eXTend DB. An embedded extensible document database. Extend with custom queries and object modifiers. Learn More ».
Morph DB. A Key-Value pair database. Allows fast in-place updates / object expansion. Learn More ».
Block Manager
An innovative library which manages on-disk blocks inside a file and provides a very simple interface to be used for variety of on-disk datastructures.
http://sscreation.net.in
This document analyzes the performance of MongoDB and HBase databases. It describes the architectures and key characteristics of each database, including MongoDB's document model, auto-sharding, and replication features. It also covers HBase's use of HDFS for storage and Zookeeper for coordination. The document examines the security features of each database, such as authentication, authorization, and encryption. Finally, it discusses findings from literature that NoSQL databases sacrifice ACID properties for scalability and performance.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed,
and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are
typically organized to model in a way that supports processes requiring information, such as modelling to
find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There
are many databases commonly, relational and non relational databases. Relational databases usually work
with structured data and non relational databases are work with semi structured data. In this paper, the
performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational
database and MongoDB is an example of non relational databases. A relational database is a data
structure that allows you to connect information from different 'tables', or different types of data buckets.
Non-relational database stores data without explicit and structured mechanisms to link data from different
buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of
Super Market Management System. A supermarket is a large form of the traditional grocery store also a
self-service shop offering a wide variety of food and household products, organized in systematic manner.
It is larger and has a open selection than a traditional grocery store.
MongoDB is an open-source document database that provides high performance, high availability, and automatic scaling. It stores data in flexible, JSON-like documents, enabling storage of data with complex relationships easily and supporting polyglot persistence. MongoDB can be used for applications such as content management systems, user profiles, logs, and more. It provides indexing, replication, load balancing and aggregation capabilities.
This document provides an overview of document databases and MongoDB. It discusses key concepts of document databases like dynamic schemas, embedding of related data, and lack of joins. Benefits include scalability, flexibility in data modeling, and performance. The document outlines MongoDB internals such as replication, sharding, and BSON data storage format. It also promotes MongoDB as the most popular open-source document database and provides links for additional .NET resources.
Annotating search results from web databases-IEEE Transaction Paper 2013Yadhu Kiran
Abstract—An increasing number of databases have become web accessible through HTML form-based search interfaces. The data units returned from the underlying database are usually encoded into the result pages dynamically for human browsing. For the encoded data units to be machine processable, which is essential for many applications such as deep web data collection and Internet comparison shopping, they need to be extracted out and assigned meaningful labels. In this paper, we present an automatic
annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. Then, for each group we annotate it from different aspects and aggregate the different annotations to predict a final annotation label for it. An annotation wrapper for the search site is automatically constructed and can be used to annotate new result pages from the same web database. Our experiments indicate that the proposed approach is highly effective.
Hands on Big Data Analysis with MongoDB - Cloud Expo Bootcamp NYCLaura Ventura
One of the most popular NoSQL databases, MongoDB is one of the building blocks for big data analysis. MongoDB can store unstructured data and makes it easy to analyze files by commonly available tools. This session will go over how big data analytics can improve sales outcomes in identifying users with a propensity to buy by processing information from social networks. All attendees will have a MongoDB instance on a public cloud, plus sample code to run Big Data Analytics.
Data mining model for the data retrieval from central server configurationijcsit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most
relevant and updated for continuous text search queries. This paper focuses on handling continuous text
extraction sustaining high document traffic. The main objective is to retrieve recent updated documents
that are most relevant to the query by applying sliding window technique. Our solution indexes the
streamed documents in the main memory with structure based on the principles of inverted file, and
processes document arrival and expiration events with incremental threshold-based method. It also ensures
elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are
ranked based on user feedback and given higher priority for retrieval.
This document discusses running SQL queries against MongoDB data using the MongoDB Connector for Business Intelligence (BI Connector) version 2.1. It provides an overview of the BI Connector's capabilities and improvements over version 1.0, demonstrates how to install and configure the necessary software, and shows log output when running a three table SQL query that is optimized by the BI Connector into a single MongoDB query.
This document analyzes the performance of MongoDB and HBase databases. It describes the architectures and key characteristics of each database, including MongoDB's document model, auto-sharding, and replication features. It also covers HBase's use of HDFS for storage and Zookeeper for coordination. The document examines the security features of each database, such as authentication, authorization, and encryption. Finally, it discusses findings from literature that NoSQL databases sacrifice ACID properties for scalability and performance.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed,
and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are
typically organized to model in a way that supports processes requiring information, such as modelling to
find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There
are many databases commonly, relational and non relational databases. Relational databases usually work
with structured data and non relational databases are work with semi structured data. In this paper, the
performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational
database and MongoDB is an example of non relational databases. A relational database is a data
structure that allows you to connect information from different 'tables', or different types of data buckets.
Non-relational database stores data without explicit and structured mechanisms to link data from different
buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of
Super Market Management System. A supermarket is a large form of the traditional grocery store also a
self-service shop offering a wide variety of food and household products, organized in systematic manner.
It is larger and has a open selection than a traditional grocery store.
MongoDB is an open-source document database that provides high performance, high availability, and automatic scaling. It stores data in flexible, JSON-like documents, enabling storage of data with complex relationships easily and supporting polyglot persistence. MongoDB can be used for applications such as content management systems, user profiles, logs, and more. It provides indexing, replication, load balancing and aggregation capabilities.
