Tree-Maps and SeeSys are space-filling visualization approaches for hierarchical information and software metrics. Tree-Maps represent hierarchies as nested rectangles, using area to represent importance. SeeSys visualizes software metrics like size, activity level, and bugs within a code's hierarchical structure as colored rectangles. Both approaches aim to efficiently use space, enable comparison of components, and help comprehension through an overview of the whole structure and relationships between parts.
Mining Gems from the Data Visualization LiteratureNils Gehlenborg
What is the data visualization community and what can we learn from it?
What are some great examples?
What are the reasons why we don’t see more of this work in bioinformatics? The valley death ...
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.
The document discusses different types of parallelism that can be utilized in parallel database systems: I/O parallelism to retrieve relations from multiple disks in parallel, interquery parallelism to run different queries simultaneously, intraquery parallelism to parallelize operations within a single query, and intraoperation parallelism to parallelize individual operations like sort and join. It also covers techniques for partitioning relations across disks and handling skew to balance the workload.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
The document describes EasyEDD, a software for analyzing tomographic electron diffraction data (TEDDI) obtained from synchrotron sources. EasyEDD allows batch processing and visualization of large diffraction data sets. It stores data in a 3D grid format and includes tools for corrections, fitting, visualization and exporting results. The software combines a graphical user interface with algorithms for numerical analysis. Current functionality and future improvements are outlined.
«Дизайн продвинутых нереляционных схем для Big Data»Olga Lavrentieva
Виктор Смирнов (Java Tech Lead в Klika Technologies)
Доклад: «Дизайн продвинутых нереляционных схем для Big Data»
О чём: Виктор познакомит всех с примерами продвинутых нереляционных схем данных и тем, как они могут использоваться для решения задач, связанных с хранением и обработкой больших данных.
Information Visualisation – an introductionAlan Dix
Slides for the Information Visualisation unit of my 2013 online course on HCI
https://hcibook.com/hcicourse/2013/unit/08-infovis
Contents:
* What is Information Visualisation? – making data easier to understand using direct sensory experience – especially visual! ... but can have aural, tactile ‘visualisation’
* Why Information Visualisation? – for the data analyst: scientist, statistician, possibly you; and for the data consumer: audience, client, reader, end-user
* A Brief History of Visualisation – from 2500 BC to 2012
Visualisation in Context Section – how visualisation fits into the decision making process
* Designing Visualisation – choosing representations, managing trade-offs and making them flexible through interaction
* Classic Visualisation – the visualisations that have shaped the area
This document discusses content-based image retrieval using singular value decomposition (SVD) and support vector machines (SVM). It begins by explaining the need for automated image indexing and describes content-based image retrieval (CBIR) which searches image collections based on automatically extracted visual features. It then covers SVD for feature extraction and SVM for classification of image classes. The document concludes with experimental results demonstrating 64.985% accuracy on a database using this approach.
Mining Gems from the Data Visualization LiteratureNils Gehlenborg
What is the data visualization community and what can we learn from it?
What are some great examples?
What are the reasons why we don’t see more of this work in bioinformatics? The valley death ...
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.
The document discusses different types of parallelism that can be utilized in parallel database systems: I/O parallelism to retrieve relations from multiple disks in parallel, interquery parallelism to run different queries simultaneously, intraquery parallelism to parallelize operations within a single query, and intraoperation parallelism to parallelize individual operations like sort and join. It also covers techniques for partitioning relations across disks and handling skew to balance the workload.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
The document describes EasyEDD, a software for analyzing tomographic electron diffraction data (TEDDI) obtained from synchrotron sources. EasyEDD allows batch processing and visualization of large diffraction data sets. It stores data in a 3D grid format and includes tools for corrections, fitting, visualization and exporting results. The software combines a graphical user interface with algorithms for numerical analysis. Current functionality and future improvements are outlined.
«Дизайн продвинутых нереляционных схем для Big Data»Olga Lavrentieva
Виктор Смирнов (Java Tech Lead в Klika Technologies)
Доклад: «Дизайн продвинутых нереляционных схем для Big Data»
О чём: Виктор познакомит всех с примерами продвинутых нереляционных схем данных и тем, как они могут использоваться для решения задач, связанных с хранением и обработкой больших данных.
