Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
Migrate and Modernize Hadoop-Based Security Policies for DatabricksDatabricks
Data teams are faced with a variety of tasks when migrating Hadoop-based platforms to Databricks. A common pitfall happens during the migration step where often overlooked access control policies can block adoption. This session will focus on the best practices to migrate and modernize Hadoop-based policies to govern data access (such as those in Apache Ranger or Apache Sentry). Data architects must consider new, fine-grained access control requirements when migrating from Hadoop architectures to Databricks in order to deliver secure access to as many data sets and data consumers as possible. This session will provide guidance across open source, AWS, Azure and partner tools, such as Immuta, on how to scale existing Hadoop-based policies to dynamically support more classes of users, implement fine-grained access control and leverage automation to protect sensitive data while maximizing utility — without manual effort
OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.
The document discusses creating a modern data architecture using a data lake approach. It describes the key components of a data lake including different zones for landing raw data, refining it into trusted datasets, and using sandboxes. It also summarizes challenges of data lakes and how an integrated data lake management platform can help with ingestion, governance, security, and enabling self-service analytics. Finally, it briefly discusses considerations for implementing cloud-based and hybrid data lakes.
The document discusses evolving data warehousing strategies and architecture options for implementing a modern data warehousing environment. It begins by describing traditional data warehouses and their limitations, such as lack of timeliness, flexibility, quality, and findability of data. It then discusses how data warehouses are evolving to be more modern by handling all types and sources of data, providing real-time access and self-service capabilities for users, and utilizing technologies like Hadoop and the cloud. Key aspects of a modern data warehouse architecture include the integration of data lakes, machine learning, streaming data, and offering a variety of deployment options. The document also covers data lake objectives, challenges, and implementation options for storing and analyzing large amounts of diverse data sources.
Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
Migrate and Modernize Hadoop-Based Security Policies for DatabricksDatabricks
Data teams are faced with a variety of tasks when migrating Hadoop-based platforms to Databricks. A common pitfall happens during the migration step where often overlooked access control policies can block adoption. This session will focus on the best practices to migrate and modernize Hadoop-based policies to govern data access (such as those in Apache Ranger or Apache Sentry). Data architects must consider new, fine-grained access control requirements when migrating from Hadoop architectures to Databricks in order to deliver secure access to as many data sets and data consumers as possible. This session will provide guidance across open source, AWS, Azure and partner tools, such as Immuta, on how to scale existing Hadoop-based policies to dynamically support more classes of users, implement fine-grained access control and leverage automation to protect sensitive data while maximizing utility — without manual effort
OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.
The document discusses creating a modern data architecture using a data lake approach. It describes the key components of a data lake including different zones for landing raw data, refining it into trusted datasets, and using sandboxes. It also summarizes challenges of data lakes and how an integrated data lake management platform can help with ingestion, governance, security, and enabling self-service analytics. Finally, it briefly discusses considerations for implementing cloud-based and hybrid data lakes.
The document discusses evolving data warehousing strategies and architecture options for implementing a modern data warehousing environment. It begins by describing traditional data warehouses and their limitations, such as lack of timeliness, flexibility, quality, and findability of data. It then discusses how data warehouses are evolving to be more modern by handling all types and sources of data, providing real-time access and self-service capabilities for users, and utilizing technologies like Hadoop and the cloud. Key aspects of a modern data warehouse architecture include the integration of data lakes, machine learning, streaming data, and offering a variety of deployment options. The document also covers data lake objectives, challenges, and implementation options for storing and analyzing large amounts of diverse data sources.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
This document discusses Snowflake's data governance capabilities including challenges around data silos, complexity of data management, and balancing security and governance with data utilization. It provides an overview of Snowflake's platform for ingesting and sharing data across various sources and consumers. Key governance capabilities in Snowflake like object tagging, classification, anonymization, access history and row/column level policies are described. The document also previews upcoming conditional masking policies and provides examples of implementing object tagging and access policies in Snowflake.
Oracle Database is a collection of data treated as a unit. The purpose of a database is to store and retrieve related information. Oracle Database was started in 1977 as Software Development Laboratories by Larry Ellison and others. Over time, Oracle released several major versions that added new functionality, such as Oracle 12c which was designed for cloud computing. A database server is the key to solving problems of information management by allowing storage, retrieval, and manipulation of data.