This document provides an overview of document databases and MongoDB. It discusses key concepts of document databases like dynamic schemas, embedding of related data, and lack of joins. Benefits include scalability, flexibility in data modeling, and performance. The document outlines MongoDB internals such as replication, sharding, and BSON data storage format. It also promotes MongoDB as the most popular open-source document database and provides links for additional .NET resources.
Annotating search results from web databases-IEEE Transaction Paper 2013Yadhu Kiran
Abstract—An increasing number of databases have become web accessible through HTML form-based search interfaces. The data units returned from the underlying database are usually encoded into the result pages dynamically for human browsing. For the encoded data units to be machine processable, which is essential for many applications such as deep web data collection and Internet comparison shopping, they need to be extracted out and assigned meaningful labels. In this paper, we present an automatic
annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. Then, for each group we annotate it from different aspects and aggregate the different annotations to predict a final annotation label for it. An annotation wrapper for the search site is automatically constructed and can be used to annotate new result pages from the same web database. Our experiments indicate that the proposed approach is highly effective.
Hands on Big Data Analysis with MongoDB - Cloud Expo Bootcamp NYCLaura Ventura
One of the most popular NoSQL databases, MongoDB is one of the building blocks for big data analysis. MongoDB can store unstructured data and makes it easy to analyze files by commonly available tools. This session will go over how big data analytics can improve sales outcomes in identifying users with a propensity to buy by processing information from social networks. All attendees will have a MongoDB instance on a public cloud, plus sample code to run Big Data Analytics.
Data mining model for the data retrieval from central server configurationijcsit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most
relevant and updated for continuous text search queries. This paper focuses on handling continuous text
extraction sustaining high document traffic. The main objective is to retrieve recent updated documents
that are most relevant to the query by applying sliding window technique. Our solution indexes the
streamed documents in the main memory with structure based on the principles of inverted file, and
processes document arrival and expiration events with incremental threshold-based method. It also ensures
elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are
ranked based on user feedback and given higher priority for retrieval.
This document discusses running SQL queries against MongoDB data using the MongoDB Connector for Business Intelligence (BI Connector) version 2.1. It provides an overview of the BI Connector's capabilities and improvements over version 1.0, demonstrates how to install and configure the necessary software, and shows log output when running a three table SQL query that is optimized by the BI Connector into a single MongoDB query.
Applied Semantic Search with Microsoft SQL ServerMark Tabladillo
Text mining is projected to dominate data mining, and the reasons are evident: we have more text available than numeric data. Microsoft introduced a new technology to SQL Server 2012 called Semantic Search. This session's detailed description and demos give you important information for the enterprise implementation of Tag Index and Document Similarity Index. The demos include a web-based Silverlight application, and content documents from Wikipedia. We'll also look at strategy tips for how to best leverage the new semantic technology with existing Microsoft data mining.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
This document presents a framework that migrates data from MySQL to NoSQL databases like MongoDB and HBase, and maps MySQL queries to queries in the NoSQL databases. The framework consists of a front-end GUI and modules for migrating data between the databases and mapping queries. It migrates data from MySQL tables to collections in MongoDB and HBase. When a user enters a MySQL query, a decision maker selects the target database and the query is mapped to that database's format to retrieve the data. The mapping time for various query types is measured to be very small, making query execution on NoSQL databases efficient using this framework.
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
1. The document discusses using MongoDB and data lakes for enterprise data management. It outlines the current issues with relational databases and how MongoDB addresses challenges like flexibility, scalability and performance.
2. Various architectures for enterprise data management with MongoDB are presented, including using it for raw, transformed and aggregated data stores.
3. The benefits of combining MongoDB and Hadoop in a data lake are greater agility, insight from handling different data structures, scalability and low latency for real-time decisions.
The document provides an overview of database management systems and the relational model. It discusses key concepts such as:
- The structure of relational databases using relations, attributes, tuples, domains, and relation schemas.
- Entity-relationship modeling and the relational algebra operations used to manipulate relational data, including selection, projection, join, and set operations.
- Additional relational concepts like primary keys, foreign keys, and database normalization to reduce data redundancy and inconsistencies.
The summary captures the main topics and essential information about database systems and the relational model covered in the document in 3 sentences.
The document discusses HTTP and how it facilitates data transfer on the World Wide Web. It describes how HTTP client-server interactions involve exchanging HTTP request and response messages to transfer web page objects like HTML files and images. It explains the process of transferring a web page that consists of multiple objects using non-persistent and persistent TCP connections. It also provides examples of HTTP request and response message formats.
The document discusses the ADO Data Control which provides access to data in databases through OLE DB. It describes adding an ADO Data Control to a project, connecting it to a database by building a connection string, setting the RecordSource property to a table or SQL, and creating bound controls to display fields from the RecordSource.
Vision Based Deep Web data Extraction on Nested Query Result RecordsIJMER
This document summarizes a research paper on vision-based deep web data extraction from nested query result records. It proposes a technique to extract data from web pages using different font styles, sizes, and cascading style sheets. The extracted data is then aligned into a table using alignment algorithms, including pair-wise, holistic, and nested-structure alignment. The goal is to remove immaterial information from query result pages to facilitate analysis of the extracted data.
