#dataaggregation

[sudarshannarwade]

If the CDS View Has Aggregate Functions, How to Expand It

The Append-structure concept is used in SE11 to add additional fields to the preexisting table structure. In a same vein, let’s say a CDS-View shows 8 fields from a table or join of multiple tables, and we would like to show 3 more fields from the same data. In that scenario, we can extend the CDS view to show all 11 fields. read more

[guillaumelauzier]

Graph Neural Networks: Revolutionizing Data Analysis in Graph-Structured Domains

Graph Neural Networks (GNNs) represent a paradigm shift in the realm of neural networks, uniquely tailored for graph-structured data. They are pivotal in addressing complex data scenarios where traditional neural networks fall short. This comprehensive article delves into the core functionalities, applications, and future potential of GNNs.

Understanding Graph Neural Networks

Direct Application to Graphs GNNs' foremost strength lies in their direct application to graphs, facilitating node-level, edge-level, and graph-level prediction tasks. This flexibility proves invaluable across various fields where data is intrinsically relational, such as analyzing social networks, understanding molecular structures, and optimizing communication networks . Processing Complex Graph-Structured Data GNNs excel at processing and analyzing intricate graph-structured data. This capacity unlocks new avenues in numerous domains, including network analysis, computational biology, and the development of advanced recommender systems . Dependence on Graph Structure Central to GNNs' functionality is their ability to capture the dependence of graphs through message passing between nodes. By leveraging the inherent structural information of graphs, GNNs can make more accurate predictions and analyses, a critical aspect in fields like network security and structural health monitoring .

Expansive Applications of GNNs

Versatility in Various Fields GNNs' adaptability to graph data makes them invaluable in areas where relationships and connections are crucial. This includes, but is not limited to, social network analysis, drug discovery and chemistry, traffic flow prediction, and biological network analysis . From Foundations to Frontiers Spanning from basic concepts to cutting-edge advancements, GNNs are continually evolving. Ongoing research and development are likely to amplify their capabilities, making them even more effective in handling diverse, graph-related challenges .

How can Graph Neural Networks be used in Generative Art?

Graph Neural Networks (GNNs) have significant potential in the realm of generative art, leveraging their unique capabilities in understanding and manipulating graph-structured data. Here are some ways GNNs can be applied in this field: - Modeling Complex Relationships: GNNs can model intricate relationships and patterns within data. In generative art, they can analyze the structure of artistic elements, like color, form, and composition, to generate new artworks that maintain stylistic coherence or offer novel artistic interpretations. - Link Prediction for Artistic Elements: GNNs are adept at inferring missing links or detecting spurious ones in graph data. This capability can be used in generative art to predict and create connections between different artistic elements, leading to the generation of visually cohesive and complex artworks . - Learning Node Embeddings: In the context of generative art, GNNs can learn embeddings (representations) of various artistic elements. These embeddings can capture the nuances of style, technique, and other artistic features, which can then be used to generate new art pieces that reflect certain styles or artistic trends . - Message Passing for Artistic Interpretation: GNNs use message passing to understand graph structures, which can be applied to the way different elements in an artwork relate to each other. This can help in creating art that dynamically changes or evolves based on certain rules or inputs, adding an interactive or adaptive element to the artwork .

Python code example of a Graph Neural Networks

Here's a basic example of implementing a Graph Neural Network (GNN) using PyTorch. This code demonstrates the creation of a simple GNN for node classification on a graph: import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import GCNConv # Define a simple GNN model class GCN(nn.Module): def __init__(self, num_features, num_classes): super(GCN, self).__init__() self.conv1 = GCNConv(num_features, 16) self.conv2 = GCNConv(16, num_classes) def forward(self, data): x, edge_index = data.x, data.edge_index # First Graph Convolutional Layer x = self.conv1(x, edge_index) x = F.relu(x) x = F.dropout(x, training=self.training) # Second Graph Convolutional Layer x = self.conv2(x, edge_index) return F.log_softmax(x, dim=1) # Example usage num_features = 10 # Number of features per node num_classes = 3 # Number of classes for classification model = GCN(num_features, num_classes) This code defines a simple two-layer Graph Convolutional Network (GCN) using PyTorch and PyTorch Geometric. The model takes in the number of features per node and the number of classes for classification. Each convolutional layer (GCNConv) in the network processes the graph data, applying a graph convolution followed by a ReLU activation and dropout. Note: This is a basic example. For a real-world application, you would need to provide graph data (nodes, edges, node features) to the model and train it on a specific task like node classification, link prediction, etc.

