Zeeshan Karamat

📅 On 07.11.2024, we invite you to the ScaDS.AI Dresden/Leipzig training "Big Data Processing on HPC" with Apurv Kulkarni and Wenyu Zhang. Apache Spark and Apache Flink are two typical #BigData analytics frameworks. Their APIs allow the development and testing of an application on a local workstation and later, without changing the source code of the application, distribute work to many computers when the local workstation is not sufficient anymore due to limited resources. The course focuses on the step from a local workstation to an #HPC environment and presents how the typical Big Data analysis workflow can be organized in an HPC environment. In this course participants will be introduced to running a data pipeline and data processing along with managing the configurations on the HPC environment, using Apache Flink and Apache Spark. Register here: 👉 https://lnkd.in/eYfmiMT8

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📣 New blog post on #RDF and SHACL In this post, we showcase an extension to the well-known RDFLib Python library written by Paco Nathan, that allows you to use Kùzu as the backend to store and query the RDF graph. Many thanks to Paco for his contribution! ✅ The Shapes Constraint Language (SHACL) is a language for validating RDF graphs against a set of conditions. It's used in Python via the pySHACL library, a pure-Python library for validating RDF graphs that sits on top of RDFLib. You can now combine these tools with Kùzu! ✅ Using the extension framework provided (GitHub link below), you can choose the most appropriate query language to analyze your data and query your triples, depending on your workflow and how you want to interface with the graph — you could use SPARQL via RDFLib or Cypher via Kùzu. ✅ Kùzu provides a simple and intuitive interface to load, query and visualize RDF graphs in Cypher, without compromising scalability and performance, because the RDF triples are essentially mapped to Kùzu’s native property graph model. ✅ This is just the tip of the iceberg in terms of the pipelines you can build over your Kùzu RDFGraphs with RDFLib integration. The linked blog post showed only how to use two implementations of RDF standards in Python: SPARQL and SHACL. But you can use others, like OWL-RL too. We hope this post has provided a good starting point for you to explore RDF data models, SHACL, and how to combine them using Kùzu as your graph backend. Feel free to go through our RDFGraphs documentation to learn more! Blog post: https://lnkd.in/ehehhWGp GitHub repo: https://lnkd.in/eG2phhGw Kùzu RDFGraphs docs: https://lnkd.in/eXRqCM3i #graph #database

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🚀 𝗢𝘂𝘁 𝗻𝗼𝘄: 𝗧𝘄𝗼 𝗵𝗲𝗶𝘀𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀 𝗼𝗻 𝗟𝗟𝗠 Alexander Schatten, senior researcher at SBA Research, and Marco Jeppe Reiser have published an interesting article: 📰 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗮𝘁 𝗽𝗿𝗼𝗺𝗽𝘁 𝗹𝗲𝘃𝗲𝗹 𝗹𝗲𝗮𝗱𝘀 𝘁𝗼 Where can AI systems really help programmers, and is the approach taken here truly novel? This article examines their role. Programming involves interpreting real-world problems and processes, abstracting them, and then translating them into a deterministic language understood by technical systems. The goal is to automate tasks and processes and interact with people or other systems as needed. Besides understanding the real-world problem, programming also requires technical consideration of advantages and disadvantages, costs, legal frameworks, dependencies, and more. Actual coding is only a small part of the whole process. Alexander Schatten's second article on heise online explores the role of LLMs in software development. 📰 𝗟𝗟𝗠𝘀 𝗶𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗯𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗳𝗮𝗶𝗹𝗲𝗱 Software systems are a combination of hardware, software, and the people who develop, maintain, and use these systems. This interaction of the three factors creates benefits and the possibility of further system development. Early on, software exhibited characteristics not observed in other technical systems. The first software crisis in the 1960s revealed that hardware and software capabilities were growing faster than those of human engineers, a crisis from which we have not yet fully emerged. One reason is the ever-increasing complexity of software itself and the complexity created by software and its increasing networking with other systems, affecting software further. Get the full articles here:   https://lnkd.in/gA2hHCBb   https://lnkd.in/giAV6f8C    Both articles will soon be published in iX magazine. 📷: Marcel Lehner 

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This is one of the advantages of using Knowledge Graph based RAG over using a Vector RAG. This shift from chunk-based retrieval to a relationship-focused (Entities and Edges) approach reduces dramatically the amount of tokens. Instead of feeding the LLM model with K chunks of text, it's feed with a list of much more facts with much less tokens.

