HOSTEL MANAGEMENT SYSTEM PROJECT REPORT
PROJECT REPORT
PROJECT SOURCE CODE
Database
management systems:
A database management system (DBMS) consists of software hostel management system project
report operates databases, providing storage, access, security, backup and
other facilities. Database management systems can be categorized according to
the database model hostel management system project report they
support, such as relational or XML,
the type(s) of computer they support, such as a server cluster or a mobile
phone, the query language(s) hostel management system project report access
the database, such as SQL or XQuery,
performance trade-offs, such as maximum scale or maximum speed or others. Some
DBMS cover more than one entry in this
hostel categories, e.g., supporting multiple query languages. Examples
of some commonly used DBMS are MySQL, PostgreSQL, Microsoft Access, SQLServer,
FileMaker, Oracle, Sybase, dBase, Clipper, FoxPro etc. Almost every database
software comes with an Open
Database Connectivity (ODBC)
driver hostel management system project
report allows the database to integrate with other databases.
Components
of DBMS:
Most DBMS as of 2009 implement
a relational model. Other DBMS
systems, such as Object DBMS, offer specific features for more specialized
requirements. Their components are similar, but not identical.
RDBMScomponents:
§ Sublanguages— Relational DBMS (RDBMS) include Data Definition Language (DDL) for defining the structure of
the database, Data Control
Language (DCL) for defining
security/access controls, and Data
Manipulation Language (DML) for
querying and updating data.
§ Interface drivers:-This
hostel drivers are code libraries hostel management system project report
provide methods to prepare statements,execute statements, fetch results, etc.
Examples include ODBC, JDBC, MySQL/PHP, FireBird/Python.
§ SQL engine:-This
hostel component interprets and executes the DDL, DCL,
and DML statements. It includes three major
components (compiler, optimizer, and executor).
§ Transaction engine:-Ensures hostel management system project report
multiple SQL statements either succeed or fail as a group, according to
application dictates.
§ Relational engine:-Relational objects such as Table, Index,
and Referential integrity constraints are implemented in this hostel component.
§ Storage engine:-This
hostel component stores and retrieves data from secondary storage, as
well as managing transaction commit and rollback, backup and recovery, etc.
ODBMScomponents:
Object DBMS (ODBMS) has transaction and storage components hostel management system project report are
analogous to those in an RDBMS. Some DBMS handle DDL, DML and update tasks
differently. Instead of using sublanguages, they provide APIs for this hostel purposes. They typically include a
sublanguage and accompanying engine for processing queries with interpretive
statements analogous to but not the same as SQL. Example object query languages
are OQL, LINQ, JDOQL, JPAQL and others. The query engine returns
collections of objects instead of relational rows.
Types:
Operational
database:
This hostel
databases store detailed data about the operations of an organization. They are
typically organized by subject matter, process relatively high volumes of
updates using transactions.
Essentially every major organization on earth uses such databases. Examples
include customer databases hostel management system project
report record contact, credit, and demographic information about a business'
customers, personnel databases hostel
management system project report hold information such as salary, benefits,
skills data about employees, Enterprise resource planning hostel management system project report record
details about product components, parts inventory, and financial databases hostel management system project report keep
track of the organization's money, accounting and financial dealings.
Data warehouse:
Data warehouses archive
modern data from operational databases and often from external sources such as
market research firms. Often operational data undergoes transformation on its
way into the warehouse, getting summarized, anonymized, reclassified, etc. The
warehouse becomes the central source of data for use by managers and other
end-users who may not have access to operational data. For example, sales data
might be aggregated to weekly totals and converted from internal product codes
to use UPC codes so hostel management system project report it can
be compared with ACNielsen data.Some basic and essential
components of data warehousing include retrieving and analyzing data,
transforming,loading and managing data so as to make it available for further
use.
Operations in a data warehouse are typically concerned with
bulk data manipulation, and as such, it is unusual and inefficient to target
individual rows for update, insert or delete. Bulk native loaders for input
data and bulk SQL passes for aggregation are the norm.
Analytical database:
Analysts may do their work directly against a data
warehouse or create a separate analytic database for Online Analytical Processing.
For example, a company might extract sales records for analyzing the
effectiveness of advertising and other sales promotions at an aggregate level.
Distributed
database:
This hostel are
databases of local work-groups and departments at regional offices, branch
offices, manufacturing plants and other work sites. This hostel databases can include segments of both
common operational and common user databases, as well as data generated and
used only at a user’s own site.
