Internet Si Bazele de Date Multimedia

8/13/2019 Internet Si Bazele de Date Multimedia

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2003 WWW-Lib-MM 1

Database Systems andWWW Applications

Digital Libraries

Multimedia Database Systems

2003 WWW-Lib-MM 2

Contents

• Database Systems and WWW Applications

– Internet DB Architecture

– Internet Applications

• Digital Libraries

• Multimedia Database Systems


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Internet Application Architecture: Today

Application messages

Browser Browser

Physical Middle Tier

Data Sources

Client Tier

ORDBMS

WEB/APP Server

Middle-Tier

Application

Data Integration,

Storage, Query,

Management

Other Data

Sources

Gateways

OLE/DB

Data source

authoring

tools, etc.

HTTPHTTP

Remote messages

Nori, A., Databases in Internet Applications: Case Studies,

in: Postmodern DBS, UC Berkeley, Spring 1999

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Internet Applications• Entertainment

– Games, Music, Films, Multi-person chat

• Public information

– Maps, Tax return helper

• Advertisement

– Interactive catalogues for products and services

• Medicine

– Diagnosis, Consultation, Remote surgery

• Education

– Learning-on-demand (for a degree),virtual museums, tour remote spaces

• Engineering

– Collaborative design, remote parallel simulation services

• Publishing

– Submit, Review, Proof-editing (text and graphics)

• Tele-communication

– Conferencing

• ...


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2003 WWW-Lib-MM 5

Contents



– Definitions

– Underlying concepts

– Digital Libraries Initiative

– Digital Libraries (examples)


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Definitions

In the Stanford Digital Library project, we view long-term digitallibrary systems as collections of widely distributed, autonomouslymaintained services. Of course, a digital library system must includeservices that allow users to search over collections of informationobjects. Examples of searchable collections include traditionallibrary collections, digital images, e-mail archives, video, on-linebooks, and scientific article citation catalogs (containing onlymetadata about the articles, not the articles themselves).

While searching services are valuable, they are not the only kind ofservice in the digital library of the future. Remotely usableinformation processing facilities are also important digital library

services. These services provide support for activities such asdocument summarization, indexing, collaborative annotation, formatconversion, bibliography maintenance, and copyright clearance.

The Stanford Digital Library Technologies Project


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Definitions

Digital libraries are organizations that provide theresources, including the specialized staff, to select,

structure, offer intellectual access to, interpret,

distribute, preserve the integrity of, and ensure the

persistence over time of collections of digital works so

that they are readily and economically available for use

by a defined community or set of communities.

The Digital Library Federation (DLF)

Note:CS researchers tend to focus on digital libraries as content

collected on behalf of user communities, while librarians focus on

digital libraries as institutions or services.

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Contents



– Definitions






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Notions

• Content – the items in the library collection

• Annotation – information added to (associated with) an item

• Subject matter – focus of a collection, topics used in classification

• Catalog – database (card file) of bibliographic records

• Classification – assigning call number, adding keywords

• Rights to use - permissions

• License agreements – contractual right to use

• Copyright

• Watermark – a subliminal pixal pattern to identify a digital work

• Copy detection – verifying copying, searching for copies

• Search (40% of search queries on the web are reported to be single words)

• Metasearchers (services that provide unified query interfaces to multiple searchengines. Thus users have the illusion of a single combined document source. Three maintasks: choosing the best sources to evaluate a query; evaluating the query at these sources;merging the query results from these sources.)

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File Formats

– Image/graphics formats

• TIFF Tagged Image File Format

• GIF Graphics Image File Format

• JFIF JPEG File Format

• SPIFF Still Picture Interchange File Format

• PICT Macintosh Picture

• TGA TrueVision Targa file (bit mapped images)

• EPS Encapsulated PostScript

• CGM Computer Graphics Metafile

• PhotoCD (Kodak)

– Picture and video formats

• JPEG Joint Photographic Expert Group

• Motion JPEG

• MPEG Moving Pictures Expert Group

– Document formats

• PostScript (Adobe)

• PDF Portable Document Format (Adobe)


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Compression

• Compression

– lossless

• color 25%-50%-67%

• B/W 50%-90%;

– lossy up to 95%

• Compression formats

– CCITT Group III or Group IV

– JPEG

– JBIG An international compression standard

– LZW.Subsampling (lossy)

• Compression schemes

– LZW Lempel-Ziv-Welch (lossless)

