MultiDimensionalSearch
Consider the web site of a seller like Amazon.
They carry tens of thousands of products, and each product has many
attributes (Name, Size, Description, Keywords, Manufacturer, Price, etc.).
The search engine allows users to specify attributes of products that
they are seeking, and shows products that have most of those
attributes. To make search efficient, the data is organized using
appropriate data structures, such as balanced trees. But, if products
are organized by Name, how can search by price implemented efficiently?
The solution, called indexing in databases, is to create a new set of
references to the objects for each search field, and organize them to
implement search operations on that field efficiently. As the objects
change, these access structures have to be kept consistent.
In this project, each object has 3 attributes: id (long int), description (one or more long ints), and price (dollars and cents). The following operations are supported:
a. Insert(id,price,list): insert a new item whose description is given in the list. If an entry with the same id already exists, then its description and price are replaced by the new values, unless list is null or empty, in which case, just the price is updated. Returns 1 if the item is new, and 0 otherwise.
b. Find(id): return price of item with given id (or 0, if not found).
c. Delete(id): delete item from storage. Returns the sum of the long ints that are in the description of the item deleted (or 0, if such an id did not exist).
d. FindMinPrice(n): given a long int, find items whose description contains that number (exact match with one of the long ints in the item's description), and return lowest price of those items. Return 0 if there is no such item.
e. FindMaxPrice(n): given a long int, find items whose description contains that number, and return highest price of those items. Return 0 if there is no such item.
f. FindPriceRange(n,low,high): given a long int n, find the number of items whose description contains n, and in addition, their prices fall within the given range, [low, high].
g. PriceHike(l,h,r): increase the price of every product, whose id is in the range [l,h] by r%. Discard any fractional pennies in the new prices of items. Note that you are truncating, not rounding. Returns the sum of the net increases of the prices.
h. RemoveNames(id, list): Remove elements of list from the description of id. It is possible that some of the items in the list are not in the id's description. Return the sum of the numbers that are actually deleted from the description of id. Return 0 if there is no such id.
Implement the operations using data structures that are best suited for the problem.
Input specification: Initially, the store is empty, and there are no items. The input contains a sequence of lines (use test sets with millions of lines). Lines starting with "#" are comments. Other lines have one operation per line: name of the operation, followed by parameters needed for that operation (separated by spaces). Lines with Insert operation will have a "0" at the end, that is not part of the name. The output is a single number, which is the sum of the following values obtained by the algorithm as it processes the input.