#aggregation in dbms pdf

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DBMS in Hindi - Aggregation

DBMS in Hindi – Aggregation

aggregation in dbms, aggregation kya hai, DBMS Aggregation in Hindi

DBMS Aggregation in Hindi

एकत्रीकरण (aggregation) में, दो entities के बीच संबंध को एक entity के रूप में माना जाता है। एकत्रीकरण में, इसकी संबंधित entities के साथ संबंध एक higher level इकाई में एकत्रित होता है।

उदाहरण के लिए:यदि कोई visitor किसी कोचिंग सेंटर में जाता है, तो वह कभी भी कोर्स के बारे में या सिर्फ सेंटर के बारे में…

[ijtsrd]

Relative Study on Warm and Hot Bituminous Mixes

by Ashish Chauhan | Nitin Thakur "Relative Study on Warm and Hot Bituminous Mixes"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd33122.pdf

Paper Url :https://www.ijtsrd.com/engineering/civil-engineering/33122/relative-study-on-warm-and-hot-bituminous-mixes/ashish-chauhan

ugcapprovedmanagementjournal, openaccessjournalofmanagement, paperpublicationinmanagement

In this work, comparison made between the Warm mix and bituminous mix technology. Warm Mix Asphalt WMA technology is one of the solutions which combine energy savings and environmental benefits. It is a modification of the conventional method of construction of flexible pavements which is surfaced with Hot Mix Asphalt HMA , which involves high mixing temperatures up to 140oC. A bituminous mix essentially consists of coarse aggregates, fine aggregates, filler and bitumen which bind together all the constituents at high temperature. The optimum dosage of Bitubuild WM was found out to be 2 by weight of bitumen and the optimum binder content for the mix was 5.66 for BC samples and 4.68 in DBM samples. No change in optimum binder content was found by addition of warm mix additive. There was a slight increase in densities of mix with addition of warm mix additive. In BC sample, there was an increase from 2.344 g cc to 2.370 g cc and in DBM sample, there was increase from 2.369 g cc to 2.378 g cc. There was a slight increase in Marshall Stability value with addition of warm mix additive. In BC sample, there was an increase from 21.15 kN to 25.53 kN and in DBM sample, there was increase from 20.17 kN to 22.14 kN. 

[thebazone]

Data - Tiny Word - BIG Impact

At The BA Zone we like to stress the importance of user-centered design. Today, I'd like to focus on data - a tiny word - that can have significant impact on all aspects of an organization, such as:  architecture, database and software design, reporting, marketing, sales, processing, compliance, and a host of other areas, all of which can lead to success or failure.

From a broader perspective, data is aggregated, analyzed, manipulated, and used for advancement across a wide spectrum of organizations and industries, such as:

Consumer-focused products

eCommerce

Healthcare

Science and Engineering

Law Enforcement

Education

Entertainment

From punch cards to self-evolving, artificial neural networks that are capable of learning based on new data inputs - data has moved from supporting business to driving business. As a BA, you need a clear, concise understanding of data concepts, structures, mapping, and analysis in order to:

Enable you to ask the right questions

Identify business results that are attainable or require process changes

Understand the impact of adding or removing data

Ensure data integrity and security

Create conceptual and logical data models

Migrate applications/databases obtained through business acquisitions

Empower users with the right information at the right time

Data Basics

There are numerous books and articles (in print and online) that cover data and database creation and management. Let's dip a toe in the water and start by reviewing some basic data concepts (just a few for now):

Primary Data vs. Derived Data

Primary Data - is data that is entered into a system manually or via file feeds.

Derived Data - is data that results from taking two or more primary data elements and applying some type of calculation, algorithm, logic, etc., the result of which is called a derived data element.

De-identified Data vs. Identified Data

De-identification of data is the process of protecting the identify of individuals and their personal information by removing or masking identifying data such as name, date of birth, social security number, etc. By using a unique identifier, data can later be re-identified. For example, healthcare organizations de-identify customer information based on the Health Insurance Portability and Accountability Act (HIPAA), designed to protect patient medical records by providing privacy compliance standards. 

Identified data is data where and individual's personal, identifying information is not masked or removed and, therefore, viewable to everyone with access to that information.

Batch Processing vs. Real-Time Processing

Two methods of handling, processing, and ingesting data into a database include: Batch Processing and Real-time/Near-time Processing. Many systems today employ both methods.

Batch Processing - takes datasets, file feeds, etc. and breaks them down into batch jobs that are scheduled for automatic processing and input into the database. These jobs often include operations that perform validation and modification of incoming data, formatting data, and the handling of bad data, just to name a few.