ETL (Extract, Transform, Load) is a process that allows companies to consolidate data from multiple sources into a single target data store, such as a data warehouse. It involves extracting data from heterogeneous sources, transforming it to fit operational needs, and loading it into the target data store. ETL tools automate this process, allowing companies to access and analyze consolidated data for critical business decisions. Popular ETL tools include IBM Infosphere Datastage, Informatica, and Oracle Warehouse Builder.
This document outlines Pivotal's Greenplum roadmap. It discusses plans to enhance Greenplum's open source strategy by continuing to align with PostgreSQL functionality. It also describes strategies for multi-cloud support on AWS, Azure, Google Cloud and others as well as integration with Kubernetes. Near term focus areas include improving the query planner, adding resource groups and containerized Python/R support. The roadmap outlines enhancements through 2018 and a major release in 2019 incorporating additional PostgreSQL features while retaining MPP performance and scale. Long term initiatives include disaster recovery, foreign data and integrating new data types like spatial and time series data.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Data Lineage: Using Knowledge Graphs for Deeper Insights into Your Data Pipel...Neo4j
Data lineage tracks how data flows through an enterprise by identifying where data comes from, where it goes, and what happens along the way. It can be difficult to achieve due to differences between business and technical views of data, scope, level of detail needed, and changes within a company over time from mergers, migrations, and high rates of change. Data lineage use cases include governance and regulatory compliance by meeting commitments faster with less manual effort, accurately defining governance initiatives, and exposing previously unknown privacy exposures. MANTA software documents lineage by analyzing SQL, ETL and BI code to visualize lineage maps or integrate with third-party governance solutions.
This document provides an overview of DAX (Data Analysis Expressions) and how it can be used for data analysis in Power BI and Analysis Services Tabular models. It discusses key DAX concepts like calculated columns, calculated measures, and filter context. It also covers common DAX functions and how to work with dates in DAX. The document provides examples of how to define security and write DAX queries against the BI Semantic Model.
Hadoop World 2011: Replacing RDB/DW with Hadoop and Hive for Telco Big Data -...Cloudera, Inc.
The document discusses migrating KT's CDR analysis system from a relational database to NexR's Hadoop-based Data Analytics Platform (NDAP). NDAP provides tools to help with the migration, including converting Oracle data and SQL queries to the Hive query language. The conversion process involves mapping data types, functions, and SQL syntax between Oracle and Hive. NDAP also includes performance monitoring and query optimization tools to help enterprise data engineers adapt to the new system.
This document compares SQL and NoSQL databases. It defines databases, describes different types including relational and NoSQL, and explains key differences between SQL and NoSQL in areas like scaling, modeling, and query syntax. SQL databases are better suited for projects with logical related discrete data requirements and data integrity needs, while NoSQL is more ideal for projects with unrelated, evolving data where speed and scalability are important. MongoDB is provided as an example of a NoSQL database, and the CAP theorem is introduced to explain tradeoffs in distributed systems.
This document contains confidential information from Arocom Solutions Private Limited intended only for the addressee. It discusses an upcoming training on Data Analysis Expressions (DAX), Power Query, and Power BI. The agenda includes introductions, an overview of DAX and how it differs from Excel, common DAX functions, calculated columns and measures, and exercises. DAX is a functional formula language for data analysis within Excel data models that allows for aggregation, filtering, and time intelligence operations.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
The document compares ETL and ELT data integration processes. ETL extracts data from sources, transforms it, and loads it into a data warehouse. ELT loads extracted data directly into the data warehouse and performs transformations there. Key differences include that ETL is better for structured data and compliance, while ELT handles any size/type of data and transformations are more flexible but can slow queries. AWS Glue, Azure Data Factory, and SAP BODS are tools that support these processes.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
This document provides an introduction and overview of implementing Data Vault 2.0 on Snowflake. It begins with an agenda and the presenter's background. It then discusses why customers are asking for Data Vault and provides an overview of the Data Vault methodology including its core components of hubs, links, and satellites. The document applies Snowflake features like separation of workloads and agile warehouse scaling to support Data Vault implementations. It also addresses modeling semi-structured data and building virtual information marts using views.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
This presentation shows all the posible options to move Oracle BI on-premise system to Oracle Analytics Cloud. We are going to see all the steps to perform this migration as well as the issues that we have seen and how to troubleshoot them. In addition we will review the most common administration tasks.