🌐 Sources

- AssemblyAI - AI trends in 2023: Graph Neural Networks - ScienceDirect - Graph neural networks: A review of methods and applications - arXiv - Generative Graph Neural Networks for Link Prediction - YouTube - AI Explained: Graph Neural Networks and Generative AI - Medium - Top Applications of Graph Neural Networks 2021 - Towards Data Science - Applications of Graph Neural Networks - XenonStack - Graph Neural Network Applications and its Future - arXiv - Graph Neural Networks: Methods, Applications, and - neptune.ai - Graph Neural Network and Some of GNN Applications - sciencedirect.com - Graph neural networks: A review of methods and applications - frontiersin.org - Graph Neural Networks and Their Current Applications in - Jonathan Hui - Applications of Graph Neural Networks (GNN) - Medium - GNN python code in Keras and pytorch - Towards Data Science - How to Create a Graph Neural Network in Python - DataCamp - A Comprehensive Introduction to Graph Neural Networks - GitHub - Hands-On-Graph-Neural-Networks-Using-Python - Towards Data Science - Graph Neural Networks in Python - Analytics Vidhya - Getting Started with Graph Neural Networks Read the full article

[kenwords-blog]

The Secret Sauce Behind Tickertags - AI-Powered Stock Research

Tickertags has exploded onto the social investing scene, quickly becoming a favorite platform for traders to collaboratively track, discuss, and discover stock ideas. But what exactly sets Tickertags apart from the crowded field of stock research apps? Two key ingredients - social engagement and AI-driven data aggregation.

I. AI That Learns Your Interests

At its core, Tickertags combines the power of community with machine intelligence. The platform's proprietary algorithms constantly scrape reliable data sources to assemble up-to-the-minute news, charts, metrics, and analysis tailored to the stocks you care about. Just tag any ticker with keywords like "cloud computing", "undervalued", or "high growth" to track it. Tickertags' AI will keep you effortlessly updated with everything impacting your tagged tickers. This means no more wasting hours digging through fragmented information sources. Tickertags puts the data that matters most to you in one place. And its machine learning models only get smarter over time at surfacing relevant insights for your watchlist.

II. Collective Wisdom Through Social Engagement

But Tickertags doesn't stop there. Its social community feed lets you share ideas and perspectives on tagged stocks with fellow investors. Tap into the wisdom of the crowds - get diverse viewpoints beyond just your own. Follow proven experts to discover what stocks they're tracking and why. Monitor trending tag conversations to spot promising opportunities. Too often, investing feels like a lonely endeavor. We pore over data and research in isolation. Tickertags breaks down those walls by combining AI-aggregation with social engagement. Now you can collaborate with real users as the platform's algorithms guide your discovery.

III. The Powerful Blend Together

This one-two punch of machine intelligence and community gives Tickertags its secret sauce. Competitors may offer social features or data tools, but none blend them as powerfully as Tickertags. Its seamless integration allows you to be both systematic and social in your research - capitalizing on human wisdom and AI capabilities. So if you're tired of fragmented, isolated stock research, head over to Tickertags and start your free account today. Start tagging stocks, following experts, and tapping into the club-like community. With its AI-powered social approach, Tickertags could take your investing results to new heights. Read the full article

[jinactusconsulting]

Transforming Data into Actionable Insights with Domo

In today's data-driven world, organizations face the challenge of managing vast amounts of data from various sources and deriving meaningful insights from it. Domo, a powerful cloud-based platform, has emerged as a game-changer in the realm of business intelligence and data analytics. In this blog post, we will explore the capabilities of Domo and how it enables businesses to harness the full potential of their data.

What is Domo?  

Domo is a cloud-based business intelligence and data analytics platform that empowers organizations to easily connect, prepare, visualize, and analyze their data in real-time. It offers a comprehensive suite of tools and features designed to streamline data operations and facilitate data-driven decision-making. 

Key Features and Benefits:

Data Integration: Domo enables seamless integration with a wide range of data sources, including databases, spreadsheets, cloud services, and more. It simplifies the process of consolidating data from disparate sources, allowing users to gain a holistic view of their organization's data.

Data Preparation: With Domo, data preparation becomes a breeze. It offers intuitive data transformation capabilities, such as data cleansing, aggregation, and enrichment, without the need for complex coding. Users can easily manipulate and shape their data to suit their analysis requirements.

Data Visualization: Domo provides powerful visualization tools that allow users to create interactive dashboards, reports, and charts. It offers a rich library of visualization options and customization features, enabling users to present their data in a visually appealing and easily understandable manner.

Collaboration and Sharing: Domo fosters collaboration within organizations by providing a centralized platform for data sharing and collaboration. Users can share reports, dashboards, and insights with team members, fostering a data-driven culture and enabling timely decision-making across departments.

AI-Powered Insights: Domo leverages artificial intelligence and machine learning algorithms to uncover hidden patterns, trends, and anomalies in data. It provides automated insights and alerts, empowering users to proactively identify opportunities and mitigate risks.

Use Cases:

Sales and Marketing Analytics: Domo helps businesses analyze sales data, track marketing campaigns, and measure ROI. It provides real-time visibility into key sales metrics, customer segmentation, and campaign performance, enabling organizations to optimize their sales and marketing strategies. 

Operations and Supply Chain Management: Domo enables organizations to gain actionable insights into their operations and supply chain. It helps identify bottlenecks, monitor inventory levels, track production metrics, and streamline processes for improved efficiency and cost savings.

Financial Analysis: Domo facilitates financial reporting and analysis by integrating data from various financial systems. It allows CFOs and finance teams to monitor key financial metrics, track budget vs. actuals, and perform advanced financial modeling to drive strategic decision-making.