Information Visualisation – an introductionAlan Dix
Slides for the Information Visualisation unit of my 2013 online course on HCI
https://hcibook.com/hcicourse/2013/unit/08-infovis
Contents:
* What is Information Visualisation? – making data easier to understand using direct sensory experience – especially visual! ... but can have aural, tactile ‘visualisation’
* Why Information Visualisation? – for the data analyst: scientist, statistician, possibly you; and for the data consumer: audience, client, reader, end-user
* A Brief History of Visualisation – from 2500 BC to 2012
Visualisation in Context Section – how visualisation fits into the decision making process
* Designing Visualisation – choosing representations, managing trade-offs and making them flexible through interaction
* Classic Visualisation – the visualisations that have shaped the area
This document discusses content-based image retrieval using singular value decomposition (SVD) and support vector machines (SVM). It begins by explaining the need for automated image indexing and describes content-based image retrieval (CBIR) which searches image collections based on automatically extracted visual features. It then covers SVD for feature extraction and SVM for classification of image classes. The document concludes with experimental results demonstrating 64.985% accuracy on a database using this approach.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges in doing so. It then proposes combining k-means clustering with Voronoi diagrams to improve the performance of k-means when clustering uncertain data. Specifically, it suggests using k-means to generate clusters and Voronoi diagrams to answer nearest neighbor queries, in order to minimize computation time. Finally, it concludes that integrating clustering algorithms with indexing methods can effectively cluster uncertain data objects.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
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.
HBaseConAsia2018: Track2-5: JanusGraph-Distributed graph database with HBaseMichael Stack
This document provides an introduction to JanusGraph, an open source distributed graph database that can be used with Apache HBase for storage. It begins with background on graph databases and their structures, such as vertices, edges, properties, and different storage models. It then discusses JanusGraph's architecture, support for the TinkerPop graph computing framework, and schema and data modeling capabilities. Details are given on partitioning graphs across servers and using different indexing approaches. The document concludes by explaining why HBase is a good storage backend for JanusGraph and providing examples of how the data model would be structured within HBase.
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. It has become a business strategy for leveraging information resources generated by social media, scientific instruments, mobile devices, sensors, and networks. While more data can be collected than ever before, the challenges lie in managing, analyzing, summarizing, visualizing, and discovering knowledge from the data in a timely and scalable way. Hadoop is an open-source software framework that addresses these challenges through distributed storage and processing of large datasets across clusters of computers using simple programming models. It provides reliable storage of data via its Hadoop Distributed File System and scalable processing of that data using the MapReduce programming model.
Nagios Conference 2014 - Sam Lansing - Utilizing Data Visualizations in Syste...Nagios
Sam Lansing's presentation on Utilizing Data Visualizations in System Management With Nagios.
The presentation was given during the Nagios World Conference North America held Oct 13th - Oct 16th, 2014 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/conference
Presentation given at DMZ about Data Structure Graphs.
Also known as Applying Social Network Analysis Techniques to Data Modeling and Data Architecture
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 discusses EasyEDD, a software for processing and analyzing synchrotron diffraction data obtained via tomographic imaging technique TEDDI. EasyEDD allows managing, processing, analyzing and visualizing large quantities of synchrotron data with ease using graphical interface and scientific computing techniques. It reads and stores 3D diffraction data, performs corrections, fitting and visualization. Future work includes 3D mapping of data, more scientific functionality, Le Bail refinement and validation with experiments.
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
EasyED is a high throughput software that processes, analyzes, and visualizes powder diffraction data. It allows users to process large quantities of data with ease using graphical user interfaces combined with scientific computing techniques. EasyED currently supports curve fitting capabilities and 4 data file formats, with future plans to add more functionality like restraints, whole pattern decomposition, and 3D data mapping.
Data Structures & Recursion-Introduction.pdfMaryJacob24
This document provides an introduction to data structures and recursion. It defines data structures as organized collections of data and discusses common data structures like arrays, linked lists, stacks, and queues. Data structures are classified as primitive (like integers and characters) or non-primitive (like arrays and linked lists). Non-primitive structures are further divided into linear (arrays, linked lists) and non-linear (trees, graphs). Memory allocation techniques like static and dynamic allocation are also covered. The document concludes with an overview of recursion, including direct and indirect recursion, and examples of recursive functions like factorial and Fibonacci.
This document discusses data mining and provides information about defining data mining, explaining the knowledge discovery process with diagrams, describing a typical data mining architecture, and discussing major issues in data mining systems. It covers topics such as data cleaning, integration, selection, transformation, mining patterns, and evaluating patterns. Diagrams are provided to illustrate the knowledge discovery process and components of a data mining architecture. Major issues discussed include mining methodology, user interaction, performance, and handling diverse data types.