– MPEG Group of Pictures: IBBBPBBBPB…I

– QuickTime (Apple)

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Images in the Digital Library

Most image-database systems store descriptive information about theimages in a traditional text-based information retrieval system. Anadditional field containing the filename of the image is added to eachrecord in this text-based system to link it to an image file. Images areselected by querying the text-based system. When the query is specificenough, the user requests a selected image (or a set of images) to view.Extensions to user-interface software look up the filename field(s) in thetext record(s) and display the image(s), often in a new window. Eachsystem handles the text/image relationship in its own way, and standardsneed to be developed to enable the interchange of image files amongsystems

Much research remains in the field of image databases, particularly withrespect to image-quality needs. Further studies need to stratify types of

collections, as well as users and uses of those collections, relating each toa series of required image qualities.

Howard Besser and Jennifer Trant

Introduction to Imaging: Issues in Constructing an Image Database

http://www.getty.edu/research/institute/standards/introimages/


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Metadata and Cataloging

• Metadata Data about data (structure and access)• OPAC On-line Public Access Catalog

• Content description structured vocabularies

data-structure guidelines

• MARC MAchine Readable Cataloging

• Dublin Core a classification scheme

• Indexing abbreviation for works

organized for reference

• MPEG7 metadata about MPEG data

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Information Retrieval - Text

• Basic searching techniques:

– Linear search (can do regular expressions)

– Inverted files

– Hash tables

– Signature files (compressed linear search)

• Linear search requires no extra space, linear complexity in size, no

preprocessing

• Inverted files cannot search for arbitrary expressions, (usually) must

start at the beginnings of words, building index takes n log n time

length of file (n words). Index overhead ranges from 25% to 200%.

• Hash coding is sensitive to the exact spelling of the word, and tends to

scatter words nearly spelled the same; requires preprocessing and has

slight storage overhead.


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Information Retrieval - Images

• Indexes that were made for other purposes

– Citations

– Reviews

– …

• Thumbnails

• Exploit layout formats (e.g., newspaper columns)

• Image alignment

– Centering

– Feature analysis, normalize rotation and X-Y orientation)

• Complementation: an image of a red rose will not normally havethe keyword "red". Thus image features and associated words

can complement and even disambiguate each other.

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Contents



– Definitions






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US Digital Libraries Initiative (Phase I)

• University of California, Berkeley

– Work-centered digital information services

• University of California, Santa Barbara

– Spatially referenced map information

• Carnegie Mellon University

– Full-content search and retrieval of video

• University of Illinois at Urbana-Champaign

– Federating repositories of scientific literature

• University of Michigan

– Intelligent agents for information location

• Stanford University

– Interoperation mechanisms among heterogeneous services

Shared vision: an entire Net of distributed repositories, where objects

of any type can be searched within and across indexed collections

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US Digital Libraries Initiative (Phase I)

• University of California, Berkeley

– http://elib.cs.berkeley.edu/

– also see http://sunsite.berkeley.edu/

• University of California, Santa Barbara

– http://www.alexandria.ucsb.edu/


– http://www.informedia.cs.cmu.edu/

• University of Illinois at Urbana-Champaign

– http://dli.grainger.uiuc.edu/idli/idli.htm

• University of Michigan – http://www.si.umich.edu/UMDL/


– http://www-diglib.stanford.edu/


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Examples of Technology Impact

• University of California, Berkeley – Multivalent Documents

– Robust Hyperlinks and Robust Locations

– TilePics

– …


– Informedia Digital Video Library System

– …


– Archival Digital Libraries Repositories

– Large Scale Copy Detection

– Google Search Engine

– …

• …

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Multivalent Documents

• Multivalent Annotations

– Stored separately from the document they annotate

– Appear in situ – as if part of the content of the document

• Hyperlinks

• Highlights

• Notes

• Copy editor markup (executable)

– Three classes of behavior

• Spans (anchored to points or intervals) – E.g., Hyperlinks, Rollovers, Highlights

• Lenses (anchored to geometric regions)

– E.g., Bit Magnify, Optical Character Recognition• Structures (within the document tree)

– E.g., Book w/chapters and sections

– Combining Annotations

• Notemarks – E.g., outlining, man pages


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Robust Hyperlinks and Robust Locations

• URLs can be made robust – if a web page moves to another location anywhere on the

web, you can find it.

• Even if that page has been edited.

