BIT 3193 MULTIMEDIA DATABASE CHAPTER 4 : QUERING MULTIMEDIA DATABASES

Language expression that describes the data to be retrieved from database.

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

Manipulation (editing and modifying data) Presentation Analysis (indexing and searching)

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

There are THREE levels of complexity associated with the “WHAT”: a)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”

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”

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"

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

Content Based Query 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”

Query by Example (QBE) 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) 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) 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”

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

Spatial Query 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”

Application Specific Query 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”

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

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

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

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.

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.

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.

Standard language for dealing with relational databases

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.

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))

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

Update: UPDATE employee SET employee_name =‘Abu Ahmad’ WHERE employee_no = ‘123B’

SQL statements: SELECT(select list) FROM(table list) WHERE(search condition) Example: SELECTemployee_name FROMemployee WHEREemployee_no = ‘123B’

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.

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

Principles of techniques specialized for text: text retrieval (TR) Schema-directed extraction (SDE) query-directed extraction (QDE)

Text Retrieval 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.

using BOOLEAN queries SELECT cocoa_name, price FROM cocoa_list WHERE region = ‘Ghana’ AND category = ‘ABC’

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

SOUNDEX codes CharacterSOUNDEX Codes B,F,P,V C,G,J,K,Q,S,X,Z D,T L M,N R Table 4.1 : SOUNDEX codes

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

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%’

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”

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

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