)
4.1 An algebra for data file format selection
The basic idea proposed here is that a feature capability of the
original content, sender, data file format or recipient is
represented as a predicate applied to a collection of feature
values. Under universal quantification (i.e. selecting all
possible values that satisfy it), a predicate indicates a range of
possible combinations of feature values).
This idea is inherent in Prolog clause notation, which is used in
the example below to describe a predicate
'acceptable_file_format(File)' which yields a set of possible file
transfer formats using other predicates which indicate the file
formats available to the sender and feature capabilities of the
file format, original content:
acceptable_file_format(File) :-
sender_available_file_format(File),
match_format(File).
match_format(File) :-
pix_x(File,Px), content_pix_x(Px), recipient_pix_x(Px),
pix_y(File,Py), content_pix_x(Py), recipient_pix_y(Py),
res_x(File,Rx), content_res_x(Rx), recipient_res_x(Rx),
res_y(File,Ry), content_res_y(Ry), recipient_res_y(Ry),
colour(File,C), content_colour(C), recipient_colour(C),
grey(File,G), content_grey(G), recipient_grey(G),
ua_media(File,M),
content_ua_media(M),
recipient_ua_media(M),
papersize(File,P),
content_papersize(P),
recipient_papersize(P).
Essentially, this selects a set of file transfer formats from those
available ('sender_available_file_format'), choosing any whose
feature capabilities have a non-empty intersection with the feature
capabilities of the original content and the recipient.
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4.1.1 Describing file format features
The above framework suggests a file format is described by a set of
feature values. As an abstract theory, this works fine but for
practical use it has a couple of problems:
(a) description of features with a large number of possibilities
(b) describing features which are supported in specific
combinations
A typical case of (a) would be where a feature (e.g. size of image
in pixels) can take any value from a range. To present and test
each value separately is not a practical proposition, even if it
were possible. (A guide here as to what constitutes a practical
approach is to make a judgement about the feasibility of writing
the corresponding Prolog program.)
A typical case of (b) would be where different values for certain
features can occur only in combinations (e.g. allowable
combinations of resolution and colour depth on a given video
display). If the features are treated independently as suggested
by the framework above, all possible combinations would be allowed,
rather than the specifically allowable combinations.
4.1.1.1 Feature ranges
The first issue can be addressed by considering the type of value
which can represent the allowed features of a data file format.
The features of a specific data file are represented as values from
an enumeration (e.g. ua_media, papersize), or a numeric values
(integer or rational). The description of allowable file format
feature needs to represent all the allowable values.
The Prolog clauses used above to describe file format features
already allow for multiple enumerated values. Each acts as a
mathematical relation to select a subset of the set of file values
allowed by the preceding predicates.
Section 3 of this document describes proposed media feature value
types.
For numeric feature values, a sequence of two numbers to represent
a closed interval is suggested, where either value may be replaced
by an empty list to indicate no limiting value. Thus:
[m,n] => { x : m <= x <= n }
[m,[]] => { x : m <= x }
[[],n] => { x : x <= n }
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The following Prolog could be used to describe such range matching:
feature_match(X,[[],[]]).
feature_match(X,[L,[]]) :- L <= X.
feature_match(X,[[],H]) :- X <= H.
feature_match(X,[L,H]) :- L <= X, X <= H.
feature_match(X,X).
(This example strectches standard Prolog, which does not support
non-integer numbers. The final clause allows 'feature_match' to
deal with equality matching for the normal enumerated value case.)
4.1.1.2 Feature combinations
Representing allowed combinations of features is trickier. Two
possible approaches might be considered:
(a) use additional predicates to impose relationships between
features.
(b) allow meta-features which are groupings of other features.
(A) Additional predicates
If x- and y- resolutions were to be constrained to square or semi-
square aspect-ratios, the following predicates might be added to
the feature set description:
( feature_match(Rx,Ry) ;
feature_match(Rx,2*Ry) ;
feature_match(2*Rx,Ry) ),
feature_match(Rx,[72,600]),
feature_match(Ry,[72,600])
(where the last two constraints might be imposed by the 'res_x' and
'res_y' predicates).
Another example might be:
( ( feature_match(Px,640), feature_match(Py,480) ) ;
( feature_match(Px,600), feature_match(Py,800) ) ;
( feature_match(Px,1024), feature_match(Py,768) ) )
This is based on the predicates 'pix_x(File,Px)', 'pix_y(File,Py)',
'res_x(File,Rx)' and 'res_y(File,Ry)' from the initial framework
above.)