• Robust Hyperlinks

– URLs are augmented with a five or so word content-based

lexical signature to make a robust hyperlink

– If the URL's address-based portion breaks:

Feed the signature into any web search engine

to find the new site of the page.

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TilePics

• A file format designed to store tiled data of arbitrary type in a

hierarchical, indexed format in order to provide fast retrieval.

• a fixed sized header

• tile index data

• an optional gap

• contiguous tile data

• optional attribute data

• Encapsulate a large amount of related, static data in a single file.

• A one or two-dimensional dataset

• At multiple scales of resolution or abstraction.

• Tileable, based on x,y coordinates for quick localized access• Store data at multiple levels of resolution

• in multiple layers of tiles

• each layer relates to the next by the same scale factor

• Zoom by drawing just the relevant tiles at the next layer down


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Informedia Digital Video Library System

• IDVLS attempts to automate cataloging by: – Recognizing speech

– Understanding text and language

– Segmenting text

– Recognizing text within imagery

– Segmenting video

– Analysing video structure

– Image matching based on perceived color

– Region matching for content-based image retrieval

– Detecting video shot boundaries

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Archival Digital Libraries Respositories

File System

Info

Monitor

usersusers

Archival Repository Web Server


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Large Scale Copy Detection

• CDS: Copy Detection System – content publishers register their valuable digital content in CDS

– CDS crawls the web

• compares the web content to the registered content

– notifies the content owners of illegal copies.

• Key challenges

– accuracy , in terms of high precision and recall,

– scalability , in terms of coping with several terabytes of data (or

several tens of millions of web pages)

– resiliency to “attacks”

• Two prototypes – SCAM (Stanford Copy Analysis Mechanism, for text)

– FRAUD (Finding Replicas of AUDio)

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Google Search Engine

• PageRank: A Citation Importance Ranking

– Number of backlinks (~ citations)

• Approximation of importance

• Citation analysis literature

– Citation indexes

• Extreme variation in importance

– Large database of links: propagation

• Idealized Model

B

C

A

1 2

B and C are

backlinks of A

l1,2 = 1

l2,1 = 0

N number of outgoing links

ni = li,j on page i (includes multiple

j=1, i≠

j links to the same page)

N WiW j = (li,j — ) PageRank of page j

i=1, i≠ j ni

Σ

Σ


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Papers on the Creation of Google

• The Anatomy of a Large-Scale Hypertextual Web SearchEngine, by Sergey Brin, Lawrence Page

• Dynamic Data Mining: Exploring Large Rule Spaces bySampling , by Sergey Brin, Lawrence Page

• Computing Iceberg Queries Efficiently , by Min Fang,Narayanan Shivakumar, Hector Garcia-Molina, Rajeev Motwani,and Jeffrey D. Ullman

• The PageRank Citation Ranking: Bringing Order to the Web,by Lawrence Page, Sergey Brin, Rajeev Motwani, andTerry Winograd

• Extracting Patterns and Relations from the World Wide Web,by Sergey Brin

• Finding near-replicas of documents on the web,

by Narayanan Shivakumar, Hector Garcia-Molina• Efficient Crawling Through URL Ordering , by Junghoo Cho,

Hector Garcia-Molina, Lawrence Page

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Contents



– Definitions






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ACM Portal: ACM Digital Library

Bibliographic information, abstracts, reviews, and the full-text forarticles published in ACM periodicals and proceedings since its

founding in 1947 are available in the library together with selected

works published by affiliated organizations.

As of October 15, 2002, the Digital Library contains:

– over 102,500 full-text articles from journals, magazines, and

conference proceedings.

– Tables of Contents with over 33,000 citations from articles

published in journals and magazines from 1954 forward.

– Tables of contents with more than 69,000 citations from articles

published in over 1100 volumes of conference proceedings

since 1970.

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ACM Digital Library

• The Digital Library presents all material associated with an article:

– Bibliographic data

includes the title, author(s), publication, volume, issue, and pagenumbers of an article.

– Index terms

compiled using article keywords and the ACM ComputingClassification System.

– Abstracts available for most articles in the Digital Library.

– Reviews from ACM Computing Review s (Selected articles)

– Full-text view or download complete articles.

Most articles are available in PDF -- some are available in otherformats including HTML, postscript, and LaTeX.

– DOI

When ACM submits a reference query and it is matched, aUniversal Resource Name (URN) in the form of a Digital ObjectIdentifier (DOI) is returned and inserted as an external link from

ACM's site to the source for the material.