The JavaScript InfoVis Toolkit provides open source interactive data visualization tools using JSON data formats, including area charts, sunbursts, hyper trees, space trees, and more to help companies visualize hierarchical and quantitative data, though some coding experience is required as it is a library rather than an application.
Stellmach.2011.designing gaze supported multimodal interactions for the explo...mrgazer
This document describes research into designing gaze-supported multimodal interactions for exploring large image collections. Specifically, it investigates combining eye gaze with a touch-and-tilt mobile device or keyboard to control an adaptive fisheye lens visualization. User interviews were conducted to understand how people would want to use such input combinations for browsing images. Based on this feedback, a prototype system was developed using a SpringLens technique with gaze and a touch device. A user study provided insights into how well the gaze-supported interaction techniques were experienced.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
This document discusses Snowflake's data governance capabilities including challenges around data silos, complexity of data management, and balancing security and governance with data utilization. It provides an overview of Snowflake's platform for ingesting and sharing data across various sources and consumers. Key governance capabilities in Snowflake like object tagging, classification, anonymization, access history and row/column level policies are described. The document also previews upcoming conditional masking policies and provides examples of implementing object tagging and access policies in Snowflake.
Oracle Database is a collection of data treated as a unit. The purpose of a database is to store and retrieve related information. Oracle Database was started in 1977 as Software Development Laboratories by Larry Ellison and others. Over time, Oracle released several major versions that added new functionality, such as Oracle 12c which was designed for cloud computing. A database server is the key to solving problems of information management by allowing storage, retrieval, and manipulation of data.
ETL (Extract, Transform, Load) is a process that allows companies to consolidate data from multiple sources into a single target data store, such as a data warehouse. It involves extracting data from heterogeneous sources, transforming it to fit operational needs, and loading it into the target data store. ETL tools automate this process, allowing companies to access and analyze consolidated data for critical business decisions. Popular ETL tools include IBM Infosphere Datastage, Informatica, and Oracle Warehouse Builder.
This document outlines Pivotal's Greenplum roadmap. It discusses plans to enhance Greenplum's open source strategy by continuing to align with PostgreSQL functionality. It also describes strategies for multi-cloud support on AWS, Azure, Google Cloud and others as well as integration with Kubernetes. Near term focus areas include improving the query planner, adding resource groups and containerized Python/R support. The roadmap outlines enhancements through 2018 and a major release in 2019 incorporating additional PostgreSQL features while retaining MPP performance and scale. Long term initiatives include disaster recovery, foreign data and integrating new data types like spatial and time series data.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Data Lineage: Using Knowledge Graphs for Deeper Insights into Your Data Pipel...Neo4j
Data lineage tracks how data flows through an enterprise by identifying where data comes from, where it goes, and what happens along the way. It can be difficult to achieve due to differences between business and technical views of data, scope, level of detail needed, and changes within a company over time from mergers, migrations, and high rates of change. Data lineage use cases include governance and regulatory compliance by meeting commitments faster with less manual effort, accurately defining governance initiatives, and exposing previously unknown privacy exposures. MANTA software documents lineage by analyzing SQL, ETL and BI code to visualize lineage maps or integrate with third-party governance solutions.
This document provides an overview of DAX (Data Analysis Expressions) and how it can be used for data analysis in Power BI and Analysis Services Tabular models. It discusses key DAX concepts like calculated columns, calculated measures, and filter context. It also covers common DAX functions and how to work with dates in DAX. The document provides examples of how to define security and write DAX queries against the BI Semantic Model.
Hadoop World 2011: Replacing RDB/DW with Hadoop and Hive for Telco Big Data -...Cloudera, Inc.
The document discusses migrating KT's CDR analysis system from a relational database to NexR's Hadoop-based Data Analytics Platform (NDAP). NDAP provides tools to help with the migration, including converting Oracle data and SQL queries to the Hive query language. The conversion process involves mapping data types, functions, and SQL syntax between Oracle and Hive. NDAP also includes performance monitoring and query optimization tools to help enterprise data engineers adapt to the new system.
