Discuss the use of data warehousing and data mining in agriculture development.

 Dataware house and data mining in Agriculture development

The agricultural census performed by the Ministry of Agriculture, Government of India, compiles a large number of agricultural parameters at the national level. District-wise agricultural production area and yield of crops are compiled, analysis, mining, and forecast statistics on the consumption of fertilizers can be turned into a data merge. Data n agricultural inputs such as seeds and fertilizers can also be effectively analyzed in a warehouse. Data from livestock census can be turned into a data warehouse. Land use pat statistics can also be analyzed in a warehousing environment. Thus, there is the substantial application of data warehouse housing and data mining techniques in the agricultural sector.

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.