IRJET- Data Retrieval using Master Resource Description FrameworkIRJET Journal
This document discusses using a Master Resource Description Framework (MRDF) to improve data retrieval efficiency from databases. The MRDF combines multiple RDF files into a single framework to reduce the time needed for search engines to query each individual RDF file. It also describes using a user profile to track user interests and tailor query results accordingly for a personalized search experience. The MRDF approach is presented as improving search efficiency while retrieving data from databases.
Semantic search in SQL Server 2012 improves search accuracy by understanding search intent and contextual meaning. It is built on full-text search and requires a predefined external database containing language statistics that is attached to the SQL Server instance and configured for semantic search. Semantic search functions return statistically significant phrases, similar documents, and key phrases explaining document similarities.
Using Page Size for Controlling Duplicate Query Results in Semantic WebIJwest
Semantic web is a web of future. The Resource Description Framework (RDF) is a language
to represent resources in the World Wide Web. When these resources are queried the problem of duplicate
query results occurs. The present techniques used hash index comparison to remove duplicate query
results. The major drawback of using the hash index to remove duplicate query results is that, if there is a
slight change in formatting or word order, then hash index is changed and query results are no more
considered as duplicate even though they have same contents. We presented an algorithm for detection and
elimination of duplicate query results from semantic web using hash index and page size comparisons.
Experimental results showed that the proposed technique removed duplicate query results from semantic
web efficiently, solved the problems of using hash index for duplicate handling and could be embedded in
existing SQL-Based query system for semantic web. Research could be carried out for certain flexibilities
in existing SQL-Based query system of semantic web to accommodate other duplicate detection techniques
as well.
This document provides answers to common ASP.NET interview questions. It begins with questions about the differences between custom controls and user controls, ASP session state and ASP.NET session state, and datasets versus recordsets in ADO.NET. Subsequent questions cover topics like view state, authentication, caching, validation controls, and working with data controls.
Apache Spark and MongoDB - Turning Analytics into Real-Time ActionJoão Gabriel Lima
This document discusses combining Apache Spark and MongoDB for real-time analytics. It provides an overview of MongoDB's native analytics capabilities including querying, data aggregation, and indexing. It then discusses how Apache Spark can extend these capabilities by providing additional analytics functions like machine learning, SQL queries, and streaming. Combining Spark and MongoDB allows organizations to perform real-time analytics on operational data without needing separate analytics infrastructure.
Rapid Development and Performance By Transitioning from RDBMSs to MongoDB
Modern day application requirements demand rich & dynamic data structures, fast response times, easy scaling, and low TCO to match the rapidly changing customer & business requirements plus the powerful programming languages used in today's software landscape.
Traditional approaches to solutions development with RDBMSs increasingly expose the gap between the modern development languages and the relational data model, and between scaling up vs. scaling horizontally on commodity hardware. Development time is wasted as the bulk of the work has shifted from adding business features to struggling with the RDBMSs.
MongoDB, the premier NoSQL database, offers a flexible and scalable solution to focus on quickly adding business value again.
In this session, we will provide:
- Overview of MongoDB's capabilities
- Code-level exploration of the MongoDB programming model and APIs and how they transform the way developers interact with a database
- Update of the exciting features in MongoDB 3.0
Object Relational Mapping with LINQ To SQLShahriar Hyder
OR Impedance Mismatch
Object Relational Mapping
The LINQ Project
Data Access In APIs Today
Data Access with DLINQ
DLinq For Relational Data
Architecture
Key Takeaways
Querying For Objects
When to Use LINQ to SQL?
RELEVANT UPDATED DATA RETRIEVAL ARCHITECTURAL MODEL FOR CONTINUOUS TEXT EXTRA...csandit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most relevant and updated for continuous text search queries. This paper focuses on handling continuous text extraction sustaining high document traffic. The main objective is to
retrieve recent updated documents that are most relevant to the query by applying sliding window technique. Our solution indexes the streamed documents in the main memory with structure based on the principles of inverted file, and processes document arrival and expiration
events with incremental threshold-based method. It also ensures elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are ranked based on user feedback and given higher priority for retrieval.
Relevant updated data retrieval architectural model for continous text extrac...csandit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that
are most relevant and updated for continuous text search queries. This paper focuses on
handling continuous text extraction sustaining high document traffic. The main objective is to
retrieve recent updated documents that are most relevant to the query by applying sliding
window technique. Our solution indexes the streamed documents in the main memory with
structure based on the principles of inverted file, and processes document arrival and expiration
events with incremental threshold-based method. It also ensures elimination of duplicate
document retrieval using unsupervised duplicate detection. The documents are ranked based on
user feedback and given higher priority for retrieval.
This document provides an introduction to MongoDB, a non-relational NoSQL database. It discusses what NoSQL databases are and their benefits compared to SQL databases, such as being more scalable and able to handle large, changing datasets. It then describes key features of MongoDB like high performance, rich querying, and horizontal scalability. The document outlines concepts like document structure, collections, and CRUD operations in MongoDB. It also covers topics such as replication, sharding, and installing MongoDB.