Spot db consistency checking and optimization in spatial databasePratik Udapure
This document discusses optimizing spatial databases. It covers spatial indexes like grids, z-order, octrees, quadtrees, UB-trees, R-trees, and kd-trees that are used to optimize spatial queries by decreasing search time. Spatial queries allow processing data types like geometry and geography and consider spatial relationships between objects. Examples of SQL queries on spatial data and features of spatial databases like spatial measurements and functions are also provided.
Data Science, Statistical Analysis and R... Learn what those mean, how they can help you find answers to your questions and complement the existing toolsets and processes you are currently using to make sense of data. We will explore R and the RStudio development environment, installing and using R packages, basic and essential data structures and data types, plotting graphics, manipulating data frames and how to connect R and SQL Server.
TreeNetViz - Revealing Patterns of Networks over Tree Structure.liangou
Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.
This document discusses convolutional neural networks (CNNs) for graph-structured data. CNNs are traditionally designed for Euclidean data like images but not irregular graph data. The key ideas are:
1) Define convolution on graphs using graph spectral theory by representing signals in the graph Fourier domain.
2) Coarsen graphs using a balanced cut model to extract hierarchical patterns.
3) Perform fast graph pooling using a binary tree of coarsened graphs for downsampling.
This allows generalizing CNNs to any graph data with the same computational efficiency as standard CNNs. Related works on graph CNNs are also discussed.
This 3-sentence summary provides the high-level information about the ICWSM'11 tutorial document:
The tutorial document announces a workshop on exploratory network analysis using Gephi, an open-source graph visualization and manipulation software, to be held on July 17, 2011 from 1-4 PM with instructors Sébastien Heymann and Julian Bilcke. The tutorial will provide an introduction to Gephi and guide participants through importing data, network visualization and manipulation, analysis, and aesthetics refinements using real datasets. Participants will work in teams and present preliminary results with the goal of learning practical skills for using Gephi on their own projects.
IIIF and DSpace 7 - IIIF Conference 2023.pdf4Science
In the last years IIIF became the “de facto” standard for presenting, navigating and delivering digital images on the web all over the world. It defines several APIs for providing a standard method for describing, analysing and sharing images over the web, as well as "presentation-based metadata" about structured sequences of images. However, images and, in particular, cultural heritage images, to be fully analysed, interpreted and enjoyed should be inserted in a “virtual ecosystem” in which they can be related with entities such as people, places, events, fonds, etc., according to different visions and interpretations.
Therefore, since 2017, we have been working at integrating IIIF in a Digital Library environment based on DSpace, the most used Open source Digital Asset Management System, developing a dedicated addon (starting from version 5), easily integrated with a set of external Image Servers, such as Cantaloupe or Digilib, and at extending DSpace data model as well, to structure contextual relationships among cultural heritage entities at different levels.
After DSpace 7 release, we worked with the community at integrating IIIF support in the official DSpace codebase. Now the DSpace REST API implements the IIIF Presentation API version 2.1.1, the IIIF Image API version 2.1.1, and the IIIF Search API version 1.0 (experimental). Any IIIF compliant image server can be integrated. The DSpace Angular frontend uses the Mirador 3.0 viewer.
However, Digital Library requirements are getting complex and complex. Therefore, to fulfil the needs of the cultural heritage domain, we enhanced our solutions based on DSpace 7, developing two further add-ons to integrate and enrich the “IIIF experience” within DSpace: the Document Viewer (for visualizing PDF files within Mirador) and the OCR module (for extracting text from images and indexing it).
Integrating IIIF and DSpace 7 and enriching the platform with new features, it has been possible to go beyond the traditional boundaries of the Digital libraries, structuring a complex system of relationships, building new narratives thanks to interdisciplinarity and the coexistence of different domains.
The proposed 2 hours workshop, addressed to librarians, archivists, historians, archaeologists, researchers and to all those who want to build their own digital library with DSpace 7 and IIIF, will introduce the attendees to the IIIF integration in DSpace both from the backend and from the frontend side.
We will analyze and share our approach and standard workflows for managing cultural heritage documents in DSpace using IIIF, starting with images submission and describing the operations required to make images available to the Mirador Image Viewer, the ones for extracting the text via OCR and for visualizing PDFs through the Image Viewer. Moreover, we will show how to relate items to each other, in order to build a complex system of relationships between entities, to be explored through network graphs.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges in doing so. It then proposes combining k-means clustering with Voronoi diagrams to improve the performance of k-means when clustering uncertain data. Specifically, it suggests using k-means to generate clusters and Voronoi diagrams to answer nearest neighbor queries, in order to minimize computation time. Finally, it concludes that integrating clustering algorithms with indexing methods can effectively cluster uncertain data objects.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
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.
