A Common Information Structure for Broadcast Applications

Metadata

Publisher: SMPTE — White Plains, NY, USA
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE J
Volume: 106, No. 10, pp. 682–697
Abstract: This paper defines an integrated data structure supporting the management of events, material, media, and playback resources. The structure supports any number of levels of abstraction for dealing with the complexities of modern broadcast, editing, and newsroom applications. The structure separates the compositional aspects of broadcast operations (the pieces that make up a program or story) from the representational aspects (the material that manifests the specified compositional pieces). As such, it differs significantly from other industry attempts to provide a unified data structure' that does not separate the programmatic intent from the actual material that implements that intent. The data structure is a simple, two-level information structure where the levels are similar to each other, creating a recursive structure for operating within the commonly accepted abstractions of the industry such as rundown, event, story, program, pod, schedule, and channel as well as new abstractions that meet the needs of an increasingly complex industry. Sequencing constraints may be applied to elements in a time-independent manner. In addition, this two-level recursive structure is independently applied to both the compositional and representational aspects, supporting a rich expressiveness for planning, operating, and reporting the actions of a complete broadcast operation.
Publication Date: 1997-10-01
DOI: 10.5594/J04506
ISSN: Print: 0036-1682
Link: https://doi.org/10.5594/J04506
Author(s): Jerry BoetjeIsochron Corp., San Ramon, CA 94583;
David CollierSony Electronics, Inc., San Jose, CA 95134.
Copyright: © 1997 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

1. For example, the Open Media Framework Interchange (OMFI) created by AVID Technologies . EXTERNAL
10. A polynomial time algorithm executes in a time that is proportional to the square, cube, or other such exponent of the number of intervals. Such algorithms execute in seconds instead of the days required by an exponential algorithm. EXTERNAL
11. This is called the transitive closure over the set of relationships. EXTERNAL
12. Ladkin P. Maddux R. , “On Binary Constraint Problems,” J. of the Assoc. for Computing Machinery , 41 : 435 – 469 , 1994 . EXTERNAL
13. This operation produces the transitive closure of the constraint matrix. In other words, it will deduce all of the possible interval relationships that exist among the set of intervals. The closure is mathematically complete. EXTERNAL
14. In practice, the algorithm terminates after three to four passes. EXTERNAL
15. Note that the matrix squaring operation must terminate on any pass where a False value is detected. Otherwise, the entire matrix will end up in a contradiction (all entries False) and the most specific contradiction will be lost. EXTERNAL
16. Macworth A. , “Consistency in Networks of Relations,” Artificial Intelligence , 8 : 99 – 118 , 1977 . EXTERNAL
17. Bessière C. , “A Simple Way to Improve Path Consistency Processing in Interval Algebra Networks,” preprint from Proc. AAAI'96, 1996 . EXTERNAL
18. Remember that the convention is that the end of a clip is really at EOM + 1. The notation in this example is sloppy, since the real value pair should be EOM + 1, SOM. EXTERNAL
19. Such bifurcations are indicated when the endpoint relation is unknown (“?”) in the literature. An unknown relation between a pair of endpoints means that the algorithm cannot determine whether one comes before the other or the two are simultaneous. EXTERNAL
2. Allen J. , “Maintaining Knowledge about Temporal Intervals.” Communications of the ACM , 26 : 832 – 843 , 1983 . EXTERNAL
3. Allen J. , “Temporal Reasoning and Planning,” in Reasoning about Plans , Allen J. F. Kautz H. A. Pelavin R. N. Tenenberg J. D. , eds. Morgan-Kaufmann , San Mateo, Calif. , 1991 , pp. 1 – 67 . EXTERNAL
4. van Beek P. , “Reasoning about Qualitative Temporal Information,” Artificial Intelligence , 58 : 297 – 326 , 1992 . EXTERNAL
5. Vilan M. Kautz H. , “Constraint Propagation Algorithms for Temporal Reasoning,” Proc. AAAI'86 , pp. 377 – 382 , 1986 . EXTERNAL
6. In broadcast terms, the start and end of a clip or event. EXTERNAL
7. Technically, the problem is NP-hard, which means that computing the solution takes time exponential in the number of intervals. It is easily possible to produce a set of relationships that would take days or weeks to solve. EXTERNAL
8. Maximal means as big as possible. It does not, however, imply unique. However, this subset is both maximal and unique. EXTERNAL
9. Nebel B. Bürckert H. , “Reasoning about Temporal Relations: A Maximal Tractible Subclass of Allen's Interval Algebra,” J. of the Assoc. for Computing Machinery , 42 : 43 – 66 , 1995 . EXTERNAL

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Jerry Boetje and David Collier; A Common Information Structure for Broadcast Applications, SMPTE Journal ( Volume: 106, Issue: 10, October 1997); SMPTE, 1997. Available at https://doi.org/10.5594/J04506

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Jerry Boetje and David Collier; A Common Information Structure for Broadcast Applications, SMPTE Journal ( Volume: 106, Issue: 10, October 1997); SMPTE, 1997
doi: 10.5594/J04506
url: https://doi.org/10.5594/J04506

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<li>
Jerry Boetje and David Collier; <cite id="bib-10-5594-j04506">A Common Information Structure for Broadcast Applications</cite>, SMPTE Journal ( Volume: 106, Issue: 10, October 1997); SMPTE, 1997
<span class="doi">10.5594/J04506</span>
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