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𝐔𝐧𝐯𝐞𝐢𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝟗 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬! Efficient data management is at the heart of modern technology, and understanding the right data structures is key to optimizing performance. We're thrilled to present our latest insights into the world of data structures, highlighting the pivotal roles they play in transforming how data is handled. 📌𝐁𝐢𝐧𝐚𝐫𝐲 𝐓𝐫𝐞𝐞: Efficiently manage hierarchical data with the structured power of Binary Trees. 📌𝐒𝐭𝐚𝐜𝐤: Embrace the LIFO simplicity and stack up your data seamlessly. 📌𝐌𝐚𝐭𝐫𝐢𝐱: Organize grids of information with Matrix data organization at its best. 📌𝐋𝐢𝐧𝐤𝐞𝐝 𝐋𝐢𝐬𝐭 𝐓𝐫𝐞𝐞: Combine the dynamic power of Linked Lists and Trees for versatile data handling. 📌𝐀𝐫𝐫𝐚𝐲: Keep it simple and organized with the straightforward efficiency of Arrays. 📌𝐇𝐞𝐚𝐩: Prioritize and process data efficiently with the robust functionality of Heaps. 📌𝐑𝐞𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐓𝐫𝐞𝐞: Maintain order with the self-balancing capability of Rebalanced Trees. 📌𝐋𝐢𝐧𝐤𝐞𝐝 𝐋𝐢𝐬𝐭: Enjoy dynamic and flexible data traversal with Linked Lists. 📌𝐒𝐩𝐚𝐫𝐬𝐞 𝐌𝐚𝐭𝐫𝐢𝐱: Optimize storage and processing with Sparse Matrices, perfect for large datasets. Whether you're a tech enthusiast or a data aficionado, these structures are your gateway to streamlined processing and enhanced performance. Let's elevate your data game and explore these essential tools together! 𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨, 𝐂𝐨𝐧𝐭𝐚𝐜𝐭: +91-8904229202 | [email protected] 𝐕𝐢𝐬𝐢𝐭: www.wininlifeacademy.com #DataStructures #TechInnovation #DataManagement #TechTips #BinaryTree #Stack #Matrix #LinkedListTree #Array #Heap #RebalancedTree #LinkedList #SparseMatrix #Bangalore #Chennai #Hyderabad

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📅 Mark your calendars for September 18th in #Vienna! At the #DataScience MeetUp, Andreas from Neo4j and Sebastian will talk about the relevance of #KnowledgeGraphs for Gen #AI, and explain why proper #GraphVisualization is critical for understanding your #data. We look forward to seeing you there! https://lnkd.in/e-EKZ9fZ

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🧠 Graph Neural Networks: The #Future of AI and Complex Data Processing GNNs are rapidly gaining attention and redefining how we handle and analyze complex, interconnected #data structures. From academic breakthroughs to real-world applications, GNNs deliver substantial performance improvements over traditional AI #models. From Facebook, Twitter, and Pinterest to Google and Uber, companies are leveraging the power of GNNs to solve complex problems that traditional #DeepLearning models struggle with. 🔍 Why GNNs Matter: 📚 Enhanced Data Representation: GNNs excel at modeling complex relationships in graph-structured data, such as social networks, molecular structures, and #knowledge graphs. ⚖ Scalability: Modern GNN architectures efficiently handle large-scale graphs, making them ideal for #enterprise-level solutions. 🔀 Versatility: Whether it’s node classification, link prediction, or graph-level tasks, GNNs offer a flexible framework adaptable to numerous #applications. 📊 Key Use Cases: 1️⃣ Social Media Networking & Analysis 2️⃣ Search Engines and AI Assistants 3️⃣ Drug Discovery and Molecular Biology 4️⃣ Materials Science & Protein Design 5️⃣ Cybersecurity Network Vulnerability and Intrusion Detection ⚙️ How GNNs Work: ♻ Node Embedding: GNNs generate vector representations for each node, capturing both node features and graph structure. 📨 Message Passing: Nodes exchange information with neighbors, iteratively refining their embeddings. 💡 Aggregation & Update: Nodes aggregate the information from neighbors, updating their states to capture local and global graph patterns. 👨‍💻 Readout: For graph-level tasks, node embeddings are aggregated into a graph representation used for classification, prediction, and more. 🔗 Dive deeper into the transformative world of #GraphNeuralNetworks in our latest blog! https://lnkd.in/gH2V9Txf #graphneuralnetworks #ai #machinelearning #datascience #gnn #innovation #futureofai #aipartner #aiconsultant #nextlevelai #genaipartner

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