HBaseConAsia2018: Track2-5: JanusGraph-Distributed graph database with HBaseMichael Stack
This document provides an introduction to JanusGraph, an open source distributed graph database that can be used with Apache HBase for storage. It begins with background on graph databases and their structures, such as vertices, edges, properties, and different storage models. It then discusses JanusGraph's architecture, support for the TinkerPop graph computing framework, and schema and data modeling capabilities. Details are given on partitioning graphs across servers and using different indexing approaches. The document concludes by explaining why HBase is a good storage backend for JanusGraph and providing examples of how the data model would be structured within HBase.
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. It has become a business strategy for leveraging information resources generated by social media, scientific instruments, mobile devices, sensors, and networks. While more data can be collected than ever before, the challenges lie in managing, analyzing, summarizing, visualizing, and discovering knowledge from the data in a timely and scalable way. Hadoop is an open-source software framework that addresses these challenges through distributed storage and processing of large datasets across clusters of computers using simple programming models. It provides reliable storage of data via its Hadoop Distributed File System and scalable processing of that data using the MapReduce programming model.
Nagios Conference 2014 - Sam Lansing - Utilizing Data Visualizations in Syste...Nagios
Sam Lansing's presentation on Utilizing Data Visualizations in System Management With Nagios.
The presentation was given during the Nagios World Conference North America held Oct 13th - Oct 16th, 2014 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/conference
Presentation given at DMZ about Data Structure Graphs.
Also known as Applying Social Network Analysis Techniques to Data Modeling and Data Architecture
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 discusses EasyEDD, a software for processing and analyzing synchrotron diffraction data obtained via tomographic imaging technique TEDDI. EasyEDD allows managing, processing, analyzing and visualizing large quantities of synchrotron data with ease using graphical interface and scientific computing techniques. It reads and stores 3D diffraction data, performs corrections, fitting and visualization. Future work includes 3D mapping of data, more scientific functionality, Le Bail refinement and validation with experiments.
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
EasyED is a high throughput software that processes, analyzes, and visualizes powder diffraction data. It allows users to process large quantities of data with ease using graphical user interfaces combined with scientific computing techniques. EasyED currently supports curve fitting capabilities and 4 data file formats, with future plans to add more functionality like restraints, whole pattern decomposition, and 3D data mapping.
Data Structures & Recursion-Introduction.pdfMaryJacob24
This document provides an introduction to data structures and recursion. It defines data structures as organized collections of data and discusses common data structures like arrays, linked lists, stacks, and queues. Data structures are classified as primitive (like integers and characters) or non-primitive (like arrays and linked lists). Non-primitive structures are further divided into linear (arrays, linked lists) and non-linear (trees, graphs). Memory allocation techniques like static and dynamic allocation are also covered. The document concludes with an overview of recursion, including direct and indirect recursion, and examples of recursive functions like factorial and Fibonacci.
This document discusses data mining and provides information about defining data mining, explaining the knowledge discovery process with diagrams, describing a typical data mining architecture, and discussing major issues in data mining systems. It covers topics such as data cleaning, integration, selection, transformation, mining patterns, and evaluating patterns. Diagrams are provided to illustrate the knowledge discovery process and components of a data mining architecture. Major issues discussed include mining methodology, user interaction, performance, and handling diverse data types.
Spot db consistency checking and optimization in spatial databasePratik Udapure
This document discusses optimizing spatial databases. It covers spatial indexes like grids, z-order, octrees, quadtrees, UB-trees, R-trees, and kd-trees that are used to optimize spatial queries by decreasing search time. Spatial queries allow processing data types like geometry and geography and consider spatial relationships between objects. Examples of SQL queries on spatial data and features of spatial databases like spatial measurements and functions are also provided.
Data Science, Statistical Analysis and R... Learn what those mean, how they can help you find answers to your questions and complement the existing toolsets and processes you are currently using to make sense of data. We will explore R and the RStudio development environment, installing and using R packages, basic and essential data structures and data types, plotting graphics, manipulating data frames and how to connect R and SQL Server.
TreeNetViz - Revealing Patterns of Networks over Tree Structure.liangou
Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.
This document discusses convolutional neural networks (CNNs) for graph-structured data. CNNs are traditionally designed for Euclidean data like images but not irregular graph data. The key ideas are:
1) Define convolution on graphs using graph spectral theory by representing signals in the graph Fourier domain.