This document compares SQL and NoSQL databases. It defines databases, describes different types including relational and NoSQL, and explains key differences between SQL and NoSQL in areas like scaling, modeling, and query syntax. SQL databases are better suited for projects with logical related discrete data requirements and data integrity needs, while NoSQL is more ideal for projects with unrelated, evolving data where speed and scalability are important. MongoDB is provided as an example of a NoSQL database, and the CAP theorem is introduced to explain tradeoffs in distributed systems.
This document contains confidential information from Arocom Solutions Private Limited intended only for the addressee. It discusses an upcoming training on Data Analysis Expressions (DAX), Power Query, and Power BI. The agenda includes introductions, an overview of DAX and how it differs from Excel, common DAX functions, calculated columns and measures, and exercises. DAX is a functional formula language for data analysis within Excel data models that allows for aggregation, filtering, and time intelligence operations.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
The document compares ETL and ELT data integration processes. ETL extracts data from sources, transforms it, and loads it into a data warehouse. ELT loads extracted data directly into the data warehouse and performs transformations there. Key differences include that ETL is better for structured data and compliance, while ELT handles any size/type of data and transformations are more flexible but can slow queries. AWS Glue, Azure Data Factory, and SAP BODS are tools that support these processes.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
This document provides an introduction and overview of implementing Data Vault 2.0 on Snowflake. It begins with an agenda and the presenter's background. It then discusses why customers are asking for Data Vault and provides an overview of the Data Vault methodology including its core components of hubs, links, and satellites. The document applies Snowflake features like separation of workloads and agile warehouse scaling to support Data Vault implementations. It also addresses modeling semi-structured data and building virtual information marts using views.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
This presentation shows all the posible options to move Oracle BI on-premise system to Oracle Analytics Cloud. We are going to see all the steps to perform this migration as well as the issues that we have seen and how to troubleshoot them. In addition we will review the most common administration tasks.
The JavaScript InfoVis Toolkit provides open source interactive data visualization tools using JSON data formats, including area charts, sunbursts, hyper trees, space trees, and more to help companies visualize hierarchical and quantitative data, though some coding experience is required as it is a library rather than an application.
Stellmach.2011.designing gaze supported multimodal interactions for the explo...mrgazer
This document describes research into designing gaze-supported multimodal interactions for exploring large image collections. Specifically, it investigates combining eye gaze with a touch-and-tilt mobile device or keyboard to control an adaptive fisheye lens visualization. User interviews were conducted to understand how people would want to use such input combinations for browsing images. Based on this feedback, a prototype system was developed using a SpringLens technique with gaze and a touch device. A user study provided insights into how well the gaze-supported interaction techniques were experienced.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document proposes an approach to integrate spatial information into inverted indexes for large-scale image retrieval. The approach divides images into grids using a spatial pyramid and builds multiple inverted indexes, one for each grid cell. This allows visual words to be indexed based on their location within an image. Experiments on benchmark datasets show the approach improves retrieval accuracy over standard inverted indexing while maintaining fast retrieval times, though it requires more memory than standard approaches.
The VISTA project aimed to integrate utility data from various UK organizations to improve coordination and reduce costs of street works. It developed methods for syntactically and semantically integrating heterogeneous utility data through a common data model and global thesaurus. Visualization techniques were also explored that incorporated uncertainty and were driven by an ontology. While the project proved the concept, further work is needed to develop the ontology and address implementation challenges regarding data currency, security, and impact on organizational systems.
This presentation has been prepared by Oleksii Prohonnyi for LvivJS 2015 conference (http://lvivjs.org.ua/)
See the speech in Russian by the following link: https://youtu.be/oi7JhB8eWnA
This document proposes a data model for managing large point cloud data while integrating semantics. It presents a conceptual model composed of three interconnected meta-models to efficiently store and manage point cloud data, and allow the injection of semantics. A prototype is implemented using Python and PostgreSQL to combine semantic and spatial concepts for queries on indoor point cloud data captured with a terrestrial laser scanner.
IRJET- Saliency based Image Co-SegmentationIRJET Journal
The document describes a saliency-based image co-segmentation method. It proposes to first extract inter-image information through co-saliency maps generated from multiple saliency extraction methods. Then, it performs single image segmentation on each individual image. A key step is fusing the diverse saliency maps through a saliency co-fusion process at the superpixel level. This exploits inter-image information to boost common foreground regions and suppress background regions. Experiments show the co-fusion based approach achieves competitive performance compared to state-of-the-art methods without parameter tuning.
