Short note on Fingerprinting.

 Fingerprinting

An approach for comparing a large number of documents is based on the idea of fingerprinting documents. A document may be divided in all possible substrings of length. These substrings are called shingles. Based on the shingles we can define resemblance R (X, Y) and containment C (X, Y) between two documents X and Y as follows. We assume S (X) and S (Y) to be a set of shingles for documents X and Y respectively.

R (X,Y)= (S(X) S (Y)} (S (X) US (Y)}

and

C (X, Y) = {S(X)S (Y) {S (X)}

An algorithm like the following may now be used to find similar documents:

1. Collect all the documents that one wishes to compare

2. Choose a suitable shingle width and compute the shingles for each document

3. Compare the shingles for each pair of documents

4. Identify those documents that are similar

Comments

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

Explain network topology .Explain tis types with its advantages and disadvantges.