1) The document discusses the features and advantages of the non-relational MongoDB database compared to relational databases like MySQL. It focuses on MongoDB's flexibility, scalability, auto-sharding, and replication capabilities that make it more suitable than MySQL for big data applications.
2) MongoDB stores data as JSON-like documents with dynamic schemas rather than tables with rigid schemas. It allows embedding of related data and does not require joins. This improves performance over relational databases.
3) The key advantages of MongoDB are its flexible data model, horizontal scalability, high performance, and rich query capabilities. It is commonly used for big data, mobile and social applications, and as a data hub.
Applied Semantic Search with Microsoft SQL ServerMark Tabladillo
Text mining is projected to dominate data mining, and the reasons are evident: we have more text available than numeric data. Microsoft introduced a new technology to SQL Server 2012 called Semantic Search. This session's detailed description and demos give you important information for the enterprise implementation of Tag Index and Document Similarity Index. The demos include a web-based Silverlight application, and content documents from Wikipedia. We'll also look at strategy tips for how to best leverage the new semantic technology with existing Microsoft data mining.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
This document presents a framework that migrates data from MySQL to NoSQL databases like MongoDB and HBase, and maps MySQL queries to queries in the NoSQL databases. The framework consists of a front-end GUI and modules for migrating data between the databases and mapping queries. It migrates data from MySQL tables to collections in MongoDB and HBase. When a user enters a MySQL query, a decision maker selects the target database and the query is mapped to that database's format to retrieve the data. The mapping time for various query types is measured to be very small, making query execution on NoSQL databases efficient using this framework.
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
1. The document discusses using MongoDB and data lakes for enterprise data management. It outlines the current issues with relational databases and how MongoDB addresses challenges like flexibility, scalability and performance.
2. Various architectures for enterprise data management with MongoDB are presented, including using it for raw, transformed and aggregated data stores.
3. The benefits of combining MongoDB and Hadoop in a data lake are greater agility, insight from handling different data structures, scalability and low latency for real-time decisions.
The document provides an overview of database management systems and the relational model. It discusses key concepts such as:
- The structure of relational databases using relations, attributes, tuples, domains, and relation schemas.
- Entity-relationship modeling and the relational algebra operations used to manipulate relational data, including selection, projection, join, and set operations.
- Additional relational concepts like primary keys, foreign keys, and database normalization to reduce data redundancy and inconsistencies.
The summary captures the main topics and essential information about database systems and the relational model covered in the document in 3 sentences.
The document discusses HTTP and how it facilitates data transfer on the World Wide Web. It describes how HTTP client-server interactions involve exchanging HTTP request and response messages to transfer web page objects like HTML files and images. It explains the process of transferring a web page that consists of multiple objects using non-persistent and persistent TCP connections. It also provides examples of HTTP request and response message formats.
The document discusses the ADO Data Control which provides access to data in databases through OLE DB. It describes adding an ADO Data Control to a project, connecting it to a database by building a connection string, setting the RecordSource property to a table or SQL, and creating bound controls to display fields from the RecordSource.
Vision Based Deep Web data Extraction on Nested Query Result RecordsIJMER
This document summarizes a research paper on vision-based deep web data extraction from nested query result records. It proposes a technique to extract data from web pages using different font styles, sizes, and cascading style sheets. The extracted data is then aligned into a table using alignment algorithms, including pair-wise, holistic, and nested-structure alignment. The goal is to remove immaterial information from query result pages to facilitate analysis of the extracted data.
IRJET- Data Retrieval using Master Resource Description FrameworkIRJET Journal
This document discusses using a Master Resource Description Framework (MRDF) to improve data retrieval efficiency from databases. The MRDF combines multiple RDF files into a single framework to reduce the time needed for search engines to query each individual RDF file. It also describes using a user profile to track user interests and tailor query results accordingly for a personalized search experience. The MRDF approach is presented as improving search efficiency while retrieving data from databases.
Semantic search in SQL Server 2012 improves search accuracy by understanding search intent and contextual meaning. It is built on full-text search and requires a predefined external database containing language statistics that is attached to the SQL Server instance and configured for semantic search. Semantic search functions return statistically significant phrases, similar documents, and key phrases explaining document similarities.
Using Page Size for Controlling Duplicate Query Results in Semantic WebIJwest
Semantic web is a web of future. The Resource Description Framework (RDF) is a language
to represent resources in the World Wide Web. When these resources are queried the problem of duplicate
query results occurs. The present techniques used hash index comparison to remove duplicate query
results. The major drawback of using the hash index to remove duplicate query results is that, if there is a
slight change in formatting or word order, then hash index is changed and query results are no more
considered as duplicate even though they have same contents. We presented an algorithm for detection and
elimination of duplicate query results from semantic web using hash index and page size comparisons.
Experimental results showed that the proposed technique removed duplicate query results from semantic
web efficiently, solved the problems of using hash index for duplicate handling and could be embedded in
existing SQL-Based query system for semantic web. Research could be carried out for certain flexibilities
in existing SQL-Based query system of semantic web to accommodate other duplicate detection techniques
as well.
This document provides answers to common ASP.NET interview questions. It begins with questions about the differences between custom controls and user controls, ASP session state and ASP.NET session state, and datasets versus recordsets in ADO.NET. Subsequent questions cover topics like view state, authentication, caching, validation controls, and working with data controls.