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
This document discusses object recognition by computers. It notes that while object recognition is easy for humans, it is difficult for computers because they cannot rely on appearance alone. Key challenges for computers include variations in scale, shape, occlusion, lighting and background clutter. The document then discusses techniques used for object recognition, including feature detection methods like SIFT and SURF that extract keypoints, descriptors that describe regions around keypoints, and feature matching to identify corresponding regions between images. It also covers bag-of-words models, visual vocabularies and inverted indexing to allow large scale image retrieval. Finally, it lists applications of object recognition like digital watermarking, face detection and robot navigation.
The document summarizes the VIZLAND project, which aims to build a queryable database of over 60 data visualizations. It outlines the steps taken to prototype an initial version, including choosing a source catalogue, manually transcribing the visualizations, and visualizing the data. It then discusses plans to develop the prototype further by deploying a web version and using technologies like SVG, D3, and Meteor to create an interactive interface centered around a 3D meteor cloud visualization concept.
Unleashing the Power of Vector Search in .NET - DotNETConf2024.pdfLuigi Fugaro
Redis OM .NET has evolved to embrace the transformative world of vector database technology, now supporting Redis vector search and seamless integration with OpenAI, Azure OpenAI, Hugging Face, and ML.NET. This talk highlights the latest advancements in Redis OM .NET, focusing on how it simplifies the complex process of vector indexing, data modeling, and querying for AI-powered applications. Vector databases are redefining data handling, enabling semantic searches across text, images, and audio encoded as vectors. Redis OM .NET simplifies this innovative approach, making it accessible even for those new to vector data. We will explore the new capabilities of Redis OM .NET, including intuitive vector search interfaces and semantic caching, which reduce the overhead of large language model (LLM) calls.
This document discusses information visualization interfaces for multi-device synchronous collocated collaboration. It begins by introducing the prevalence of multiple connected devices and explores using these devices for collaborative work. The document then reviews key concepts in information visualization like visual variables and design guidelines. It discusses visualizations for different data types and devices. Importantly, it examines benefits of collaborative visualization and proposes developing a prototype interface to further test multi-device collaboration opportunities.
This document summarizes a research paper on Fashion AI. It proposes a new Group Decreasing Network (GroupDNet) that uses group convolutions in the generator and gradually reduces the percentage of groups in the decoder's convolutions. This allows the model to have more control over generating images from semantic labels and produce high-quality, multi-modal outputs. The paper describes GroupDNet's architecture, compares it to other approaches like using multiple generators, and shows it outperforms other methods on challenging datasets based on metrics like FID and mIoU. Potential applications discussed include mixed fashion styles, semantic manipulation, and tracking fashion trends over time. The conclusion discusses GroupDNet's performance but notes room for improving computational efficiency
Stack Zooming for Multi-Focus Interaction in Time-Series Data VisualizationNiklas Elmqvist
In this IEEE PacificVis 2010 presentation, we introduce a method for supporting multi-focus interaction in time-series datasets that we call stack zooming. The approach is based on the user interactively building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the visual exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness among the focus points.
Visualization approaches in text mining emphasize making large amounts of data easily accessible and identifying patterns within the data. Common visualization tools include simple concept graphs, histograms, line graphs, and circle graphs. These tools allow users to quickly explore relationships within text data and gain insights that may not be apparent from raw text alone. Architecturally, visualization tools are layered on top of text mining systems' core algorithms and allow for modular integration of different visualization front ends.
The document discusses using MapReduce for a sequential web access-based recommendation system. It explains how web server logs could be mapped to create a pattern tree showing frequent sequences of accessed web pages. When making recommendations for a user, their access pattern would be compared to patterns in the tree to find matching branches to suggest. MapReduce is well-suited for this because it can efficiently process and modify the large, dynamic tree structure across many machines in a fault-tolerant way.
The document discusses SuperMap's GIS products and technologies. It introduces their Land Management System and Field Mapper products. It then summarizes their GIS architecture, data model, and storage solutions including support for CAD data, databases using SuperMap SDX+, and file-based SDB/SDD formats. Finally, it outlines their focus on developing a general GIS platform and mentions their customer base of over 2000 organizations.
