Presentation on theme: "1 Clustering Mohammad Rezaei 31.10.2012. Clustering Module 1 Input: raw data and parameters Output: clusters information Module 2 Input: raw."— Presentation transcript:
1 Clustering Mohammad Rezaei
Clustering Module 1 Input: raw data and parameters Output: clusters information Module 2 Input: raw data and parameters Output: Clustered markers and all functionalities for handling them on the map 2 Markers on the Google map
search_kw abd search_lnk Tables List of keywords for local services in search_kw 4 0pysäkki 4ravintola 5kahvila search_kwsearch_lnk List of keywords for local services
Logic of finding keywords 1. Find all KW_ID from search_lnk table when SERV_ID=X 2. For each KW_ID find the keyword from search_kw table 5
Photo of a service Find photo where service_id=X Service_id = 0 means the photo is not linked to any service _ _ jpg MopsiAndreiT NokiaN95_8G B/ image/jpg photo 1- If we delete a photo, we will not have the photo for the service as well 2- For a service there is a photo, then we have the location of the photo from point_id and point table, is it the same as location of the service we already have in search_cache table?
Rating User can rate a service when he is logged in User (with user_id) rates a service from x=1 to 5 In services_rating: rating += x and total_votes += 1 This is logged in “votes” table so that the user cannot rate that service again until a certain time To display rating for a service: average = int(rating/total_votes) services_rating votes