Apache Spark and MongoDB - Turning Analytics into Real-Time ActionJoão Gabriel Lima
This document discusses combining Apache Spark and MongoDB for real-time analytics. It provides an overview of MongoDB's native analytics capabilities including querying, data aggregation, and indexing. It then discusses how Apache Spark can extend these capabilities by providing additional analytics functions like machine learning, SQL queries, and streaming. Combining Spark and MongoDB allows organizations to perform real-time analytics on operational data without needing separate analytics infrastructure.
Rapid Development and Performance By Transitioning from RDBMSs to MongoDB
Modern day application requirements demand rich & dynamic data structures, fast response times, easy scaling, and low TCO to match the rapidly changing customer & business requirements plus the powerful programming languages used in today's software landscape.
Traditional approaches to solutions development with RDBMSs increasingly expose the gap between the modern development languages and the relational data model, and between scaling up vs. scaling horizontally on commodity hardware. Development time is wasted as the bulk of the work has shifted from adding business features to struggling with the RDBMSs.
MongoDB, the premier NoSQL database, offers a flexible and scalable solution to focus on quickly adding business value again.
In this session, we will provide:
- Overview of MongoDB's capabilities
- Code-level exploration of the MongoDB programming model and APIs and how they transform the way developers interact with a database
- Update of the exciting features in MongoDB 3.0
Object Relational Mapping with LINQ To SQLShahriar Hyder
OR Impedance Mismatch
Object Relational Mapping
The LINQ Project
Data Access In APIs Today
Data Access with DLINQ
DLinq For Relational Data
Architecture
Key Takeaways
Querying For Objects
When to Use LINQ to SQL?
RELEVANT UPDATED DATA RETRIEVAL ARCHITECTURAL MODEL FOR CONTINUOUS TEXT EXTRA...csandit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most relevant and updated for continuous text search queries. This paper focuses on handling continuous text extraction sustaining high document traffic. The main objective is to
retrieve recent updated documents that are most relevant to the query by applying sliding window technique. Our solution indexes the streamed documents in the main memory with structure based on the principles of inverted file, and processes document arrival and expiration
events with incremental threshold-based method. It also ensures elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are ranked based on user feedback and given higher priority for retrieval.
Relevant updated data retrieval architectural model for continous text extrac...csandit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that
are most relevant and updated for continuous text search queries. This paper focuses on
handling continuous text extraction sustaining high document traffic. The main objective is to
retrieve recent updated documents that are most relevant to the query by applying sliding
window technique. Our solution indexes the streamed documents in the main memory with
structure based on the principles of inverted file, and processes document arrival and expiration
events with incremental threshold-based method. It also ensures elimination of duplicate
document retrieval using unsupervised duplicate detection. The documents are ranked based on
user feedback and given higher priority for retrieval.
This document provides an introduction to MongoDB, a non-relational NoSQL database. It discusses what NoSQL databases are and their benefits compared to SQL databases, such as being more scalable and able to handle large, changing datasets. It then describes key features of MongoDB like high performance, rich querying, and horizontal scalability. The document outlines concepts like document structure, collections, and CRUD operations in MongoDB. It also covers topics such as replication, sharding, and installing MongoDB.
1) The document discusses the features and advantages of the non-relational MongoDB database compared to relational databases like MySQL. It focuses on MongoDB's flexibility, scalability, auto-sharding, and replication capabilities that make it more suitable than MySQL for big data applications.
2) MongoDB stores data as JSON-like documents with dynamic schemas rather than tables with rigid schemas. It allows embedding of related data and does not require joins. This improves performance over relational databases.
3) The key advantages of MongoDB are its flexible data model, horizontal scalability, high performance, and rich query capabilities. It is commonly used for big data, mobile and social applications, and as a data hub.
This paper trying to focus on main features, advantages and applications of non-relational database namely Mongo DB and thus justifying why MongoDB is more suitable than relational databases in big data applications. The database used here for comparison with MongoDB is MySQL. The main features of MongoDB are flexibility, scalability, auto sharding and replication. MongoDB is used in big data and real time web applications since it is a leading database technology.
This document provides information about MongoDB, including:
- MongoDB is a cross-platform document-oriented database that provides high performance, high availability, and easy scalability.
- Data is stored in MongoDB in the form of JSON-like documents with dynamic schemas, instead of using fixed table schemas as in SQL-based databases.
- Relationships between documents can be modeled either by embedding one document inside another or by storing references between separate documents.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed, and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are typically organized to model in a way that supports processes requiring information, such as modelling to find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There are many databases commonly, relational and non relational databases. Relational databases usually work with structured data and non relational databases are work with semi structured data. In this paper, the performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational database and MongoDB is an example of non relational databases. A relational database is a data structure that allows you to connect information from different 'tables', or different types of data buckets. Non-relational database stores data without explicit and structured mechanisms to link data from different buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of Super Market Management System. A supermarket is a large form of the traditional grocery store also a self-service shop offering a wide variety of food and household products, organized in systematic manner. It is larger and has a open selection than a traditional grocery store.
