1 / 45

BIT 3193 MULTIMEDIA DATABASE

BIT 3193 MULTIMEDIA DATABASE. CHAPTER 4 : QUERING MULTIMEDIA DATABASES. What is QUERY ??. Language expression that describes the data to be retrieved from database. Query. omponents. Data item : as output. Information base : search is to be made.

winter
Download Presentation

BIT 3193 MULTIMEDIA DATABASE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BIT 3193 MULTIMEDIA DATABASE CHAPTER 4 : QUERING MULTIMEDIA DATABASES

  2. What is QUERY ??.. Language expression that describes the data to be retrieved from database.

  3. Query omponents Data item : as output Information base : search is to be made Condition : have to be satisfied for a data item to be selected

  4. MMDBMS perations Manipulation (editing and modifying data) Presentation Analysis (indexing and searching)

  5. Query Processing

  6. Query Processing • Multimedia queries can be of different types (deal with different media and with different properties) • The process of querying multimedia data is complex and it can approach this in TWO ways: • WHAT INFORMATION CAN BE RETRIEVED • HOW THE INFORMATION CAN BE RETRIEVED

  7. 1st Approach : WHAT ?? • There are THREE levels of complexity associated with the “WHAT”: • Level 1 : • Retrieval of primitive features such as color, shape, texture, spatial location and object movement. • Query example: • “ find clips of objects flying from top-right to bottom-left of screen”

  8. 1st Approach : WHAT ?? • b) Level 2 : • Retrieval of logical features related to the identity of the object within the media. • Query example: • “ find clips of an aeroplane taking off”

  9. 1st Approach : WHAT ?? • c) Level 3 : • Retrieval of abstract attributes associated with an understanding of the nature or purpose of the object. • Query example: • “ find a picture of nutritional disasters"

  10. 2nd Approach : HOW?? • The “ HOW” can also be classified on the basis of whether the information is retrieved by: • Query on the Content (Content based query) • Query by Example (QBE) • Time Indexed Query • Spatial Query • Application Specific Query

  11. The content of media information is • described by the metadata associated • with media objects. • Hence, these queries have to be • processed by: • accessing directly the metadata • then the media objects • Example: • “Show the details of the movie where • a cartoon character says: • “I like you” Content Based Query

  12. Have to be processed by finding a • similar object that matches the one in • the example. • The query processor has to identify • exactly the characteristics of the • example object the user wants to match. • The similarity matching required by the • user can be on texture, color, spatial • characteristics or the shape of the • object. Query by Example (QBE)

  13. Matching can be exact or partial. • For partial matching, the query • processor has to identify the degree • of mismatch that can be tolerated. • Then the query processor has to apply • the cluster generation function for the • example media object. • These cluster generating functions map • the example object into an m- • dimensional feature space. Query by Example (QBE)

  14. Object present within this distance d • are retrieved with a certain measure • of confidence and are presented as an • ordered list. • Here the distance d is proportional to • the degree of mismatch that can be • tolerated. • Example: • “ Show me the movie which contains • this song: Flying Without Wings” Query by Example (QBE)

  15. These queries are made on the • temporal characteristics of the media • objects. • The temporal characteristics can be • stored using segment index trees. • The query processor has to process the • time indexed queries by accessing the • index information stored using segment • trees or other similar methods. Time Indexed Query

  16. Example : • “Show me the movie 30 minutes after • its start” Time Indexed Query

  17. There are made on the spatial • characteristics associated with the • media objects. • These spatial characteristics can be • generated as metadata information. • The query processor can access this • metadata information to generate the • response. • Example: • “ Show me the image where Ali is seen • to the left of Abu” Spatial Query

  18. Application specific descriptions can be • stored as metadata information. • The query processor can access this • information for generating purposes. • Example : • “ Show me the video where the river • changes its course” Application Specific Query

  19. Simple Query Process • Involve single media. • Example : text Text Index Text Database Other Media DBs Text Media Query Response to Query Figure 4.1 : Processing Single Media Query

  20. Simple Query Process • Query Process: • Assuming the existence of metadata for the text • information: • the indexed file is accessed first • and the information is presented to the user

  21. Multiple Query Process • Involve more than one media. • Example : text and image Text & Image Media Query Text Index Text Database Image Index Image Database Response to Query a) Accessing Text Index First Text & Image Media Query Image Index Image Database Text Index Text Database Response to Query a) Accessing Image Index First Figure 4.2 : Processing Multiple Media Query

  22. Multiple Query Process • Query Process: • can be in TWO different ways: • a) Accessing Text Index First • Select an initial set of documents • This set of documents are examined to determine whether any documents contains the image object specified in the query. • This implies that documents carries the information regarding the contained images.