2) Coarsen graphs using a balanced cut model to extract hierarchical patterns.
3) Perform fast graph pooling using a binary tree of coarsened graphs for downsampling.
This allows generalizing CNNs to any graph data with the same computational efficiency as standard CNNs. Related works on graph CNNs are also discussed.
This 3-sentence summary provides the high-level information about the ICWSM'11 tutorial document:
The tutorial document announces a workshop on exploratory network analysis using Gephi, an open-source graph visualization and manipulation software, to be held on July 17, 2011 from 1-4 PM with instructors Sébastien Heymann and Julian Bilcke. The tutorial will provide an introduction to Gephi and guide participants through importing data, network visualization and manipulation, analysis, and aesthetics refinements using real datasets. Participants will work in teams and present preliminary results with the goal of learning practical skills for using Gephi on their own projects.
IIIF and DSpace 7 - IIIF Conference 2023.pdf4Science
In the last years IIIF became the “de facto” standard for presenting, navigating and delivering digital images on the web all over the world. It defines several APIs for providing a standard method for describing, analysing and sharing images over the web, as well as "presentation-based metadata" about structured sequences of images. However, images and, in particular, cultural heritage images, to be fully analysed, interpreted and enjoyed should be inserted in a “virtual ecosystem” in which they can be related with entities such as people, places, events, fonds, etc., according to different visions and interpretations.
Therefore, since 2017, we have been working at integrating IIIF in a Digital Library environment based on DSpace, the most used Open source Digital Asset Management System, developing a dedicated addon (starting from version 5), easily integrated with a set of external Image Servers, such as Cantaloupe or Digilib, and at extending DSpace data model as well, to structure contextual relationships among cultural heritage entities at different levels.
After DSpace 7 release, we worked with the community at integrating IIIF support in the official DSpace codebase. Now the DSpace REST API implements the IIIF Presentation API version 2.1.1, the IIIF Image API version 2.1.1, and the IIIF Search API version 1.0 (experimental). Any IIIF compliant image server can be integrated. The DSpace Angular frontend uses the Mirador 3.0 viewer.
However, Digital Library requirements are getting complex and complex. Therefore, to fulfil the needs of the cultural heritage domain, we enhanced our solutions based on DSpace 7, developing two further add-ons to integrate and enrich the “IIIF experience” within DSpace: the Document Viewer (for visualizing PDF files within Mirador) and the OCR module (for extracting text from images and indexing it).
Integrating IIIF and DSpace 7 and enriching the platform with new features, it has been possible to go beyond the traditional boundaries of the Digital libraries, structuring a complex system of relationships, building new narratives thanks to interdisciplinarity and the coexistence of different domains.
The proposed 2 hours workshop, addressed to librarians, archivists, historians, archaeologists, researchers and to all those who want to build their own digital library with DSpace 7 and IIIF, will introduce the attendees to the IIIF integration in DSpace both from the backend and from the frontend side.
We will analyze and share our approach and standard workflows for managing cultural heritage documents in DSpace using IIIF, starting with images submission and describing the operations required to make images available to the Mirador Image Viewer, the ones for extracting the text via OCR and for visualizing PDFs through the Image Viewer. Moreover, we will show how to relate items to each other, in order to build a complex system of relationships between entities, to be explored through network graphs.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
10 tree map
1. Tree-Maps: A Space-FillingTree-Maps: A Space-Filling
Approach to the Visualization ofApproach to the Visualization of
Hierarchical Information StructuresHierarchical Information Structures
Presented by:Presented by:
Daniel Loewus-DeitchDaniel Loewus-Deitch
2. IntroductionIntroduction
Novel method for visualizing hierarchies.Novel method for visualizing hierarchies.
– Makes 100% use of available spaceMakes 100% use of available space
Maps the full hierarchy onto the screen inMaps the full hierarchy onto the screen in
a “space-filling manner.”a “space-filling manner.”
– Called Tree-MapsCalled Tree-Maps
Interactive ControlInteractive Control
– Allows users to specify presentation of bothAllows users to specify presentation of both
structural and content informationstructural and content information
3. IntroductionIntroduction
Sections of hierarchy with more importantSections of hierarchy with more important
information are allocated more display space.information are allocated more display space.
Collection of rectangular boxes represent theCollection of rectangular boxes represent the
tree structure.tree structure.
Best suited to hierarchies whereBest suited to hierarchies where
– The content of the leaf nodes and the structure of theThe content of the leaf nodes and the structure of the
hierarchy are most important.hierarchy are most important.