Human Resources Analytics: Domo can be leveraged to analyze HR data, including employee performance, retention, and engagement. It provides HR professionals with valuable insights for talent management, workforce planning, and improving overall employee satisfaction.

Success Stories: Several organizations have witnessed significant benefits from adopting Domo. For example, a global retail chain utilized Domo to consolidate and analyze data from multiple stores, resulting in improved inventory management and optimized product placement. A technology startup leveraged Domo to analyze customer behavior and enhance its product offerings, leading to increased customer satisfaction and higher revenue.

Domo offers a powerful and user-friendly platform for organizations to unlock the full potential of their data. By providing seamless data integration, robust analytics capabilities, and collaboration features, Domo empowers businesses to make data-driven decisions and gain a competitive edge in today's fast-paced business landscape. Whether it's sales, marketing, operations, finance, or HR, Domo can revolutionize the way organizations leverage data to drive growth and innovation.

[itesservices]

Ready to take your data aggregation to the next level? Discover how strategic partnerships with digital data collection service companies can revolutionize your business. From scaling operations to enhancing data quality and compliance, there's immense potential waiting to be unlocked.

Learn how partnering with us can offer unparalleled data scope, faster insights, cost optimization, and access to specialized expertise. Let's collaborate to innovate and drive success together.

Connect with us today to explore how our digital data collection services can propel your data aggregation business forward. Don't miss out on the opportunity to harness the power of strategic partnerships for smarter data and smarter decisions. Read more: https://www.damcogroup.com/blogs/beyond-manual-exploring-benefits-of-automated-data-processing

#DataAggregation #Partnerships #DataCollection #BusinessGrowth

[luminadata]

best data aggregation tool

Lumina Datamatics uses multiple web scraping techniques leveraged with years of ecommerce experience to give exciting insights for making business and strategic decisions.

[global-insights]

SG Analytics' data aggregation services assist clients in mining large enterprise data and processing it efficiently - from exploratory data analysis to other business processes, to derive actionable insights for better decision-making.

[incegna]

Tableau for Beginners: Get Certified,Jump into data visualization with hands-on exercises.Create bar charts, pie charts, and line graphs.Create trend lines and understand the relevant statistical metrics such as p-value and R-squared.Create a dynamic dashboards combining multiple worksheets. Interested People can share me your details. Check our Info : www.incegna.com Reg Link for Programs : http://www.incegna.com/contact-us Follow us on Facebook : www.facebook.com/INCEGNA/? Follow us on Instagram : https://www.instagram.com/_incegna/ For Queries : enquiry@incegna.com #tableau,#datavisualization,#barcharts,#piecharts,#datasources,#dataanalysis,#statistical,#pvalued,#Rsquare,#DataAggregation,#tableaudesktop,#WebAuthoring,#datablending https://www.instagram.com/p/B60Fi-agt5S/?igshid=1m7wep4cf3xdk

[amberstevens311-blog]

#BigData is giving rise to a new breed of #Services aimed at helping over-burdened #ITDepartments take on the challenges of #DataAnalytics without investing in additional infrastructure.

https://www.centurysoft.com/services/big-data/

[mobiusservices-blog]

Find out how #datacrawling can help your #business just like how it did for #Google Read on to know from our recent #blog #dataaggregation #datascraping #data #bigdata #SEO #Friday

[abdussalamsheikh]

Attaches them to the main social profiles/business pages like Facebook, Twitter, Pinterest, LinkedIn, Instagram, youtube, etc. This will really help to optimize your business pages or company pages also will lead to huge traffic.

#SEO #backlink #social #profiles #business #abdus_salam #MadeOnLegiit #LocalSEO #Legiit #localcitation #locallistings #GMB #googlemybusiness #localcitations #localSEO #mapscitation #mapslistings #mapsranking #localranking #dataaggregators #SEO #fiverr #upwork #freelancer #linkbuilding

[prevajconsultants]

Introducing the Aggregation Pipeline Builder in MongoDB Compass! Read about it here →https://t.co/UGPhj7tz7r #MongoDBCompass #DataAggregation https://t.co/xqKBY7NN03

Introducing the Aggregation Pipeline Builder in MongoDB Compass! Read about it here →https://t.co/UGPhj7tz7r #MongoDBCompass #DataAggregation pic.twitter.com/xqKBY7NN03

— MongoDB (@MongoDB) May 31, 2018

MongoDB

[yogeshmalik]

Baker McKenzie's Ben Allgrove on why law is now a 'data-aggregation business' https://ift.tt/2Jdk7W8

[itesservices]

Excited to unveil the benefits of strategic partnerships between data aggregators and digital data collection agencies. Our latest infographic highlights how collaboration drives scalability, enhances data quality, and unlocks new growth opportunities. Discover the advantages and tips for building symbiotic relationships in our infographic. #DataAggregators #DataCollection #Partnerships #Infographic #DataInsights

[luminadata]

Retail Data Aggregation & Web Scraping Services | Lumina Datamatics

Use Lumina Datamatics' retail data aggregation & web scraping services for business insights and strategic decision making to ensure competitive advantage.