MONGODB VS MYSQL: A COMPARATIVE STUDY OF PERFORMANCE IN SUPER MARKET MANAGEME...ijcsity
A database is information collection that is organized in tables so that it can easily be accessed, managed,
and updated. It is the collection of tables, schemas, queries, reports, views and other objects. The data are
typically organized to model in a way that supports processes requiring information, such as modelling to
find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies. There
are many databases commonly, relational and non relational databases. Relational databases usually work
with structured data and non relational databases are work with semi structured data. In this paper, the
performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational
database and MongoDB is an example of non relational databases. A relational database is a data
structure that allows you to connect information from different 'tables', or different types of data buckets.
Non-relational database stores data without explicit and structured mechanisms to link data from different
buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of
Super Market Management System. A supermarket is a large form of the traditional grocery store also a
self-service shop offering a wide variety of food and household products, organized in systematic manner.
It is larger and has a open selection than a traditional grocery store.
This is an introduction about the MongoDB. It includes basic MongoQueries. Not a advance level of presentation but provide nice information for the starters
Introduction to MongoDB and its best practicesAshishRathore72
This document provides a summary of a presentation on MongoDB best practices. It discusses MongoDB concepts like data modeling, CRUD operations, querying, and aggregation. It also covers topics like MongoDB security, scaling options, real-world use cases, and best practices for hardware, schema design, indexing, and scalability. The presentation provides an overview of using MongoDB effectively.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
This document discusses the basics of database management systems (DBMS). It begins by explaining the data storage hierarchy from the bit level up to the database level. It then covers different database models including hierarchical, network, relational, and object-oriented. Key components of a DBMS like DDL, DML, query language, and report generators are defined. Commercial DBMS examples are provided. The document concludes with an overview of creating and using a database, including defining the structure, entering data, and searching for information.
Spring Data provides a unified model for data access and management across different data access technologies such as relational, non-relational and cloud data stores. It includes utilities such as repository support, object mapping and templating to simplify data access layers. Spring Data MongoDB provides specific support for MongoDB including configuration, mapping, querying and integration with Spring MVC. It simplifies MongoDB access through MongoTemplate and provides a repository abstraction layer.
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3.Implementation with NOSQL databases Document Databases (Mongodb).pptxRushikeshChikane2
this Chapter gives information about Document Based Database and Graph based Database. It gives their basic structures, Features,applications ,Limitations and use cases
MongoDB is an open-source, cross-platform document-oriented database written in C++. It provides high performance, high availability, and automatic scaling. MongoDB stores data as documents with dynamic schemas, making it flexible and suitable for big data and real-time applications. It supports features like ad-hoc queries, indexing, replication, sharding, and map-reduce for aggregation.
Everything You Need to Know About MongoDB Development.pptx75waytechnologies
Today, organizations from different verticals want to harness the power of data to grab new business opportunities and touch new heights of success. Such an urge leads them to follow unique ways to use and handle data effectively. After all, the right use of data boosts the ability to make business decisions faster. But at the same time, working with data is not as easy as a walk in the garden. It sometimes turns out to be a long-standing problem for businesses that also affects their overall functioning.
Companies expect fast phase development and better data management in every scenario. Modern web-based applications development demands a quality working system that can be deployed faster, and the application is able to scale in the future as per the constantly changing environment.
Earlier, relational databases were used as a primary data store for web application development. But today, developers show a high interest in adopting alternative data stores for modern applications such as NoSQL (Not Only Structured Query Language) because of its incredible benefits. And if you ask us, one of the technologies that can do wonders in modern web-based application development is MongoDB.
MongoDB is the first name strike in our heads when developing scalable applications with evolving data schemas. Because MongoDB is a document database, it makes it easier for developers to store both structured and unstructured data. Stores and handles large amounts of data quickly, MongoDB is undoubtedly the smart move toward building scalable and data-driven applications. If you’re wondering what MongoDB is and how it can help your digital success, this blog is surely for you.
how_can_businesses_address_storage_issues_using_mongodb.pdfsarah david
MongoDB is an open-source database that can help businesses address storage issues. It provides scalability, availability, and handles large amounts of data well. MongoDB uses a flexible document data model and has features like replication, sharding, and indexing that improve performance. While it has advantages like flexibility, simplicity, and speed, it also has drawbacks like limited transactions and joins compared to relational databases. Understanding both the benefits and limitations of MongoDB is important for businesses evaluating it for their data storage needs.
how_can_businesses_address_storage_issues_using_mongodb.pptxsarah david
MongoDB enables seamless data storage and performance. Explore our blog to learn how MongoDB handles storage issues for startups and large-scale enterprises. Discover how to optimize MongoDB performance using open-source database storage.
This presentation is related to nosql database and nosql database types information. this presentationa also contains discussion about, how mongodb works and mongodb security and mongodb sharding information.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
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UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
2. Motivation
Example Usecases
eXTend DB.
Design
Extensibility
Limitations
Morph DB – KeyValue pair
Design and implementation
Block management design
implementation
Caches
Unique Approach towards the Database
(c) Copy right 2013 contact extenddb@gmail.com
3. No SQL document Database like mongo steadily becoming
popular.
Mongo DB features suitable for wide variety of applications
over traditional sql databases
JSON-style documents with dynamic schemas offer simplicity
and power.
Rich, document-based queries.
Index on any attribute.
Fast in-place updates and atomic modifiers.
Features like replication , sharding , High availability , Map
reduce etc. are not applicable in this context.
