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J. Glenn Brookshear

Chapter 9. C H A P T E R 3. DataBase & DBMS. J. Glenn Brookshear 蔡 文 能. J. Glenn Brookshear. Chapter 9: Database Systems. 9.5 Traditional File Structures 9.1 Database Fundamentals 9.2 The Relational Model 9.3 Object-Oriented Databases 9.4 Maintaining Database Integrity

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J. Glenn Brookshear

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  1. Chapter9 C H A P T E R 3 DataBase & DBMS J. Glenn Brookshear 蔡 文 能 J. Glenn Brookshear

  2. Chapter 9: Database Systems • 9.5 Traditional File Structures • 9.1 Database Fundamentals • 9.2 The Relational Model • 9.3 Object-Oriented Databases • 9.4 Maintaining Database Integrity • 9.6 Data Mining • 9.7 Social Impact of Database Technology

  3. Data Structure vs. File Structure • Data Structure : How to arrange data in memory • File Structure : How to arrange data in Disk and/or any other secondary storage • DataBase and DataBase Management System • Users do NOT have to care about how to store data in a file. DBMS will handle the detail. • Users can use SQL (Structured Query Language) to access the DataBase in an interactive command and/or through a program (embeded SQL) RAM Disk

  4. File Types • Sequential file: one accessed in a serial manner from beginning to end. E.g. audio, video, text, programs. • Text file: sequential file in which each logical record is a single character. ASCII: 1 byte/char Unicode: 2 bytes/char

  5. Sequential Files • Sequential file: A file whose contents can only be read in order • Reader must be able to detect end-of-file (EOF) • Data can be stored in logical records, sorted by a key field • Greatly increases the speed of batch updates

  6. Text Files • Simple file structure. • Extendable to more complex file structures using markup languages (XHTML, HTML). • XHTML, HTML control the display of the file on a monitor. • XML is a standard for markup languages.

  7. Converting data from two’s complement notation into ASCII for storage in a text file

  8. Figure 9.15 A procedure for merging two sequential files

  9. Figure 9.16Applying the merge algorithm (Letters are used to represent entire records. The particular letter indicates the value of the record’s key field.)

  10. XML • Example <note> <to>Tove</to> <from>Jani</from> <heading>Reminder</heading> <body>Don't forget me this weekend!</body> </note> http://www.w3schools.com/xml/xml_whatis.asp

  11. Indexed File key pointer Data item for Brown Data file Index file Index: A list of key values and the location of their associated records

  12. An inverted file

  13. A file with a partial index

  14. Figure 9.17 Opening an indexed file

  15. Hashing • In hashing the index file is replaced by a hash function. • The storage space is divided into buckets. • Each record has a key field. Each record is stored in the bucket corresponding to the hash of its key. • A hash function computes a bucket number for each key value. • Advantage: no index table needed. • Disadvantages: i) hash function needs careful design; ii) unpredictable performance

  16. Terminology • Bucket: section of the data storage area. • Key: identifier for a block of information. • Hash function: takes as input a key and outputs a bucket number. • Collision: two keys yield the same bucket number.

  17. Hash Functions • Hash Function Requirements • Easily and quickly computed. • Values evenly spread over the bucket numbers. • What can go wrong: bucket number computed from 1st and 3rd characters of a name: Brown, Brook, Broom, Broadhead, Biot, Bloom, … • Examples of Hash Functions • Mid square: compute (key x key) and set bucket number = middle digits. • Extraction: select digits from certain positions within the key. • Divide key by number of buckets and use the remainder.

  18. Figure 9.18 Hashing the key field value 25X3Z to one of 41 buckets

  19. Figure 9.19 The rudiments of a hashing system, in which each bucket holds those records that hash to that bucket number

  20. Collisions in Hashing • Collision: The case of two keys hashing to the same bucket • Clustering problem: Poorly designed hashing function can have uneven distribution of keys into buckets • Collision also becomes a problem when there aren’t enough buckets (probability greatly increases as load factor (% of buckets filled) approaches 75%) • Solution: somewhere between 50% and 75% load factor, increase number of buckets and rehash all data