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University of Oslo Digital Library Project

• “Post graduate theses in the digital library” (Hovedfagsoppgaveri digitalt bibliotek) is a pilot project where theses will be

published in full text on the world wide web.

• A step in establishing a digital library where the University of

Oslo shall keep electronic teaching materials and documents.

• A joint project between the USIT SGML group, the University of

Oslo library, and other institutions, including the Institute of

Informatics

• Students are to use Microsoft Word and a template file provided

by the project.

• Microsoft Word documents using the template styles can by

automatically converted to HTML and SGML.

• http://www.digbib.uio.no/ (in norwegian)

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Contents




– Definitions

– Example Application

• MM QoS Requirements

– MMDBMS Requirements

– MMDBMS Concepts


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Definitions

• Multimedia (MM); loosely: any system that can be used to presentinformation in more than one form: text, graphics, still images,

animation, sound, video, special computer-generated effects.

The system should have user-friendly interactive interfaces that help

the communication of complexly structured data.

• MMDBSs: are the DBSs that manage MM data, facilitate MM for

presentations, and use specific tools for the storage, management,

and retrieval of MM data.

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Multimedia Applications

• Entertainment

• Public information

• Advertisement

• Education

• Medicine

• Engineering

• Publishing

• Tele-communication

• ...


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Data Flow for a Multimedia Network Server

Network

Storage

Multimedia server

Buffers Buffers

Graphics/videohardware

Audiohardware

Client

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Contents




– Definitions




– MMDBMS Concepts


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Multimedia-Supported Learning of

Practical Medical Procedures

• Provide realistic visualization of required practical skills

• Proven to be pedagogically beneficial to view the multimedia lesson ona procedure in a “learning on demand” setting before observing it in theclinic

• Lessons involve realistic multimedia elements (video and audio)recorded in Oslo hospitals, with expert commentary,

• Over 17,000 multimedia elements in OKSE-basen database.

• Mostly on CD-ROM.

• LoD over the Internet would enable

– Greater flexibility (time and location) for students

– Other applications

• Paramedics review skills on demand in emergency situations

• Doctors take courses in their office for lifelong learning

– Incremental release and revision of lessons or skill segments

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Selective Multimedia Quality is Critical

• Quality of Service (QoS):

– The collective effects of service performance which determinethe degree of satisfaction of a user of the service.

– Performance, not operation (non-functional requirements,independent of functional requirements)

• Video accuracy, for example, when draining the chest.

– The video must accurately show location of arteries, ribs,where the drain can safely be inserted to avoid arteries.

• Audio fidelity, for example, when breathing is difficulty.

– The audio must be clear enough to differentiate between stridor,an obstruction of the large airways, and asthmatic breath sounds.

• Timing accuracy. – Some procedures should be viewed in near real time, possibly atreduced video resolution and reduced audio fidelity.

• The critical quality focus may shift within a lesson.

– The infrastructure should shift resources to the critical qualities(and ignore others if necessary).


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Contents




– Definitions




– MMDBMS Concepts

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Requirements for MMDBSs

• represent arbitrary data types and specification of programs that

interact with arbitrary data sources;

• query and modify (update, insert, delete) MM data; including

retrieval of MM data via associative search within MM data

(minimally, text);

• specify and execute abstract operations on MM data, e.g., play,

fast forward, pause, and rewind one-dimensional data like audio

or text; to display, expand, and compress two-dimensional data

like bit-mapped images;

• deal with heterogeneous data sources in a uniform manner; this

includes access to data in these sources and migration of data

from one data source to another.

Ability to ...


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Requirements - 2

• MM & object-oriented data modeling concepts;• management of several kinds of magnetic and optical storage

devices appropriate for MM data handling;

• uniform management of very large data volumes =>

management of tertiary storage and multi-level storage

hierarchies;

• support for realtime data processing =>

appropriate scheduling and resource allocation techniques;

• support for storage and processing parallelism (performance

requirements);

• support for distribution => appropriate distributed DBMS

concepts.

MM data storage and retrieval:

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Storage space requirements for uncompressed

digital multimedia data (examples)

Media type Specifications Data rate per sec.