– The content information associated with internalThe content information associated with internal
nodes is largely derived from their children.nodes is largely derived from their children.
5. MotivationMotivation
(Problems with Current Methods)(Problems with Current Methods)
Traditional methods for displayingTraditional methods for displaying
hierarchies can be classified into 3hierarchies can be classified into 3
categories:categories:
– ListingsListings
– OutlinesOutlines
– Tree diagramsTree diagrams
6. ListingsListings
Can provide detailed content information.Can provide detailed content information.
Present structural information poorly.Present structural information poorly.
– Requires users to parse path information andRequires users to parse path information and
move manually through the hierarchy to get amove manually through the hierarchy to get a
real idea of its structure.real idea of its structure.
7. OutlinesOutlines
Can nicely provide both structural andCan nicely provide both structural and
content.content.
Structure can only be viewed a few linesStructure can only be viewed a few lines
at a time.at a time.
Inadequate for displaying a hierarchicalInadequate for displaying a hierarchical
structure with more than a few hundredstructure with more than a few hundred
nodes.nodes.
8. Tree DiagramsTree Diagrams
Excellent for small structures.Excellent for small structures.
Make poor use of available display space.Make poor use of available display space.
– Too much space used up for background.Too much space used up for background.
Little content information.Little content information.
– Presenting additional information clutters thePresenting additional information clutters the
display space.display space.
9. Why Tree-Maps are a goodWhy Tree-Maps are a good
alternativealternative
They use display space efficiently.They use display space efficiently.
Can provide structural informationCan provide structural information
implicitly.implicitly.
– Eliminates the need to draw internal nodes.Eliminates the need to draw internal nodes.
Provide an overall (global) view of theProvide an overall (global) view of the
entire hierarchy.entire hierarchy.
– Makes navigation and orientation easier.Makes navigation and orientation easier.
Provides creative visual cues toProvides creative visual cues to
communicate content information.communicate content information.
10. Presenting DirectoriesPresenting Directories
Problems with current methods:Problems with current methods:
– None provide a graphical representation ofNone provide a graphical representation of
the relative sizes of files or directories.the relative sizes of files or directories.
– Command line listings force user to pieceCommand line listings force user to piece
directory tree together manually.directory tree together manually.
– Windows obscure each other and require tooWindows obscure each other and require too
much effort to be arranged in any kind ofmuch effort to be arranged in any kind of
useful manner.useful manner.
– Icons only show the type of the file, but noIcons only show the type of the file, but no
other properties.other properties.
11. Presenting DirectoriesPresenting Directories
Origin of Tree-Maps conceptOrigin of Tree-Maps concept
– Venn diagramsVenn diagrams
– Tree diagramsTree diagrams
– Because these waste space, decided to useBecause these waste space, decided to use
boxes instead of ovals, along with a bin-boxes instead of ovals, along with a bin-
packing algorithm.packing algorithm.
– Worked well for small hierarchies only.Worked well for small hierarchies only.
Nesting caused problems.Nesting caused problems.
12. Presenting DirectoriesPresenting Directories
Origin of Tree-Maps conceptOrigin of Tree-Maps concept
– Discovered “slice and dice” method.Discovered “slice and dice” method.
Simple linear method (top-down).Simple linear method (top-down).
– Developed a weight-proportionate distribution.Developed a weight-proportionate distribution.
– Added a pop-up dialog window for detailedAdded a pop-up dialog window for detailed
content information.content information.
– Simple color mapping helps distinguishSimple color mapping helps distinguish
various properties of files, such as type andvarious properties of files, such as type and
size.size.
13. Tree Map MethodTree Map Method
Structural Information:Structural Information:
– Interactive approach gives user control overInteractive approach gives user control over
how tree is displayed.how tree is displayed.
– Requires that a weight be assigned to eachRequires that a weight be assigned to each
node, which determines the size of thatnode, which determines the size of that
node’s bounding box.node’s bounding box.
14. Tree Map MethodTree Map Method
Structural Information:Structural Information:
– There are some properties that always hold,There are some properties that always hold,
maintaining a consistent relationship betweenmaintaining a consistent relationship between
the structure of the hierarchy and its Tree-the structure of the hierarchy and its Tree-
Map representation (pg. 156).Map representation (pg. 156).
– Structural information is implicitly presented,Structural information is implicitly presented,
but can be nested to explicitly indicate.but can be nested to explicitly indicate.
– Non-nested display explicity provides directNon-nested display explicity provides direct
selection only for leaf nodes.selection only for leaf nodes.