  21. Handling bucket overflow

  22. A large file partitioned into bucketsto be accessed by hashing

  23. The role of an operating system when accessing a file System calls ?

  24. Maintaining a file’s order by means of a File Allocation Table (FAT)

  25. Information Required on a Hard Drive to Load an OS • Startup BIOS (POST , Load MBR) • Master Boot Record (MBR) • Master Boot Program • Partition Table (16 bytes * 4) • OS Boot Record (Boot Sector) • Loads the first program file of the OS • Boot Loader Program • Begins process of loading OS into memory

  26. How Data Is Logically Stored on a Floppy Disk • All floppy and hard disk drives are divided into tracks and sectors • Tracks are concentric circles on a disk • Sector • Always 512 bytes • Physical organization of a disk • BIOS manages disk as sectors • Cluster (file allocation unit) • Group of sectors • Logical organization of a disk • OS views disk as a list of clusters

  27. The Boot Record • Track 0, sector 1 of a floppy disk • Contains basic information about how the disk is organized • Includes bootstrap program, which can be used to boot from the disk • Uniform layout and content of boot record allows any version of DOS or Windows to read any DOS or Windows disk

  28. The File Allocation Table (FAT) • Lists the location of files on disk in a one-column table • Floppy disk FAT is 12 bits wide, called FAT12 • Each entry describes how a cluster on the disk is used • A bad cluster on the disk will be marked in the FAT

  29. The Root Directory • Lists all the files assigned to this table • Contains a fixed number of entries • Some items included are: • Filename and extension • Time and date of creation or last update • File attributes • First cluster number

  30. How a Hard Drive is Logically Organized to Hold Data • Low-level format • Creates tracks and sectors, done at factory • Partition the hard drive (FDISK.EXE) • Creates partition table at the beginning of drive • High-level format • Done by OS for each logical drive • Master Boot Record (MBR) is the first 512 bytes of a hard drive • Master boot program (446 bytes) calls boot program to load OS • Partition table • Description, Location, Size

  31. Partitions and Logical Drives

  32. FAT16 • Supported by DOS and all versions of Windows • Uses 16 bits for each cluster entry • As the size of the logical drive increases, FAT16 cluster size increases dramatically

  33. FAT32 • Became available with Windows 95 OSR2 • Used 32 bits per FAT entry, although only 28 bits were used to hold cluster numbers • More efficient than FAT16 in terms of cluster size

  34. NTFS • Supported by Windows NT/2000/XP • Provides greater security • Used a database called the master file table (MFT) to locate files and directories • Supports large hard drives

  35. Comparing FAT16, FAT32, NTFS

  36. Figure 9.1: A file versus a database organization (1/2)

  37. Figure 9.1: A file versus a database organization (2/2)

  38. Figure 9.2: The conceptual layers of a database

  39. Schemas • Schema: A description of the structure of an entire database, used by database software to maintain the database • Subschema: A description of only that portion of the database pertinent to a particular user’s needs, used to prevent sensitive data from being accessed by unauthorized personnel

  40. Database Management Systems • Database Management System (DBMS): A software layer that manipulates a database in response to requests from applications • Distributed Database: A database stored on multiple machines • DBMS will mask this organizational detail from its users • Data independence: The ability to change the organization of a database without changing the application software that uses it

  41. Database Models • Database model: A conceptual view of a database • Relational database model • Object-oriented database model

  42. Relational Database Model • Relation: A rectangular table • Attribute: A column in the table • Tuple: A row in the table • Relational Design • Avoid multiple concepts within one relation • Can lead to redundant data • Deleting a tuple could also delete necessary but unrelated information

  43. Improving a Relational Design • Decomposition: Dividing the columns of a relation into two or more relations, duplicating those columns necessary to maintain relationships • Lossless or nonloss decomposition: A “correct” decomposition that does not lose any information

  44. Figure 9.3: A relation containing employee information

  45. Figure 9.4: A relation containing redundancy

  46. Figure 9.5 An employee database consisting of three relations

  47. Figure 9.6 Finding the departments in which employee 23Y34 has worked

  48. Figure 9.7: A relation and a proposed decomposition

  49. Relational Operations • Select: Choose rows • Project: Choose columns • Join: Assemble information from two or more relations

  50. Figure 9.8: The SELECT operation

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