(c) Copy right 2013 contact extenddb@gmail.com
4. Features mentioned previously are also applicable for stand-alone
applications installed/running on user machines.
There are few problems in using Mongo DB in such applications.
External dependency on Mongo DB .
User needs to install it separately.
User has to manage Mongo DB for the application to work.
Possibility of name space collision among different unrelated applications.
Unnecessary client-server communication impacts performance.
So there is need for an embedded (into application) document database
with similar features as Mongo DB. Basically sqlite equivalent of Mongo
DB.
An extensible database is a plus.
(c) Copy right 2013 contact extenddb@gmail.com
5. Logging library -
Each log file entry could be an object in the database.
Indexes could be created at later point in time to analyze log
files using rich querying.
File tagging application -
Each file information could be stored as an object to the DB.
With tags attached removed dynamically.
Indexed data could extend the object with new fields.
Querying / searching based on tags or indexed data.
(c) Copy right 2013 contact extenddb@gmail.com
6. Single node user-space NFS server –
Stores all metadata information into the database.
Maps filehandle to object/file attributes.
Objects accessed with filehandles and/or parent
file handle and name.
File data stored separately outside the database
using object-id based name space.
Any other stand-alone applications .
(c) Copy right 2013 contact extenddb@gmail.com
7. No SQL Document Database
Stores BSON documents
Embedded into process
Mongo DB like querying interface
Extensible
Each database collection is stored into set of
files in user specified directory.
(c) Copy right 2013 contact extenddb@gmail.com
8. Application
DataBase API
Query related Management api
Query
Optimizer
Extensible
Query Module
Storage Layer
Tokyo cabinet Morph DB
In-memory
Key value DB
(c) Copy right 2013 contact extenddb@gmail.com
9. Data is stored in 3 types of files backed up by storage
layer key value database.
Descriptor DB –
Holds information about the list of indexes in the database.
Main DB –
Stores all the document information with generated BSON
object ids as keys.
BSON object id uniquely identifies the object in the
collection.
Index DB –
Stores references of objects with particular field values as
key and list of object ids.
(c) Copy right 2013 contact extenddb@gmail.com
10. Simple weight based query optimizer.
Index with the least number of objects is
chosen.
(c) Copy right 2013 contact extenddb@gmail.com
11. Provides 2 functionalities for database engine
Given a query in bson object format returns a list
of indexes which can be used for the particular
query.
▪ This is in-turn used by query optimizer for finding the
best index to use.
Takes bson object and a query bson object returns
whether the object matches the query or not.
(c) Copy right 2013 contact extenddb@gmail.com
12. Query module implements comparison
operator between 2 bson elements.
Has no knowledge of storage layer , just
operates on the given bson objects.
Can be overridden by users by registering user
specified comparison operators.
This could be very useful for custom binary
data stored in database.
Different query operators are implemented in
the module for providing complex querying.
(c) Copy right 2013 contact extenddb@gmail.com
13. Operators let a object be selected in different ways other than just by
comparing the value is equal to the value in query.
E.g.
{‘a’:3} will match and all documents which has field a with value 3. This is a
simple query.
But if we want to get all objects whose values are greater than 3 we cant
accomplish this with simple query.
{‘a’:{‘$gt’:3}} is the query which will match all the documents where the value
is greater than 3.
Here operator ‘$gt’ is given meaning “greater than”.
Any field name starting with “$” is considered as an operator and the rest
of the name gives the name of the operator.
Querying function looks up for the operator in the registered list and
invokes the handler to check whether the field matches the criteria in
query.
By default various operators like $lt, $lte ,$nin, $all , $in, $exists have
been implemented.
(c) Copy right 2013 contact extenddb@gmail.com
14. Custom operators can be registered with the
query module.
When a particular query comes the
corresponding user call back will be invoked.
Call back takes value of the field as one
parameter and value of the query value as other
one and returns boolean.
This way query language of eXTend DB can be
extended without having the need to edit the
code of the database or wait for the developer to
implement the features.
(c) Copy right 2013 contact extenddb@gmail.com
15. Abstract layer which provides key-value storage.
Isolates data storage from the rest of the
database engine.
Only place where the data is stored.
Backend can be any key-value pair database.
E.g.
Tokyo cabinet
Morph DB
In memory key value pair
Currently tokyo-cabinet is the default key-value
pair backend which stores all the data to files.
Also Morph DB backend is almost complete.
(c) Copy right 2013 contact extenddb@gmail.com
16. Different backends can be chosen depending
the type of data stored.
E.g.
Index Databases can be stored completely in
memory which will provide fast access.
Main DB could be stored using tokyo cabinet back
end.
For persistent indexes Morph DB could be used .
(c) Copy right 2013 contact extenddb@gmail.com
17. Easy to use mongodb like embedded
database.
Extensible storage backends .
Extensible query language.
Completely customizable query behavior .
(c) Copy right 2013 contact extenddb@gmail.com
18. Tokyocabinet updates are not in-place
Every time the object is expanded old space in
file is discarded new space is found.
This is a serious problem for heavy update
workload.
Tokyo cabinet by default writes to memory need
to do sync to sync the data to file.
If application crashes without sync data is lost.
Sync calls are costly.
Incase sync gets called after every insert the
performance is very low.