Real-time or Near-time processing - reflects data that is processed immediately in real-time or as near to real-time (e.g. near-time) as possible.

In general, data is collectively stored in structured tables (entities), columns (attributes) and rows (records) within databases and updated depending on multiple variables/operations. Some basic operations that can be performed on data include:  create, update, delete, read-only (sometimes called - reference), and query.

Types of Databases

There are different types of databases based on function. Here are a few of the most commonly used database types:

Operational/Transactional Databases

As its name implies, an operational/transactional database is the real-time, database-of-record that manages and stores data elements from the day-to-day operational processes and/or transactions of an organization. For example, that Mystery Science Theater 3000 (MST3K) video-on-demand you purchased from RiffTrax - what was it again? Oh right, The Crawling Eye, (you have to see it to believe it) would be considered a transaction. In general, these databases reflect current events/data elements along with control data (e.g. flags, counters, etc.) and are designed for fast retrieval/update of data and usually provide minimal reporting.

Data Warehouses and Data Marts

Data Warehouses are, typically, integrated with one or more operational/transactional databases to manage and store multiple versions of events and data elements, creating a historical view and audit trail. For example, detailed, multiple, hospital admissions for a patient over a period of time. Data Warehouses can support high volume, analytical processing, and reporting capabilities. Whereas, data warehouses generally store information at the enterprise level, Data Marts are subsets of data warehouses and provide targeted information. For example, Data Marts can provide client specific dashboard views of information.

Distributed Databases

Distributed databases are individual databases or portions of a database stored in multiple physical locations that are synchronized by a centralized, software system called a distributed database management system (DDBMS). This type of database might be used in a company that has multiple branches or offices, etc. Although physically separated, to users this type of database looks like a single database.

Database Management System (DBMS)

A database management system (DBMS) is software designed to enable users to manage data in a database. Typically managed by a database administrator (DBA), using a query language, the DBMS software interacts with the database to: insert, remove, modify, validate, and retrieve data. The software also provides protection of the database through secure access and recovery measures from user/hardware failures.

How organizations structure their databases is dependent on multiple variables. These variables can be revealed using data modeling techniques.

Data Modeling

A data model is a visual representation of an organization's data that allows database designers, developers, and end users to understand and agree on the organization and manipulation/integrity of data, the relationships between data and any constraints on the data. Data models can be grouped into three levels:

Conceptual Model (also known as a Data Model) - is a high-level representation of the type of information an organization needs, primarily focusing on entities (e.g. person, place, thing, process, event, etc., which you can collect data on) and their relationships and constraints.

Logical Model - takes the conceptual/data model a step further by adding as much detail as possible (e.g. attributes/details of entities and detailed relationships) and then layers in some of the technical aspects of implementation without detailing the physical database structure.

Physical Model - specifies database design, structure, tables, columns, rows, constraints, keys (primary/foreign), implementation, and physical storage, etc.

As a Business Analyst, you might be asked to elicit and provide the Conceptual and/or Logical models. As you create these models, focus on capturing a clear, detailed model, free of ambiguity that communicates everyone's understanding of the data.

Types of Database Models 

There are a variety of data models (also called, data structures and data schema) out there.  Here's a high-level look at some (not all) of these models.

Hierarchical Database Model

The Hierarchical database model organizes data into a tree-like, linear structure with a parent-child relationship where each child (record) has a single parent (table). This model states that each child (record) must have only one parent (table), but each parent (table) can have one to many children (records). In order for data to be retrieved from this type of model, it must begin at the parent (or root node).

Relational Database Model

The Relational database model, currently the most widely used database model, logically organizes data as independent tables with each table assigned a key field that connects it to data in one or more other tables. Retrieval of data from this type of model uses these keys - a Primary Key (PK) and a Foreign Key (FK) -  to create a relationship between the tables and quickly access data.

Network Database Model

The Network database model expands the Hierarchical Model from a one parent - many children construct to a model that employs records and sets, where a set consists of one parent record and one or more child records, allowing a record type to be a child of more than one set. This model allows retrieval of data by navigating through these individual sets instances.

Object-Oriented Database Model

The Object-Oriented database model (also called Object Database Management Systems - ODBMS) are databases that store their contents as objects. Objects contain data and executable code in the form of Attributes (data that defines the characteristics of an object) and Methods (the behavior of an object). Classes are used as a template to define the data and methods within the object. This type of model can be used to address complex data/complex data relationships.

Entity-Relationship Database Model

In the Entity-Relationship database model (also referred to as an Entity-Relationship Diagram (ERD), data is defined as Entities (people, places, things, processes) and Attributes (characteristics of an entity), which together make up their domain. The relationships (cardinality) between entities states how may rows in one entity will match rows in another entity (e.g. one-to-one, one-to-many, many-to-many, etc.).