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(B) Meta-features as groupings of other features
Applying this to the above examples would replace:
pix_x(File,Px),
pix_y(File,Py),
res_x(File,Rx),
res_y(File,Ry),
with the meta-features 'pix' and 'res':
pix(File,[Px,Py]),
res(File,[Rx,Ry])
where:
pix(File,[640, 480]).
pix(File,[800, 600]).
pix(File,[1024,768]).
res(File,[Rx,Ry]) :-
feature_match(Rx,[72,600]),
feature_match(Ry,[72,600]),
( feature_match(Rx,Ry) ;
feature_match(Rx,2*Ry) ;
feature_match(2*Rx,Ry) ).
On closer examination, these two options turn out to be pretty much
the same thing: a requirement to impose additional constraint
predicates on a file feature set. They differ only in where the
predicates are applied.
This all suggests that file format capabilities can be described by
feature set predicates: arbitrary logical expressions using AND,
OR, NOT logical combining operators, and media feature value
matching.
4.1.2 Content, sender and recipient capabilities
It has already been suggested that these are represented as
predicates on the feature set of a particular data file.
Having also shown that these same predicates can represent
constraints on feature combinations, we proceed directly to a
proposal that everything is represented by predicates.
4.2 Conclusion and proposal
Data file features, original content features, sender features and
recipient features (and user features) can all be represented as
predicates.
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A key insight, which points to this conclusion, is that a
collection of feature values can be viewed as describing a specific
document rendered by a specific recipient. The capabilities that
we wish to describe, be they sender, file format, recipient or
other capabilities, are sets of such feature collections, with the
potential to ultimately render using any of the feature value
collections in the set.
This raises a terminology problem, because the term "feature set"
has been used to mean a collection of specific feature values and a
range of possible feature values. Thus the more restricted
definitions of "feature collection" and "feature set" which appear
in the terminology section of this document.
Original content, data files and recipients (and users) all embody
the potential capability to deal with a "feature set". One of the
aims of content negotiation is to select an available data file
format (availability being circumscribed by the original content
and sender capabilities) whose feature set intersection with the
recipient feature set is non-empty. (The further issue of
preference being deferred for later consideration.)
The concept of a mathematical relation as a subset defined by a
predicate can be used to define feature sets, using universal
quantification (i.e. using the predicate to select from some
notional universe of all possible feature collections).
Thus, a common framework of predicates can be used to represent the
feature capabilities of original content, data file formats,
recipients and any other participating entity which may impose
constraints on the usable feature sets.
Within this framework, it is sufficient to represent individual
feature values as enumerated values or numeric ranges. The thesis
in section 3 of his document, combined with a study of "Media
Features for Display, Print, and Fax" [6], indicate that more
complex media feature values can be handled by predicates.
5. Indicating preferences
5.1 Combining preferences
The general problem of describing and combining preferences among
feature sets is very much more complex than simply describing
allowable feature sets. For example, given two feature sets:
{A1,B1}
{A2,B2}
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where:
A1 is preferred over A2
B2 is preferred over B1
which of the feature sets is preferred? In the absence of
additional information or assumptions, there is no generally
satisfactory answer to this.
The proposed resolution of this issue is simply to assert that
preference information cannot be combined. Applied to the above
example, any preference information about A1 in relation to A2, or
B1 in relation to B2 is not presumed to convey any information
about preference of {A1,B1} in relation to {A2,B2}. (This approach
was selected as being the simplest among those considered, and
because there is no clear need for anything more).
In practical terms, this restricts the aplication of preference
information to top-level predicate clauses. A top-level clause
completely defines an allowable feature set; clauses combined by
logical-AND operators cannot be top-level clauses.
5.2 Representing preferences
A convenient way to represent preferences is by numeric "quality
values", as used in HTTP "Accept" headers, etc. (see RFC 2068 [9],
section 3.9]).
It has been suggested that numeric quality values, as used in some
HTTP negotiations, are misleading and are really just a way of
ranking options. Attempts to perform arithmetic on quality values
do seem to degenerate into meaningless juggling of numbers.
Numeric quality values in the range 0 to 1 (as defined by RFC 2068
[9], section 3.9) are used to rank feature sets according to
preference. Higher values are preferred over lower values, and
equal values are presumed to be equally preferred. Beyond this,
the actual number used has no significance, and should not be used
as a basis for any arithmetic operation.
In the absence of any explcitly applied quality value, a value of
"1" is assumed, suggesting an option which is equally or more
preferred than any other.
This approach can be represented in the Prolog-based framework of
an earlier example as follows:
match_format(File,Qvalue) :-
match_format(File),
Qvalue=1.
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match_format(File) :-
pix(File,[1024,768],
res(File,[Rx,Ry]).
match_format(File,Q) :-
pix(File,[800, 600]),
res(File,[Rx,Ry]),
Qvalue=0.9.
match_format(File,Q) :-
pix(File,[640, 480]).
res(File,[Rx,Ry]),
Qvalue=0.8.
res(File,[Rx,Ry]) :-
feature_match(Rx,[72,600]),
feature_match(Ry,[72,600]),
( feature_match(Rx,Ry) ;
feature_match(Rx,2*Ry) ;
feature_match(2*Rx,Ry) ).
