Consider CITIES ( Code , Name, Area, Population, Country ) relation containing 1 0 0 0 0 records, where each page of the relation holds 5 records. Assume that Code attribute takes integer values in the range from 1 to 1 0 0 0 0 ; Area and Population attributes take integer values. For each of the following queries, choose one of the below listed indexes that would most likely require the fewest disk accesses for processing the query. ( You don't need to calculate the number of disk accesses made, you should write one of the given index structure that should be used ) The indexes that can be used are as follows: : A lustened Be tse index on attribute Code. An extendible hash index on attribute Code. • A linear hash index on attribute Name. • A trie index on attribute Name. • A grid file on attributes Name, Area, and Population. • An ISAM index on attribute Population. • An inverted index on attribute Country. The queries are: a ) Find all cities where Country = " T ü rkiye " . b ) Find all cities where Code 2 0 AND Code 1 5 0 0 0 0 0 . d ) Find all cities where Area

Q: Consider CITIES ( Code , Name, Area, Population, Country ) relation containing 1 0 0 0 0 records, where each page of the relation holds 5 records. Assume that Code attribute takes integer values in the range from 1 to 1 0 0 0 0 ; Area and Population attributes take integer values. For each of the following queries, choose one of the below listed indexes that would most likely require the fewest disk accesses for processing the query. ( You don't need to calculate the number of disk accesses made, you should write one of the given index structure that should be used ) The indexes that can be used are as follows: : A lustened Be tse index on attribute Code. An extendible hash index on attribute Code. • A linear hash index on attribute Name. • A trie index on attribute Name. • A grid file on attributes Name, Area, and Population. • An ISAM index on attribute Population. • An inverted index on attribute Country. The queries are: a ) Find all cities where Country = " T ü rkiye " . b ) Find all cities where Code > 2 0 AND Code 1 5 0 0 0 0 0 . d ) Find all cities where Area < 1 5 0 0 AND Population < 1 0 0 0 0 0 0 . e ) Find all cities where Name starts with "Ada".

For each of the queries listed, I will provide the index that would most likely require the fewest disk accesses for processing the query: a) ⚹ For the query to find all cities where Country = "Türkiye", the best index to use would be the inverted index on attribute Country. ⚹ b) ⚹ For the query to find all cities where Code > 20 AND Code 15000000, the ISAM index on attribute Population would be suitable, as it is optimized for range queries on sequentially ordered data. ⚹ d) ⚹ For the query to find all cities where Area

Question

Consider CITIES ( Code , Name, Area, Population, Country ) relation containing 1 0 0 0 0 records, where each page of the relation holds 5 records. Assume that Code attribute takes integer values in the range from 1 to 1 0 0 0 0 ; Area and Population attributes take integer values. For each of the following queries, choose one of the below listed indexes that would most likely require the fewest disk accesses for processing the query. ( You don't need to calculate the number of disk accesses made, you should write one of the given index structure that should be used ) The indexes that can be used are as follows: : A lustened Be tse index on attribute Code. An extendible hash index on attribute Code. • A linear hash index on attribute Name. • A trie index on attribute Name. • A grid file on attributes Name, Area, and Population. • An ISAM index on attribute Population. • An inverted index on attribute Country. The queries are: a ) Find all cities where Country = " T ü rkiye " . b ) Find all cities where Code > 2 0 AND Code < 1 0 0 . c ) Find all cities where Pop 𝚤 lation > 1 5 0 0 0 0 0 . d ) Find all cities where Area < 1 5 0 0 AND Population < 1 0 0 0 0 0 0 . e ) Find all cities where Name starts with "Ada".

Accepted Answer

For each of the queries listed, I will provide the index that would most likely require the fewest disk accesses for processing the query: a) ⚹ For the query to find all cities where Country = "Türkiye", the best index to use would be the inverted index on attribute Country. ⚹ b) ⚹ For the query to find all cities where Code > 20 AND Code < 100, an efficient index to use would be the B-tree index on attribute Code, as it allows for range queries. ⚹ c) ⚹ For the query to find all cities where Population > 15000000, the ISAM index on attribute Population would be suitable, as it is optimized for range queries on sequentially ordered data. ⚹ d) ⚹ For the query to find all cities where Area < 1500 AND Population < 10000000, the grid file on attributes Name, Area, and Population would be the most appropriate, as it allows for efficient multi-attribute queries. ⚹ e) ⚹ For the query to find all cities where Name starts with "Ada", the trie index on attribute Name would be the most efficient, as tries are particularly well-suited for queries that involve string prefixes. ⚹

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