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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 • The content of media information is described by the metadata associated with media objects. Content Based Query • 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” • Have to be processed by finding a similar object that matches the one in the example. Query by Example (QBE) • 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. • Matching can be exact or partial. • For partial matching, the query processor has to identify the degree of mismatch that can be tolerated. Query by Example (QBE) • 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 mdimensional feature space. • Object present within this distance d are retrieved with a certain measure of confidence and are presented as an ordered list. Query by Example (QBE) • 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” • These queries are made on the temporal characteristics of the media objects. Time Indexed Query • 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. • Example : Time Indexed Query “Show me the movie 30 minutes after its start” • There are made on the spatial characteristics associated with the media objects. Spatial Query • 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 descriptions can be stored as metadata information. Application Specific Query • 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 Media Query Text Index Text Database Other Media DBs Response to 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 & 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 Response to Query a) Accessing Image Index First Figure 4.2 : Processing Multiple Media Query Text Database • 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’, ‘1666.22’) • Update: UPDATE employee SET employee_name =‘Abu Ahmad’ WHERE employee_no = ‘123B’ • SQL statements: SELECT (select list) FROM (table list) WHERE (search condition) • Example: SELECT employee_name FROM employee WHERE employee_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 Table 4.1 : SOUNDEX codes Character SOUNDEX Codes B,F,P,V 1 C,G,J,K,Q,S,X,Z 2 D,T 3 L 4 M,N 5 R 6 • 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” QueryDirected 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 SchemaDirected extraction • process that manipulates large collections of related text objects such as e-mail • schema : e-mail {sender, receiver, date, subject, persons, places}