This example applies image preference ranking based solely on the
size of the image, provided that the resolution constrains are
satisfied.
6. Feature set representation
The foregoing sections have desribed a framework and semantics for
defining feature sets with predicates applied to feature
collections. This section proposes some concrete representations
for these feature setpredicates.
Rather than invent an all-new notation, this proposal adapts a
notation already defined for directory access [7,8]. Observe that
a feature collection is similar to a directory entry, in that it
consists of a collection of named values. Further, the semantics
of the mechanism for selecting feature collections from a feature
set is in most respects identical to selection of directory entries
from a directory.
Differences between directory selection (per [7]) and feature set
selection described previously are:
. Directory selection provides substring-, approximate- and
extensible- matching for attribute values. Directory selection
may also be based on the presence of an attribute without regard
to its value.
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. Directory selection provides for matching rules which are
dependent upon the declared data type of an attribute value.
. Feature selection provides for the association of a quality value
with a top-level feature predicate as a way of ranking the
selected value collections.
The idea of substring matching does not seem to be relevant to
feature set selection, and is excluded from these proposals.
The idea of extensible matching and matching rules dependent upon
data types are facets of a problem not addressed by this memo, but
which do not necessarily affect the feature selection syntax. An
aspect which might have a bearing on the syntax would be a
requirement to specify a matching rule explicitly as part of a
selection expression.
Testing for the presence of a feature may be useful in some
circumstances, but does not sit comfortably within the semantic
framework. Feature sets are described by universal quantification
over predicates, and the absence of reference to a given feature
means the set is not constrained by that feature. Against this, it
is difficult to define what might be meant by "presence" of a
feature, so this option is not included in these proposals.
6.1 Text string representation
The text representation of a feature set is closely based on RFC
2254 "The String Representation of LDAP Search Filters" [8],
excluding those elements not relevant to feature set selection
(discussed above), and adding options to associate quality values
with top-level predicates.
The format of a feature predicate is defined by the production for
"filter" in the following, using the syntax notation of [10]:
filter = "(" filtercomp [ ";" "q=" qvalue ] )"
qvalue = ( "0" [ "." 0*3DIGIT ] )
/ ( "1" [ "." 0*3("0") ] )
filtercomp = and / or / not / item
and = "&" filterlist
or = "|" filterlist
not = "!" filter
filterlist = 1*filter
item = simple
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simple = attr filtertype value
filtertype = equal / greater / less
equal = "="
approx = "~="
greater = ">="
less = "<="
attr = <Feature tag, as defined in [3]>
value = <Feature value, per the named feature tag>
As described, the syntax permits a quality value to be attached to
any "filter" value in the predicate (not just top-level values).
But it should be noted that values which are enclosed by "not" or
"and" constructs are not visible to the enclosing context.
If a given feature collection is matched by more than one "filter"
in an "or" clause, the highest associated quality value is applied.
NOTE
The flexible approach to allowable quality values in this
syntax has been adopted for two reasons: (a) to make it
easy to combine separately constructed feature
predicates, and (b) to allow that the mechanism used for
quality values might, in future, be generalized to an
extensible tagging mechanism (for example, to incorporate
a conceivable requirement to explicitly specify a
matching rule).
6.2 ASN.1 representation
Should it be required, the LDAP search filter model provides the
basis for an ASN.1 representation of a feature predicate.
The following ASN.1 is adapted from RFC 2251 "Lightweight Directory
Access Protocol (v3)" [7] (also contained in RFC 2254 "The String
Representation of LDAP Search Filters" [8]) to mirror the
adaptation of the string representation presented above
[[The following ASN.1 fragment does not include provision for
quality value (and possibly other parameter values) to be attached
to a filter value. Also, if using an ASN.1-derived representation
it would seem more appropriate to use an ISO object identifier for
the feature tag, and an appropriate ASN.1 type for the feature
value. Such changes would remove any semblance of compatibility
with LDAP, but that may not matter.]]
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Filter ::= CHOICE {
and [0] SET OF Filter,
or [1] SET OF Filter,
not [2] Filter,
equalityMatch [3] AttributeValueAssertion,
greaterOrEqual [5] AttributeValueAssertion,
lessOrEqual [6] AttributeValueAssertion
}
AttributeValueAssertion ::= SEQUENCE {
featureTag OCTET STRING,
featureValue OCTET STRING
}
7. Security considerations
Some security considerations for content negotiation are raised in
[1,2,3].
The following are primary security concerns for capability
identification mechanisms:
. Unintentional disclosure of private information through the
announcement of capabilities or user preferences.
. Disruption to system operation caused by accidental or malicious
provision of incorrect capability information.