Document Database Model

A Document database model (also referred to as a document store or document-oriented database) is a software program that stores, retrieves and manages document-oriented information (e.g. Microsoft Word, PDF, XML, etc.) that is semi-structured in nature. Instead of employing a table format (rows and columns, etc.), each document can have the same or different structure. To support this design, documents are grouped into Collections, which can then be queried (searched) for documents with certain attributes.

Entity-Attribute-Value Database Model

An Entity-Attribute-Value (EAV) database model (also referred to as an Object-Attribute-Value Model, vertical database model and open schema), is one where the Attribute and Value pair to describe one attribute of a given entity. Often used where the number of attributes could be limitless, but the number that will actually apply to a given entity is relatively modest. For example, a supermarket carries a limitless amount of products (that are being added, changed, updated, etc.). Each product (e.g. entity) might specify, size, weight, unit price, etc. (e.g. attributes) and each attribute may contain a price (e.g. value). From the limitless amount of potential products available, a customer may only purchase a modest bag of groceries.

Star Schema Database Model

In a Star Schema database model, data is organized into two categories:  Facts (events) surrounded by Dimensions (reference information about the Facts) that when diagrammed resemble the shape of a star. The relationships between Fact and Dimension tables are handled using keys. Each Dimension table is assigned a unique primary key. Each Fact table contains the unique primary key of each associated Dimension table and stores it as a foreign key. Considered the simplest database model, Star Schemas are commonly used in data marts and data warehouses.

Data - Byte By Byte

If you were not familiar with the data side of business analysis, you now have a lot to digest! Consider the information covered in this blog your appetizer portion. For the novice, data is best served one byte at time. Bon Appetite!

[hinditutorialspoint]

DBMS in Hindi - Specialization

DBMS in Hindi – Specialization

specialization in dbms, specialization kya hai, dbms specialization in hindi

DBMS Specialization in Hindi

specialization एक top-down दृष्टिकोण है, और यह generalization के विपरीत है। specialization में, एक higher level की इकाई को दो lower level की entities में तोड़ा जा सकता है।

specialization का उपयोग एक इकाई set के subset की पहचान करने के लिए किया जाता है जो कुछ विशिष्ठ विशेषताओ को साझा करता है।

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DBMS in Hindi - Generalization

DBMS in Hindi – Generalization

Generalization in hindi, generalization kya hoti hai, dbms generalization in hindi, generalization kya hai,

DBMS Generalization in Hindi

generalization एक bottom-up approach की तरह है जिसमें lower level की दो या दो से अधिक इकाइयां higher level की इकाई बनाने के लिए combine होती हैं यदि उनमें कुछ सामान्य विशेषताएँ हैं।

generalization में, higher level की एक इकाई भी lower level की entities  के साथ…

[ijtsrd]

Expermential Investagation and Analysis of Bitumen Modifaction Using Fly Ash

by Mohammad Ather Ul Jahangeer | Er. Pannu Ovais Manzoor | Er. Sajad Ahmad Mir "Expermential Investagation and Analysis of Bitumen Modifaction Using Fly Ash"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd33073.pdf

Paper Url :https://www.ijtsrd.com/engineering/transport-engineering/33073/expermential-investagation-and-analysis-of-bitumen-modifaction-using-fly-ash/mohammad-ather-ul-jahangeer

callforpapercommerce, ugcapprovedjournalsincommerce, commercejournal

The increase of crude oil prices in recent years resulted in an increase in bitumen prices as crude oil is origin for bitumen, asphalt and in other hand the fly ash from the power generating plants causes severe disposal problems. The main purpose of this project is to study the possibility of using fly ash as mineral filler in Bituminous paving mixes. Fly ash, a coal combustion product once treated as waste and disposed in landfills, is used today in substructure and road works. The research of fly ash properties may solve the problems of treatment and intelligent use of this residual material. This Marshall stability test is employed to determine the properties like stability, flow value, air voids, voids in mineral aggregate VMA , voids filled with bitumen VFB for a Dense Bituminous Macadam DBM mix. The experimental work is carried out by using specifications from MORTH Ministry of road transports and highways, specifications for road and bridge works,5th revision .By replacing the stone dust with fly ash at the levels 4 , 6 and 18 the results are compared. The variation of properties, optimum bitumen and fly ash contents are evaluated. It is observed that the mixes with fly ash as filler not differ much in properties when compared with control mix and satisfy desired criteria specified by a much higher margin. Hence, it has been recommended to utilize fly ash wherever available, not only reducing the cost of execution, but also partly solve the fly ash utilization and disposal problems.