Voice-quali ty audio 1 channel, 8-bitsamples at 8 kHz

64 Kbits

MPEG-encoded audio Equiv. to CD quali ty 384 Kbits

CD-quality audio 2 channels, 16-bitsamples at 44.1 kHz

1.4 Mbits

MPEG2-encoded video 640x480 pixels/frame,24 bits/pixel

0.42 Mbytes

NTSC-quality video 640x480 pixels/frame,

24 bits/pixel

27 Mbytes

HDTV-quality video 1280x720 pixels/frame,24 bits/pixel

81 Mbytes


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Requirements - 3

• “soft” realtime transfer requirements• “hard” transaction deadlines

• synchronization between different data streams (data types)

• user interactions (synchronous and asynchronous)

Realtime and synchronization issues:

=> dependent on data distribution, storage devices,

compression techniques for the various data types,

buffer management techniques, scheduling algorithms,

data placement techniques, and communication bandwidth

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Contents




– Definitions




– MMDBMS Concepts


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DBMS Concepts

• Data modeling: temporal object-oriented modeling andpresenting (HCI) of multimedia data

+ extra data types & operations

• Query processing and optimization: browsing, content

addressing

• Storage management: optimization techniques

• Transaction management: realtime processing for read

transactions (presentations),

write transactions (authoring) use a advanced transaction model

(e.g., checkout-checkin with versioned data)

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User Interface Design for MM Applications

• User interaction and user interfaces become much more

complex if MM data is involved.

• State-of-the-art: buttons, text entry, scrollable areas, ...

-> does not support interaction with continuous media

• New devices (e.g., cameras, microphones, loudspeakers, ...)

have to be taken into account in addition to keyboard, mouse,

monitor, and external devices (e.g., VCRs, ...) for input and

output handling:

- simultaneous control of different devices

- efficient handling of user interrupts

- standardized interaction paradigms

- support for pen + voice input

- ...


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Object-Oriented Data Modeling + ...

• text• graphic

• image

• audio

• speech

• video

• generated media

Data types and operations for:

Temporal relationships:

- Synchronization and realtime processing

Quality-of-Service:- to handle average delay, speed ratio, utilization,

jitter, skew, and reliability.

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Required Data Model Concepts

and Related Work• Time independent data types

• Time dependent data types (continuous types)

• Temporal concepts: valid, transaction, and play time

• Temporal data models: TIGUKAT, T_Chimera, Mediadoc,

SGML/HyTime, ...

• Multimedia data models: AMOS, SGML/HyTime, LMDM, ...


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Learning-on-Demand (Asynchronous IDL)

- store lectures in a DBMS

- make lectures available

for students

- students should be able to retrieve

data from campus and from home

- flexible query facilities

- quality of service support

- scalable and synchronized playback

ObjectStore

Query proc. & opt.

TOOMM

Network

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Concepts of TOOMM

Logical Data Model

Video 1

Video 2

Audio 1

Multimedia Objects

CPO1

Atomic Presentation Objects

Presentation Model

P_Video 13

P_Video 14

P_Video 15

P_Audio 11

Composite

PresentationObject


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Example: Modeling a Video Object

Frame 0

Timestamp 0

Frame 1

Timestamp 1

Frame n

Timestamp n

TA 0

TA 1

TA n

Video 1

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Type Hierarchy

MMDT

PTI_MMDT

PictureText Graphics

CGM

Video

PTD_MMDT

Stream

Audio Music Animation

LDU

Anim.

Component

Event

NoteSampleFrame


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Play Time

Play Time

Components of a stream multimedia object

TA 0

TS 0 LDU 0

TA 1

TS 1 LDU 1

TA n

TS n LDU n

Components of a CGM multimedia object

TA 0

event 0 TS 0

TA 1

event 1 TS 1

TA n

event n TS n

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EER Diagram

P_MMDT

P_Text P_Picture P_Graphics

P_PTI_MMDT

P_Music P_Anim.