. Use of a capability identification mechanism might be used to
probe a network (e.g. by identifying specific hosts used, and
exploiting their known weaknesses).
The most contentious security concerns are raised by mechanisms
which automatically send capability identification data in response
to a query from some unknown system. Use of directory services
(based on LDAP [7], etc.) seem to be less problematic because
proper authentication mechanisms are available.
Mechanisms which provide capability information when sending a
message are less contentious, presumably because some intention can
be inferred that person whose details are disclosed wishes to
communicate with the recipient of those details. This does not,
however, solve problems of spoofed supply of incorrect capability
information.
The use of format converting gateways may prove problematic because
such systems would tend to defeat any message integrity and
authenticity checking mechanisms that are employed.
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8. Copyright
Copyright (C) The Internet Society 1998. All Rights Reserved.
This document and translations of it may be copied and furnished to
others, and derivative works that comment on or otherwise explain
it or assist in its implementation may be prepared, copied,
published and distributed, in whole or in part, without restriction
of any kind, provided that the above copyright notice and this
paragraph are included on all such copies and derivative works.
However, this document itself may not be modified in any way, such
as by removing the copyright notice or references to the Internet
Society or other Internet organizations, except as needed for the
purpose of developing Internet standards in which case the
procedures for copyrights defined in the Internet Standards process
must be followed, or as required to translate it into languages
other than English.
The limited permissions granted above are perpetual and will not be
revoked by the Internet Society or its successors or assigns.
This document and the information contained herein is provided on
an "AS IS" basis and THE INTERNET SOCIETY AND THE INTERNET
ENGINEERING TASK FORCE DISCLAIMS ALL WARRANTIES, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF
THE INFORMATION HEREIN WILL NOT INFRINGE ANY RIGHTS OR ANY IMPLIED
WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
9. Acknowledgements
My thanks to Larry Masinter for demonstrating to me the breadth of
the media feature issue, and encouraging me to air my early ideas.
Early discussions of ideas on the IETF-HTTP and IETF-FAX discussion
lists led to useful inputs also from Koen Holtman, Ted Hardie and
Dan Wing.
The debate later moved to the IETF conneg WG mailing list, where Al
Gilman was particularly helpful in helping me to refine the feature
set algebra. Several ideas for indicating preferences were
suggested by Larry Masinter.
10. References
[1] "Scenarios for the Delivery of Negotiated Content"
T. Hardie, NASA Network Information Center
Internet draft: <draft-ietf-http-negotiate-scenario-02.txt>
Work in progress, November 1997.
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[2] "Requirements for protocol-independent content negotiation"
G. Klyne, Integralis Ltd.
Internet draft: <draft-ietf-conneg-requirements-00.txt>
Work in progress, March 1998.
[3] "Content feature tag registration procedures"
Koen Holtman, TUE
Andrew Mutz, Hewlett-Packard
Ted Hardie, NASA
Internet draft: <draft-ietf-http-feature-reg-03.txt>
Work in progress, November 1997.
[4] "Notes on data structuring"
C. A. R. Hoare,
in "Structured Programming"
Academic Press, APIC Studies in Data Processing No. 8
ISBN 0-12-200550-3 / 0-12-200556-2
1972.
[5] "Programming in Prolog" (2nd edition)
W. F. Clocksin and C. S. Mellish,
Springer Verlag
ISBN 3-540-15011-0 / 0-387-15011-0
1984.
[6] "Media Features for Display, Print, and Fax"
Larry Masinter, Xerox PARC
Koen Holtman, TUE
Andrew Mutz, Hewlett-Packard
Dan Wing, Cisco Systems
Internet draft: <draft-masinter-media-features-02.txt>
Work in progress, January 1998.
[7] RFC 2251, "Lightweight Directory Access Protocol (v3)"
M. Wahl, Critical Angle Inc.
T. Howes, Netscape Communications Corp.
S. Kille, Isode Limited
December 1997.
[8] RFC 2254, "The String Representation of LDAP Search Filters"
T. Howes, Netscape Communications Corp.
December 1997.
[9] RFC 2068, "Hyptertext Transfer Protocol -- HTTP/1.1"
R. Fielding, UC Irvine
J. Gettys,
J. Mogul, DEC
H. Frytyk,
T. Berners-Lee, MIT/LCS
January 1997.
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[10] RFC 2234, "Augmented BNF for Syntax Specifications: ABNF"
D. Crocker (editor), Internet Mail Consortium
P. Overell, Demon Internet Ltd.
November 1997.
11. Author's address
Graham Klyne
Content Technologies Ltd
Forum 1
Station Road
Theale
Reading, RG7 4RA
United Kingdom
Telephone: +44 118 930 1300
Facsimile: +44 118 930 1301
E-mail: GK@ACM.ORG
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