15. Tree Map MethodTree Map Method
Content Information:Content Information:
– Variety of display properties determines howVariety of display properties determines how
the node is drawn.the node is drawn.
– Color is the most important property.Color is the most important property.
– Other properties include pitch of tone andOther properties include pitch of tone and
color saturation.color saturation.
– Pop-up display provides information about thePop-up display provides information about the
node currently under the cursor.node currently under the cursor.
16. Coping With SizeCoping With Size
Groups of small files can becomeGroups of small files can become
indistinguishable (completely blackindistinguishable (completely black
regions).regions).
Zooming in on these regions helps theZooming in on these regions helps the
local structure become clear.local structure become clear.
17. Future ResearchFuture Research
Exploration of alternate structuralExploration of alternate structural
partitioning schemes.partitioning schemes.
Appropriate visual display of both numericAppropriate visual display of both numeric
and non-numeric content information.and non-numeric content information.
Dynamic viewsDynamic views
– Animated time sliceAnimated time slice
20. IntroductionIntroduction
SeeSys is a system that allows users toSeeSys is a system that allows users to
visualize statistics associated with codevisualize statistics associated with code
that is divided hierarchically intothat is divided hierarchically into
subsystems, directories, and files.subsystems, directories, and files.
21. IntroductionIntroduction
Problems with current methods:Problems with current methods:
– Ineffective for large software systems.Ineffective for large software systems.
– Routines for producing flow charts, functionRoutines for producing flow charts, function
call graphs, and structure diagrams oftencall graphs, and structure diagrams often
break down.break down.
– Incomprehensible, cluttered display.Incomprehensible, cluttered display.
22. IntroductionIntroduction
Project managers need a tool thatProject managers need a tool that
facilitates management issues of softwarefacilitates management issues of software
development.development.
– Where new development activity is occurring.Where new development activity is occurring.
– Which modules are error prone.Which modules are error prone.
Motivation for SeeSys came from AT&T’sMotivation for SeeSys came from AT&T’s
massive communications softwaremassive communications software
system.system.
Five questions for project managers (pg.Five questions for project managers (pg.
162)162)
23. IntroductionIntroduction
Statistical methods, alone, don’t provideStatistical methods, alone, don’t provide
thethe contextcontext necessary to make validnecessary to make valid
analyses.analyses.
SeeSys visualizes subsystem, directory,SeeSys visualizes subsystem, directory,
and file statistics, but within appropriateand file statistics, but within appropriate
context.context.
– Preserves hierarchical relationships in thePreserves hierarchical relationships in the
code.code.
– Makes it easy to relate the statistics to theMakes it easy to relate the statistics to the
components.components.
24. ApproachApproach
Based on idea that software system canBased on idea that software system can
be decomposed into its individualbe decomposed into its individual
components.components.
Subsystems labeled with letters.Subsystems labeled with letters.
Subsystems are partitioned vertically andSubsystems are partitioned vertically and
their area is based on a particulartheir area is based on a particular
subsystem statistic.subsystem statistic.
Allows for visual comparison of directoriesAllows for visual comparison of directories
within a subsystem.within a subsystem.
25. ApproachApproach
Fill represents a second statistic, such asFill represents a second statistic, such as
indicating newly-developed code.indicating newly-developed code.
Zoom view to get a closer look at anZoom view to get a closer look at an
individual subsystem.individual subsystem.
Hierarchical decomposition immediatelyHierarchical decomposition immediately
relates the files to their directories and therelates the files to their directories and the
directories to their subsystems.directories to their subsystems.
– Makes cross unit comparisons easy.Makes cross unit comparisons easy.
26. ApproachApproach
The fill represents percentages.The fill represents percentages.
– Allows for quick discovery of outliers.Allows for quick discovery of outliers.
27. ApplicationsApplications
Subsystem informationSubsystem information
– Size and color brightness represent the sizeSize and color brightness represent the size
or individual subsystems.or individual subsystems.
Directory informationDirectory information
– Each subsystem is partitioned vertically toEach subsystem is partitioned vertically to
show its internal directories.show its internal directories.
– Area and color represent size.Area and color represent size.
– Fill is related to new development.Fill is related to new development.
– Figure 3 is the “software skyline.”Figure 3 is the “software skyline.”
28. ApplicationsApplications
Error-prone codeError-prone code
– Directory spikes represent detail for directoryDirectory spikes represent detail for directory
bug fixing.bug fixing.