  23. Multiple Query Process • b) Accessing Image Index First • Select a set images. • Examined to determine whether images are part of any document. • This strategy assumes that the information regarding the containment of images in documents are maintained as a separated information base.

  24. Multimedia Data Access

  25. Multimedia Data Access • Access to multimedia information must be quick so that retrieval time is minimal. • Metadata must be stored using appropriate index structure to provide efficient access. • Index structures to be used depend on the media, the metadata and the types of the query. • SQL language is relevant to multimedia data.

  26. Introduction to SQL

  27. What is SQL ??.. Standard language for dealing with relational databases

  28. Introduction to SQL • developed from an earlier Structured English Query Language, SEQUEL. • with a lot of improvement, SQL become the de facto standard of the database world. • includes the specification for a data dictionary called the information schema.

  29. Creating Table • Involves TWO process: • create the structure of the table • followed by inserting the data • example: • Create the structure • CREATE TABLE employee • (employee_no CHAR (4), • employee_name VARCHAR2(30), • salary NUMBER(6,2))

  30. Manipulating Data • Insert: • INSERT INTO employee • (employee_no CHAR (4), • employee_name VARCHAR2(30), • salary NUMBER(6,2)) • VALUES • (‘123B’, ‘Ali Ahmad’, ‘1666.22’)

  31. Manipulating Data • Update: • UPDATE employee • SET employee_name =‘Abu Ahmad’ • WHERE employee_no = ‘123B’

  32. Retriving Data • SQL statements: • SELECT (select list) • FROM (table list) • WHERE (search condition) • Example: • SELECT employee_name • FROM employee • WHERE employee_no = ‘123B’

  33. Retrieving Text Data

  34. Retrieving Text Data • Text objects consist of words. • Easy to recognize because within the body of text can be delineated by spaces. • Words consist of character strings. • These features enable to process easily using query language such as SQL.

  35. Retrieving Text Data • However there are some problems with words • meaningless words such as “and, but, the, from, to” • these are called stop words • deal with them by removing or ignoring them • synonymy when a word has the same meaning as another word • polysemy when a word has more than one meaning in different context

  36. Retrieving Text Data • Principles of techniques specialized for text: • text retrieval (TR) • Schema-directed extraction (SDE) • query-directed extraction (QDE)

  37. simplest form of processing • documents are returned in the result • set if they are considered relevant to • the query • method to achieve: • exact matching • inexact matching • proximity searches • intelligent searches, ect. Text Retrieval

  38. Exact Matching • using BOOLEAN queries • SELECT cocoa_name, price • FROM cocoa_list • WHERE region = ‘Ghana’ AND category = ‘ABC’

  39. Inexact Matching • the uncertain spelling problem can be solved by using SOUNDEX function • SELECT cocoa_name, SOUNDEX (cocoa_type) • FROM cocoa_list • WHERE SOUNDEX (cocoa_type)=SOUNDEX (‘couverture’) • Coco-type SOUN • ------------------------------- • couverture C130

  40. Inexact Matching • SOUNDEX codes Table 4.1 : SOUNDEX codes

  41. Inexact Matching • vowel sounds “A,E,H,I,O,U,W,Y” are not assigned a value.

  42. LIKE operator • can be used in both exact and inexact search conditions. • LIKE keyword is used to match a column with a known string or part of a string • example: • SELECT coco_type • FROM coco_list • WHERE coco_type LIKE ‘cou%’

  43. Proximity Searches • also use LIKE to search topics within character data • example: • SELECT coco_type • FROM coco_list • WHERE character LIKE ‘%chocolate%’ • This query can be expressed using natural language as • “Give me type of each cocoa that has character that is described by a string that contains the substring “chocolate” with any number of characters before or after”

  44. it is useful for semi-structured text • documents such as business or scientific • domains where documents are laid out • to conform to a pattern Query- Directed extraction

  45. process that manipulates large • collections of related text objects such • as e-mail • schema : • e-mail {sender, receiver, date, subject, • persons, places} Schema- Directed extraction

More Related