P_CGM

P_Audio P_Video

P_Stream

P_PTD_MMDT

Temporal_reference

start stop1:1

1:1

1:11:11:10:1

MMDT1:1 0:M

Effect1:1 0:M

CPO

Parallel

Temporal_Relationships

Serial

0:M1:1

1:1

1:M

1

2


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Example: Using Temporal References

P_Video 1

P_Video 2

P_Text 3

Composite multimediapresentation

Recursive temporal

reference list

Reference: True

deviation: 0

time_point: NA

Reference: True

deviation: -5

time_point: NA

Reference: Truedeviation: NA

time_point: 15

1

2

3

Multimedia

presentation

objects

time Actual play time value

Temporal references

2003 WWW-Lib-MM 56

Type: CPOName:Lecture_19_2_1998MTU_duration:1/44100

Type: P_AudioName: P_Audio 1Speed: 1Start: 0Stop: 31752000p_start.get_time_point()=0p_stop.get_time_point() =31752000

Type: AudioName: PMC_Lecture_hour1_clip_1LDU_duration: 1/44100Duration: 31752000Content description:- (0, 4988, “Lecturer talks about files”)- (4989, 12134, “Lecturer talks about

directories”)

Type: P_VideoName: P_Video 1Speed: 1

Start: 0Stop: 18000p_start.get_time_point()=0p_stop.get_time_point() =31752000

Type: VideoName: PMC_Lecture_hour1_scene1LDU_duration: 1/25

Duration: 1800Content description:- (0, 4988, “Lecturer talks about files”)- (4989, 12134, “Lecturer talks about

directories”)

Type: ParallelName: TR 1Temporal relationshiptype: EqualSkew tolerance: 80 ms

Type: P_HTMLName: P_HTML 1p_start.get_time_point() =3987233p_stop.get_time_point() =7234443

Type: P_HTMLName: P_HTML 2

p_start.get_time_point() =10234234p_stop.get_time_point() =16230933

Type: P_Light_PenName: P_Light_Pen 1p_start.get_time_point() =4457111p_stop.get_time_point() =6283324

Type: HTMLName: File System

Type: HTMLName: Directory Example

Type: Light_PenName: Drawing_objectsLDU_duration: 200Content description:- (0, 100, “Draw a bow in File System”)- (101, 200, “Draw a dot”)

Example

CPO


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Query Processing and Optimization

- Browsing:efficient location of data elements in very large amounts of data,exact-match (pattern-matching) queries (e.g., text)and similarity-based queries (e.g., images, ...)-> query refinement-> set-oriented and navigation-oriented browsing techniques

- Content addressing:efficient location of data with complex data types like images(difficult to access in realtime using pattern-recognition techniques)comprises: natural language understanding,speech processing,vision,

and user modeling

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Meta-Data Management

• Meta-data needed especially for continuous data to support

retrieval

• Textual data describing contents of audio and video segments

• Content search mostly performed on meta-data

• Problems:

– Modeling of meta-data

– Meta-data acquisition

– Association of meta-data to “real” data


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Storage Management Issues

• addressing techniques

• access paths

• data placement techniques:

clustering, partitioning, allocation

• system buffer management:

paging, ...

• disk scheduling:

sweeping, deadline-driven, …

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Data Placement

• Clustering and partitioning:

– data striping and data interleaving

• Allocation:

– contiguous placement

– constrained placement

– log-structured placement

Controller

Sector 0Sector 1Sector 2



Logical sector 0


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Disk Scheduling

• Traditional algorithms – FIFO (first come, first served)

– SSTF (shortest seek time first)

– SCAN (elevator algorithm)

1.Generation MM algorithms

– EDF (earliest deadline first)

– SCAN-EDF

– GSS (grouped sweeping scheme)

2.Generation MM algorithms

– two-phase algorithms

- reduce seek time

- reduce rotational latency

- increase throughput

- fair stream access

- real-time constraints?

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Transaction and Workflow Management• distributed transaction management mechanisms

• realtime transaction management mechanisms

• various new transaction and workflow management mechanisms


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Parallelism and Other Optimization

Techniques in MMDBSs

• parallelism on storage level, e.g., disk arrays -> striping, ...

• parallelism on processing level, e.g., multiprocessor machines, ...

• storage structures / data placement techniques

• query optimization

• transaction management mechanisms

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MMDBS: Conclusions

• investigated functionality needed to support MM applications

• illustrated how object-oriented and other modern DBMS

technologies can be applied to realize MMDBMS

• alternative “levels” of application support by DBMS

• open issues:

- effective storage models

- MM query languages and processing techniques

(handling of imprecise queries)

- ...

• Role of (MM)DBS in distributed MM systems


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2003 WWW-Lib-MM 65

Conclusions - State-of-the-Art

• Multimedia file systems and multimedia storage servers forspecial multimedia applications exist today

• Implement the presented concepts

• Acceptable performance

• Multimedia database systems are still under development,

certain aspects are solved

• Retrieval problems not yet solved in a satisfying manner