– Subsystem g shows an example of a verySubsystem g shows an example of a very
high bug rate (figure 5), represented by thehigh bug rate (figure 5), represented by the
light gray subsystem rectangle.light gray subsystem rectangle.
System evolutionSystem evolution
– Animated display portrays growth through theAnimated display portrays growth through the
software’s version releases.software’s version releases.
– Shows history and trends of each subsystem.Shows history and trends of each subsystem.
29. The Visualization SystemThe Visualization System
SeeSys was designed to display softwareSeeSys was designed to display software
metrics that have two propertiesmetrics that have two properties
– Quantitative measureQuantitative measure
– AdditiveAdditive
May be extended to display complexityMay be extended to display complexity
metrics.metrics.
30. User InteractionUser Interaction
Tracks mouse movements and showsTracks mouse movements and shows
extra information about the componentextra information about the component
that the mouse cursor is touching.that the mouse cursor is touching.
– Active component indicated by a redActive component indicated by a red
highlighted boundary.highlighted boundary.
Available stats are shown on lower leftAvailable stats are shown on lower left
side of screen.side of screen.
Clicking these stats creates a redrawnClicking these stats creates a redrawn
display focused on this particular statistic.display focused on this particular statistic.
31. User InteractionUser Interaction
Five buttons control various options suchFive buttons control various options such
as presence of fill and zoom activation.as presence of fill and zoom activation.
ROWS slider controls the number of rowsROWS slider controls the number of rows
in the display.in the display.
Speed slider and frame slider controlSpeed slider and frame slider control
animation.animation.
During animation, one can watch theDuring animation, one can watch the
active bar in the slider to see thatactive bar in the slider to see that
particular subsystem’s evolution.particular subsystem’s evolution.
32. Display PrinciplesDisplay Principles
Based on 3 principles:Based on 3 principles:
– Individual components can be assembled toIndividual components can be assembled to
form the whole.form the whole.
Allows users to see relationships betweenAllows users to see relationships between
components.components.
– Pairs of components can be compared.Pairs of components can be compared.
– Components can be disassembled intoComponents can be disassembled into
smaller components.smaller components.
Allows structure of display to reflect structure ofAllows structure of display to reflect structure of
software.software.
33. Screen Real-EstateScreen Real-Estate
100% of display area is utilized.100% of display area is utilized.
Components with large statistics areComponents with large statistics are
visually dominant.visually dominant.
Zoom feature allows user to see smallZoom feature allows user to see small
directories.directories.
34. Spatial RelationshipsSpatial Relationships
Takes advantage of human ability toTakes advantage of human ability to
recognize spatial relationships.recognize spatial relationships.
People relate each component to thePeople relate each component to the
whole.whole.
It is easier to see relationships betweenIt is easier to see relationships between
components if the heights of thecomponents if the heights of the
rectangles are equal.rectangles are equal.
– Row slider allows user to choose number ofRow slider allows user to choose number of
rows displayed for an optimal display.rows displayed for an optimal display.
35. ColorColor
Redundantly encodes size.Redundantly encodes size.
Can also be used to encode age,Can also be used to encode age,
complexity, activity, number ofcomplexity, activity, number of
programmers, etc.programmers, etc.
36. ImplementationImplementation
Four linked views of data:Four linked views of data:
– Colorful space-filling display.Colorful space-filling display.
– Leftspace – controls, buttons, sliders.Leftspace – controls, buttons, sliders.
– Bottomspace – color scale and statistics.Bottomspace – color scale and statistics.
– Zoom view – details of a particularZoom view – details of a particular
subsystem.subsystem.
37. SummarySummary
SeeSys provides the following utilities:SeeSys provides the following utilities:
– Shows the sizes of the subsystems andShows the sizes of the subsystems and
directories and where the recent activity hasdirectories and where the recent activity has
occurred.occurred.
– Zoom in on particular subsystems.Zoom in on particular subsystems.
– Explore where bug fixes and new functionalityExplore where bug fixes and new functionality
have occurred.have occurred.
– Identify directories and subsystems with highIdentify directories and subsystems with high
fix-on-fix rates.fix-on-fix rates.
– Find historically active and extinct subsystemsFind historically active and extinct subsystems
38. SummarySummary
3 principles should ultimately be observed3 principles should ultimately be observed
when designing any visualization systemwhen designing any visualization system
for large software systems:for large software systems:
– Structure of display should reflect structure ofStructure of display should reflect structure of
software.software.
– Individual components should by comparableIndividual components should by comparable
and decomposable.and decomposable.
– Animation helps user visualize the evolutionAnimation helps user visualize the evolution
of the software.of the software.