Ontology based semantics and graphical notation as directed graphsJohann Höchtl
The document discusses ontology-based semantics and visualization of ontologies as directed graphs. It provides an overview of ontology visualization tools including IsaViz, Protégé, Jambalaya, and Graphviz. It also discusses notions of ontology similarity and approaches used by tools like COMA++, BayesOWL, and SOQA-SimPack to measure structural and semantic similarity between ontologies.
Similaire à Datavisualization - Embed - Focus + Text (20)
Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
environmental standards.
Prepare feasibility studies, construction contracts, and tender documents with specifications and
tender analyses.
Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
meets environmental, safety, structural, zoning, and aesthetic standards.
Monitoring the progress of a project to assess whether or not it is in compliance with building plans
and project deadlines.
Attention to detail, exceptional time management, and strong problem-solving and communication
skills are required for this role.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
Best Digital Marketing Strategy Build Your Online Presence 2024.pptxpavankumarpayexelsol
This presentation provides a comprehensive guide to the best digital marketing strategies for 2024, focusing on enhancing your online presence. Key topics include understanding and targeting your audience, building a user-friendly and mobile-responsive website, leveraging the power of social media platforms, optimizing content for search engines, and using email marketing to foster direct engagement. By adopting these strategies, you can increase brand visibility, drive traffic, generate leads, and ultimately boost sales, ensuring your business thrives in the competitive digital landscape.
2. INTRODUCTION
Information visualization is widely acknowledged as a powerful
way of helping users make sense of complicated data, and a
great number of methods for visualizing and working with
various types of information have been presented.
However, all information visualization techniques will have to
comply to one inherent limitation: they will need to limit
themselves to the available area of a computer screen.
Embed: Focus + Text
3. INTRODUCTION
A common solution to this problem is to provide some kind of
movable view-port to the data, which can be controlled
through the manipulation of scrollbars or other means.
Zooming interfaces have also been introduced to let users
control the amount of data shown.
Sometimes, however, it might be important to give users access
to both overview and detailed information at the same time;
such techniques include, with separate areas for overview and
detail-on-demand information.
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4. Embed: Focus + Text
Data Visualization
Focus + Text
Integrated Visual Access (EMBED)
Details Overview
Dataset
5. DEFINITION
The family of idioms known as focus+context are based on
the design choice to embed detailed information about a
selected set—the focus—within a single view that also
contains overview information about more of the data—the
context.
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Data Visualization
6. ROLE OF FOCUS+TEXT
These idioms reduce the amount of data to show
in the view through sophisticated combinations of
filtering and aggregation.
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Data Visualization
7. WHY EMBED?
The goal of embedding focus and context together is to
mitigate the potential for disorientation that comes with
standard navigation techniques such as geometric zooming.
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Data Visualization
Geometric zooming allows the user to specify the scale of magnification and
increasing or decreasing the magnification of an image by that scale. This allows the
user focus on a specific area and information outside of this area is generally
discarded. A great example is mapping software like MapQuest.
8. WHY EMBED?
With realistic camera motion, only a small part of world space is visible
in the image when the camera is zoomed in to see details for a small
region. With geometric navigation and a single view that changes over
time, the only way to maintain orientation is to internally remember
one’s own navigation history.
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Data Visualization
Focus+context idioms attempt to support orientation by providing
contextual information intended to act as recognizable landmarks, using
external memory to reduce internal cognitive load.
cognitive
load refers
to the
used
amount of
working
memory
resources
9. ELIDE
One design choice for embedding is elision: some items are omitted
from the view completely, in a form of dynamic filtering.
Other items are summarized using dynamic aggregation for context,
and only the focus items are shown in detail.
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Data Visualization
10. ELIDE - DOITrees (DEGREE OF INTEREST TREES)
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Data Visualization
DOITrees Revisited uses elision to show multiple focus nodes within context in a 600,000 node tree.
12. SUPERIMPOSE
The focus layer is limited to a local region, rather than being a global
layer that stretches across the entire view to cover everything.
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Data Visualization
The superimpose family of design choices pertains to combining multiple
layers together by stacking them directly on top of each other in a single
composite view. Multiple simple drawings are combined on top of each other
into a single shared frame.
