PHYSICAL DATABASE DESIGN EXAMPLES Example 1 – Consider the following relational database for the Super Baseball League. It keeps track of teams in the league, coaches and players on the teams, work experience of the coaches, bats belonging to each team, and which players have played on which teams. Note the following facts about this environment: The database keeps track of the history of all of the teams that each player has played on and all of the players who have played on each team. The database only keeps track of the current team that a coach works for. Team number, team name, and player number are each unique attributes across the league. Coach name is only unique within a team (and we assume that a team cannot have two …show more content…
The Team Number field. ii. The Player Name field. b) Construct a B+-tree index of the type shown in this chapter for the Player file, assuming that now there are many more records than are shown above. The file and the index have the following characteristics: The file is stored on eight cylinders of the disk. The highest key values on the eight cylinders, in order, are: Cylinder 1: 1427 Cylinder 2: 1965 Cylinder 3: 2848 Cylinder 4: 3721 Cylinder 5: 4508 Cylinder 6: 5396 Cylinder 7: 6530 Cylinder 8: 7442 Each index record can hold four key value/pointer pairs. There are three index records at the lowest level of the tree index. c) The same as part b above, but now there are four index records at the lowest level of the tree index. d) The same as part b above, but each index record can hold two key value/pointer pairs and there are four index records at the lowest level of the tree index. Answer - a) Simple linear index. i. (Based on Team Number.) 12 1 12 3 12 8 15 9 18 5 18 6 20 4 20 7 35 2 35 10 ii. (Based on Player Name.) Barry Morton 10 Chico Lopez 8 Chris Vernon 9 Dave Lester 6 Fred Williams 1 Juan Gomez 3 Rod Smith 7 Steve
Although technically in charge of all of the coaches, they are often far beyond well-compensated
According to the data downloaded from the website, it involved some information about the all ordinaries index. For instance, it covered the date, open, high, low, close, and volume and adjust close.
We can highlight some insight right away by just looking at table 1. A quick overview of the attributes’ path-worths values :
to do the specific task. It has a node structure which contains integer data, pointers for next node and previous node of doubly linked list. It has a Head node
Sequential file organizations access data sequentially from the beginning [1], i.e. to reach file 27, the 26 preceding files must be accessed first. They are much slower to access compared to random access [1]. Sequential files are stored on a sequential access device [2]. The records contained in sequential files are stored in a predetermined order [2], according to the value of a search key [1], and are stored one after the other [2] as they are inserted into the file. Records can only be accessed (read from and written to) sequentially (i.e. in the same order that they were entered) [3]. Sequential files are designed for efficient processing [1]). Records stored in sequential files cannot be deleted,
At each step the search space is condensed hierarchically and the Binary trie is a sequential prefix search by size. Till the onset to a node without branch node can be inserted, putting in a prefix starts with a search. As a prefix and erasing idle nodes, removing processes begins also with a search unmarking the node. As the prefixes are characterized by the trie configuration, nodes don't store prefixes.
the table $T_{vo}$ and table $T_{vc}$ shown as Figure~\ref{f:data structure access}(d) and Figure~\ref{f:data structure access}(e).
The right branch has records 1,3,8,9,10. Now we split the right child which has records 1,3,8,9,10. Candidate Split Left Child Node, tL Right Child Node, tR
The left sub-tree contains only nodes with keys less than the parent node; the right sub-tree contains only nodes with keys greater than the parent node. BSTs are also dynamic data structures, and the size of a BST is only limited by the amount of free memory in the operating system. The main advantage of binary search trees is that it remains ordered, which provides quicker search times than many other data structures.
Indexes are used to boost performance in a database. Finding an individual record or set of records most efficiently done by Index. Index key is the reference point where an index is an ordered arrangement of keys and pointers. Each key is appointed to the location of the data recognized by the key. For example: when we print out at the NEU library we have given our User ID so
c) how individual records can be accessed and how long it takes to access them.
2.) What is the value in the array element when the index contains 2? (Reference: array tutorial)
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Question 1: Assume a base cuboid of 10 dimensions contains only three base cells: (1) (a1, b2, c3, d4; ..., d9, d10), (2) (a1, c2, b3, d4, ..., d9, d10), and (3) (b1, c2, b3, d4, ..., d9, d10), where a_i != b_i, b_i != c_i, etc. The measure of the cube is count. 1, How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2, How many nonempty aggregate (i.e., non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3, which yields the result. 3, How many