End-user
database:
This hostel
databases consist of data developed by individual end-users. Examples of this hostel are collections of documents in
spreadsheets, word processing and downloaded files, even managing their
personal baseball card collection.
External
database:
This hostel
databases contain data collected for use across multiple organizations, either
freely or via subscription. The Internet
Movie Database is one example.
Hypermedia
databases:
The World
Wide Web can be thought of as a
database, albeit one spread across millions of independent computing systems. Web browsers "process" this hostel data one page at a time, while web crawlers and other software provide the
equivalent of database indexes to support search and other activities.
Models:
Post-relational
database models:
Products offering a more general data model than the
relational model are sometimes classified as post-relational Alternate terms include "hybrid
database", "Object-enhanced RDBMS" and others. The data model in
such products incorporates relations but is not constrained by E.F. Codd's Information Principle,
which requires hostel management system
project report all information in the database must be cast explicitly in terms
of values in relations and in no other way some of this hostel extensions to the relational model
integrate concepts from technologies hostel management system project report
pre-date the relational model. For example, they allow representation of a directed graph with trees on the nodes.
Some post-relational products extend relational systems
with non-relational features. Others arrived in much the same place by adding
relational features to pre-relational systems. Paradoxically, this hostel allows products hostel management system project report are
historically pre-relational, such as PICK and MUMPS,
to make a plausible claim to be post-relational.
Database Developer:
Our skilled team have confident hands & expertise on:-
·
Oracle
·
MS SQL Server
·
My SQL
·
MS Access
Object
database models:
In recent years, the object-oriented paradigm has been applied in areas
such as engineering and spatial databases, telecommunications and in various
scientific domains. The conglomeration of object oriented programming and
database technology led to this hostel
new kind of database. This hostel
databases attempt to bring the database world and the application-programming
world closer together, in particular by ensuring hostel management system project report the
database uses the same type
system as the application
program. This hostel aims to avoid the
overhead (sometimes referred to as the impedance
mismatch) of converting information between its representation in the
database (for example as rows in tables) and its representation in the
application program (typically as objects). At the same time, object databases
attempt to introduce key ideas of object programming, such as encapsulation and polymorphism,
into the world of databases.
A variety of this
hostel ways have been triedfor storing objects in a database. Some
products have approached the problem from the application-programming side, by
making the objects manipulated by the program persistent.
This hostel also typically requires the
addition of some kind of query language, since conventional programming
languages do not provide language-level functionality for finding objects based
on their information content. Othershave attacked the problem from the database
end, by defining an object-oriented data model for the database, and defining a
database programming language hostel management system project
report allows full programming capabilities as well as traditional query
facilities.
Storage
structures:
Databases
may store relational tables/indexes in memory or on hard disk in one of many
forms:
§ ordered/unordered flat
files
§ ISAM
§ heaps
§ hash buckets
§ logically-blocked files
§ Fractal Tree indexes
§ B+ trees
The most
commonly usedare B+ trees and ISAM.
Object databases use a range of storage mechanisms. Some
use virtual memory-mapped files to make the native language (C++, Java etc.)
objects persistent. This hostel can be
highly efficient but it can make multi-language access more difficult. Others
disassemble objects into fixed- and varying-length components hostel management system project report are
then clustered in fixed sized blocks on disk and reassembled into the
appropriate format on either the client or server address space. Another
popular technique involves storing the objects in tuples (much like a relational database) which the
database server then reassembles into objects for the client.
Other techniques include clustering by category (such as
grouping data by month, or location), storing pre-computed query results, known
as materialized views,
partitioning data by range (e.g., a data range) or by hash.
Memory management and storage topology can be important
design choices for database
designers as well. Just as normalization is used to reduce storage requirements
and improve database designs, conversely renormalizations are often used to
reduce join complexity and reduce query execution time.
Indexing:
Indexing is a
technique for improving database performance. The many types of index share the
common property hostel management system
project report they eliminate the need to examine every entry when running a
query. In large databases, this hostel
can reduce query time/cost by orders of magnitude. The simplest form of index
is a sorted list of values hostel
management system project report can be searched using a binary search with an adjacent reference to the location
of the entry, analogous to the index in the back of a book. The same data can
have multiple indexes (an employee database could be indexed by last name and
hire date.)
Indexes affect performance, but not results. Database
designers can add or remove indexes without changing application logic,
reducing maintenance costs as the database grows and database usage evolves.
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