13. SUPERIMPOSE
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https://www.megapixl.com/abstract-squares-
superimposed-layers-illustration-56538917
Image Courtesy: saylordotorg.github.io
Data Visualization
14. SUPERIMPOSE
Embed: Focus + Text
The superimpose family of design choices pertains
to combining multiple layers together by stacking
them directly on top of each other in a single
composite view. Multiple simple drawings are
combined on top of each other into a single shared
frame.
Data Visualization
15. SUPERIMPOSE
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Tool Glass and Magic Lenses
The Toolglass and Magic Lenses
system uses a see-through lenses
to show color-coded Gaussian
curvature in a foreground layer
consisting of the 3D scene.
Data Visualization
16. SUPERIMPOSE
Embed: Focus + Text
The Toolglass and Magic Lenses
idiom provides focus and context
through a superimposed local
layer: the see-through lens color
codes the patchwork
sphere with Gaussian curvature
information and provides a
numeric value for
the point at the center.
Data Visualization
18. DISTORT
In contrast to using elision or layers, many
focus+context idioms solve the problem of
integrating focus and context into a single
view using geometric distortion of the contextual
regions to make room for the details in the focus
regions.
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Data Visualization
19. The Cone Tree system used
3D perspective for
focus+context, providing
a global distortion region
with a single focus point,
and using standard
geometric
navigation for interaction.
DISTORT
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Data Visualization
21. DISTORT - Fisheye Lens
The fisheye lens distortion idiom uses a single focus
with local extent and radial shape and the interaction
metaphor of a draggable lens on top of
the main view.
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Data Visualization
22. DISTORT - Fisheye Lens
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Data Visualization
Focus+context with interactive fisheye lens, with poker player dataset.
(a) Scatterplot showing correlation between two strategies. (b) Dense matrix
view showing correlation between a specific complex strategy and the player’s winning
rate, encoded by color.
24. HYPERBOLIC GEOMETRY
The distortion idiom of hyperbolic geometry uses a single radial global
focus with the interaction metaphor of hyperbolic translation. This
approach
exploits the mathematics of non-Euclidean geometry to elegantly
accommodate structures such as trees that grow by an exponential
factor, in contrast to standard Euclidean geometry where there is only a
polynomial amount of space available for placing items.
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Data Visualization
25. HYPERBOLIC GEOMETRY
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Data Visualization
Animated transition showing
navigation through 3D hyperbolic
geometry for a file system tree laid
out with the H3 idiom, where the
first three frames show hyperbolic
translation changing the focus
point and the last three show
standard
3D rotation spinning the structure
around.
28. STRETCH AND SQUISH NAVIGATION
The stretch and squish navigation idiom uses multiple
rectangular foci of global extent for distortion, and the
interaction metaphor where enlarging some regions causes
others to shrink. In this metaphor, the entire scene is
considered to be drawn on a rubber sheet where stretching
one region squishes the rest.
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Data Visualization
29. PRISequence Juxtaposer
supports comparing gene
sequences using
the stretch and squish
navigation idiom with the
guaranteed visibility of
marks
representing items with a
high importance value, via a
rendering algorithm with
custom subpixel
aggregation.
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Data Visualization
Nonlinear Magnification Fields
30. STRETCH AND SQUISH NAVIGATION
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Data Visualization
Tree Juxtaposer uses stretch and squish navigation with multiple rectangular foci for exploring phylogenetic
trees. (a) Stretching a single region when comparing two small trees. (b) Stretching multiple regions within
a large tree.
31. Embed: Focus + Text
Data Visualization
STRETCH AND SQUISH NAVIGATION
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Data Visualization
Nonlinear Magnification Fields
The nonlinear magnification fields idiom relies on a
general computational framework featuring
multiple foci of arbitrary magnification levels and
shapes, whose scope can be constrained to affect
only local regions.
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Data Visualization
Nonlinear Magnification Fields
General frameworks
calculate the
magnification and
minimization fields
needed to achieve
desired
transformations in the
image. (a) Desired
transformations. (b)
Calculated magnification
fields.
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Data Visualization
Nonlinear Magnification Fields
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Data Visualization
References:
Visualization Analysis & Design
Tamara Munzner
Thank you!