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Evolution of Freebase and the Google Knowledge Graph

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Evolution and Demise of Freebase and the Google Knowledge Graph

Freebase was launched in 2005 as an open database of knowledge, initially populated with Wikipedia data and later incorporating crowdsourced updates. Acquired by Google in 2010, it was transitioned into Google's Knowledge Graph before being decommissioned in 2016. The schema contained around 1500 ty

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Unlock the power of Google for research with practical guidance on effective search strategies, understanding search engines, utilizing Google Scholar, accessing scholarly articles, and optimizing your search experience. Learn how to leverage Google's advanced features to enhance your research capab

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Introduction to Google App Engine - A Platform for Easy Application Development

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Implementing Google Hangout On Air Successfully

Google Hangouts is a free video chat service from Google that allows for group interactions and live broadcasting through "Hangouts on Air." To start, create a Google account, download the Hangout plugin, connect to YouTube, and begin your Hangout. Share event details, invite participants, and manag

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Explore the art of Google hacking, a method of utilizing advanced search operators and logic operators to uncover sensitive information online. From understanding Google hacking to diving into advanced search strategies and leveraging the Google Hacking Database (GHDB), discover how to enhance your

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Balanced graph edge partitioning is a crucial problem in graph computation, machine learning, and graph databases. It involves partitioning a graph's vertices or edges into balanced components while minimizing cut costs. This process is essential for various real-world applications such as iterative

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Query-Centric Framework for Big Graph Querying

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Introduction to Distributed Computing at Stanford University

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Introduction to Google's Pregel Distributed Analytics Framework

Google's Pregel is a large-scale graph-parallel distributed analytics framework designed for graph processing tasks. It offers high scalability, fault tolerance, and flexibility in expressing graph algorithms. Inspired by the Bulk Synchronous Parallel (BSP) model, Pregel operates in super-steps, ena

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Introduction to GraphLab: Large-Scale Distributed Analytics Engine

GraphLab is a powerful distributed analytics engine designed for large-scale graph-parallel processing. It offers features like in-memory processing, automatic fault-tolerance, and flexibility in expressing graph algorithms. With characteristics such as high scalability and asynchronous processing,

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Introduction to Spark: Lightning-Fast Cluster Computing

Spark is a parallel computing system developed at UC Berkeley that aims to provide lightning-fast cluster computing capabilities. It offers a high-level API in Scala and supports in-memory execution, making it efficient for data analytics tasks. With a focus on scalability and ease of deployment, Sp

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Data Processing and Analysis for Graph-Based Algorithms

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Unleashing the Potential of Google: Online Webinar Insights

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