(c) Copy right 2013 contact extenddb@gmail.com
19. Morph DB is a key value pair database aimed
at solving the limitations of tokyo cabinet.
Aims of Morph DB –
Fast in-place updates / object expansion.
A fast block management layer which could reuse
storage used by deleted objects.
Once written data read should not be slowed
down by block management layer.
Writes all data directly to the file while
maintaining performance.
(c) Copy right 2013 contact extenddb@gmail.com
20. B+ Tree implementation on top of block
management layer.
Provides generation based cursors.
Cursors can work while DB is being modified.
Can search for values in a range of keys.
(c) Copy right 2013 contact extenddb@gmail.com
21. Provides 2 basic functionalities
Data Write –
▪ Finds allocates resources in file
▪ Writes the data to suitable location(s).
▪ Returns an address where the data is written.
▪ Upper layer must store this reference to read the data back.
▪ Data is not interpreted.
Data Read –
▪ Given the address which was earlier returned by the Data
write reads data from the offset or links of offsets
▪ Verifies the checksum of each piece
▪ Returns stitched object to the caller.
(c) Copy right 2013 contact extenddb@gmail.com
22. File storage is managed in terms of resource
clusters.
Each resource cluster contains some header
information and the resources followed by it.
Unique property of resources is that it is of
variable size instead of a fixed single block size
like in various solutions.
Individual resources (block) size varies from 128
bytes to 4MB.
This range of block sizes makes it suitable for
data of various sizes from very small values to 16
MB.
(c) Copy right 2013 contact extenddb@gmail.com
23. Clusters are allocated on-demand for a particular
type of resource.
Cluster sizes start from 128K and subsequent
cluster sizes are double the previous one capped
by 32 MB.
Increasing cluster sizes makes the database file
size small initially and grows along with the data
size.
In case of small clusters header information
could be significant size compared to the
resource sizes.
(c) Copy right 2013 contact extenddb@gmail.com
24. Data is stored in list of blocks each stores reference to
next block in the list.
Each chunk stores the checksum of the entire data.
This helps in identifying corrupt or partially updated
links.
When data is expanded according to the expanding
data size suitable block is allocated and linked.
There is a cap on link counts there can be maximum 4
links.
Once data spreads across 4 links data is automatically
defragmented and a suitable block bigger is found for
the entire block which will reduce number of links.
(c) Copy right 2013 contact extenddb@gmail.com
25. Block allocation takes a block size parameter .
A free block of specified size found in the bitmap
residing in cluster header and the address is returned.
DiskAddr structure identifying resource is 64 bit , bit-
field structure.
56-bit component directly gives the address of the
resources .
So no translation of address in IO path.
4-bit type field indicates the resource size 0 for 128
and 1 for 256 and so on.
Type field helps identifying the resource when freeing.
(c) Copy right 2013 contact extenddb@gmail.com
26. Block allocation need to be extremely fast.
Caches used to remember last cluster from
which data was allocated cluster.
One such cache for each resource type.
Cache state makes allocation O(1) in case of
series of allocations.
Freeing resource will set the cache state to
point to the lowest offset resource.
Always search continues in the next clusters.
(c) Copy right 2013 contact extenddb@gmail.com
27. System calls (mostly pread/pwrite were used)
are very fast in some machines(core i3
processors). Doing large number of small
writes were not a problem.
In other machines (core 2 Duo) system calls
were significantly slower and huge percentage
of time was spent in system calls.
Memory mapped IOs were significantly faster.
(c) Copy right 2013 contact extenddb@gmail.com
28. Mapping entire file has few problems.
File sizes can grow
In 32-bit machines will limit the database size.
Unused regions could be mapped and kernel could choose to remove
wrong set of pages.
To avoid above draw backs list of mmapped blocks were used.
Number was limited by 10 to limit the virtual address usage.
Least recently used mmapped region is removed if new region is to
be mmapped.
Whenever a cluster is allocated whole cluster is mmapped.
For each IO this list is checked if it is a hit simple memcpy is done
or else fall back to old system call.
This improved the performance by almost 50 % in slow machines.
(c) Copy right 2013 contact extenddb@gmail.com
29. B+ Tree uses the block management layer to
store its internal nodes and data.
Block manager has no information about how
the blocks are going to be used.
Provides a slot for the upper layer to store a
reference to its superblock.
Internal nodes stores all keys of the nodes and
references to corresponding child nodes/values.
Parent pointer is not maintained on-disk this
makes the splitting of nodes fast.
Parent child relation ship is established during
search.
(c) Copy right 2013 contact extenddb@gmail.com
30. All the nodes being modified are in-memory.
Nodes are pinned in cache.
After each modification node is written back
to file.
(c) Copy right 2013 contact extenddb@gmail.com
31. Concurrent modifications can be allowed by taking write
lock on root of sub-tree which could be modified by
insert/delete.
An insert in B+Tree could modify few to all nodes in the
path from root to the leaf.
The highest level which will be modified could be found by
whether child could overrun by the insert.
If child is overrun then parent will be modified.
So instead of locking root we just need to lock the subtree
whose root is the top most parent which could be
modified.
Similar speculation could be done for deletes.
All the nodes from root to the first child which could be
modified will be locked for read.
(c) Copy right 2013 contact extenddb@gmail.com