Introduction to persistency and Berkeley DB
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This document discusses different data models for databases including hierarchical, relational, object-oriented, NoSQL, and object-relational models. It also covers the differences between client-server and embedded databases as well as introducing BerkeleyDB as an open source, embedded, non-relational database management system.
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data base system to new data science lerne
Databases are organized collections of data that allow for efficient data access and management. There are different types of databases including relational databases, NoSQL databases, object-oriented databases, and graph databases. Databases have evolved over time from flat file systems to hierarchical, network, relational, and modern cloud-based systems. A database management system provides tools for creating, accessing, and managing databases and ensures security, integrity, and consistency of stored data.
NOSQL Databases types and Uses
The four categories of NoSQL databases
When to Use NoSQL
When NOT to use NoSQL
Use cases NoSQL (Each Category)
NoSQL Databases
NoSQL databases were developed to handle large volumes of data and optimize for read/write operations better than relational databases. Popular NoSQL databases include MongoDB, Cassandra, Redis, and Neo4j. MongoDB is a document database where data is stored in collections as documents with dynamic schemas. Redis is an in-memory key-value store that also supports data structures like lists, sets, and hashes. Both MongoDB and Redis can scale horizontally and offer high availability through replication.
Unit-10.pptx
This document discusses emerging trends in databases, including NoSQL databases and object-oriented databases. It provides information on the characteristics, categories, advantages, and disadvantages of NoSQL databases. It also compares relational databases to object-oriented databases and discusses object-relational mapping.
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The lecture covers several key topics in database systems including:
1. An overview of database concepts such as data models, normalization, data integrity restrictions, query optimization and processing, and SQL.
2. Parallel processing of data and recovery methods.
3. Database design and development including object-relational mapping technologies.
4. Distributed, parallel and heterogeneous databases including definitions and examples of each.
NoSQL Data Stores in Research and Practice - ICDE 2016 Tutorial - Extended Ve...
This document provides an overview of NoSQL data stores and techniques for scalable data management. It begins with an introduction to NoSQL and the motivations for using specialized data systems instead of traditional relational databases. It then covers the four main classes of NoSQL databases - key-value stores, wide-column stores, document stores, and graph databases. The document also discusses the CAP theorem and its implications, as well as common techniques like sharding, replication, and query processing that NoSQL databases employ to achieve scalability and high availability. The goal is to help readers understand how to approach decisions around which database system may be best for their needs and requirements.
Presentation
This document summarizes three papers on keyword search over structured databases using an interpretative approach. The first paper discusses building an efficient index table to map keywords to row and column identifiers in the database. The second paper presents a general algorithm with two steps - a publication step to pre-compute indexing, and a search step to lookup keywords and generate SQL queries. The third paper introduces the concept of intrinsic and contextual weights to model the dependency between query keywords and generate a ranked list of query interpretations.
DATABASE MANAGEMENT SYSTEM
A database management system (DBMS) is a collection of programs that enables users to create and maintain databases and control all access to them. The primary goal of a DBMS is to provide an environment that is both convenient and efficient for users to retrieve and store information.
NO SQL Databases, Big Data and the cloud
1) NoSQL databases were developed to address problems with scaling relational databases (RDBMS) and fitting certain types of data and use cases.
2) NoSQL databases are non-relational and come in different types including key-value, wide column, document, and graph databases. They are designed for high scale, simplicity, and distribution across clusters.
3) The cloud allows for massive data analysis by providing unlimited scalability through mounting large compute clusters to process vast amounts of diverse data from various sources.
Nosql
The document provides an overview of NoSQL databases and MongoDB. It discusses:
- What NoSQL is and why it was created
- The different categories of NoSQL databases, including key-value stores, document databases, column family stores, and graph databases
- MongoDB specifically, including its flexible schema, horizontal scalability, replication support, and data modeling approach
- Comparisons between relational and NoSQL databases
Which no sql database
The document compares different NoSQL database types: key-value databases for fast access to large numbers of objects but limited querying; document databases for flexible schemas and querying within documents; columnar databases optimized for analytics on entire columns but no joins; and graph databases suited for interconnected data modeled as nodes and edges but difficult to scale for large generic graphs. The document provides examples, use cases, limitations, and databases for each type.
Nosql
The document provides an overview of NoSQL and MongoDB. It discusses that NoSQL databases were built for large datasets and cloud applications. It covers some of the main types of NoSQL databases like document stores, key-value stores, and column family stores. The document also compares NoSQL to SQL/relational databases, discussing how NoSQL is more flexible and scales horizontally. MongoDB is presented as a popular document-oriented NoSQL database, covering its flexible schema, horizontal scaling, and replication features.
Open Conceptual Data Models
The document discusses conceptual data models and how they can be realized through ontologies and linked data on the semantic web. It describes how conceptual models provide a more semantic description of a problem domain compared to logical or physical data models. RDF, RDFS and OWL allow conceptual models to be exposed as linked data, providing globally unique identifiers and enabling natural navigation between interconnected data items. This overcomes issues with traditional logical/relational models such as impedance mismatch and loss of semantics during development.
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This seminar provides an introduction to software engineering for the smart grid. It will be conducted as a graduate seminar with students leading discussions and presenting on research papers. The seminar topics will focus on areas related to smart grid software research. Assessments will include seminar preparation, presentation, and engagement as well as biweekly progress reports on an individual smart grid software project.
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The February meeting of the Kukui Cup/UH 2012 Organizing Committee covered the following topics:
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
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3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
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Giridhar Meka - https://www.linkedin.com/in/giridharmeka
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