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Developing successful multimedia systems is challenging as it involves integrating different media types into a coherent framework. Continuous media like video require large storage and bandwidth while automatic analysis of audio, image and video content is difficult. Multimedia computing draws from many areas and requires complex algorithms and efficient hardware. Example multimedia systems at MIT's Project Athena included applications for real estate, navigation, learning and photos sharing. Key techniques in multimedia include data compression, processing and analysis, delivery over networks, and database indexing and retrieval.
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2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
Science Big, Science Connected
The document discusses many topics related to informatics, data science, and collective intelligence on the web. It touches on data storage and management, open data initiatives like 1000 Genomes, challenges of data volume, distributed and networked computing models, harnessing collective intelligence through social networks and communities, and architectural principles for data-driven platforms that enable data collection, analysis, and reuse through visualization and mashups.
Information_Systems
The document discusses the role of databases in information systems. It begins by describing how businesses kept records before computers using paper filing cabinets. It then explains key database concepts like fields, records, and files. Databases store data in an organized way and make it accessible to information systems. Relational databases allow data to be accessed and shared through SQL. Databases underlie modern information systems and store both structured and unstructured data. New types of databases like NoSQL have emerged to handle big data from a variety of sources. Overall, databases play a fundamental role in information systems by organizing data to support business operations, decision making, and analytics.
DIP Using Image Encryption and XOR Operation Affine Transform
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
B017250715
This document discusses digital image processing and image encryption techniques. It provides an overview of digital image processing, describing how images are defined and processed digitally. It then discusses various purposes of image processing like visualization, sharpening, recognition, and encryption. The document outlines different categories of image encryption methods, including position permutation, value transformation, and position-substitution techniques. It provides examples of specific encryption algorithms that fall under each category, such as Arnold transform and digital signatures. Finally, it discusses using XOR operations and affine transforms for image encryption.
Deep dive in to modern it technology
This document provides an overview of Viknaraj's background and expertise in modern IT technologies. Viknaraj has over 17 years of experience in IT and worked in network engineering for over 11 years. He specializes in Microsoft products including servers, cloud infrastructure, Office 365, and virtualization. The document then provides brief 1-2 sentence definitions of key technologies including cloud computing, virtualization, artificial intelligence, internet of things, blockchain, machine learning, big data, image processing, and video processing. It concludes by inviting questions and discussion and providing Viknaraj's contact information.
Redundancy avoidance for big data in data centers a conventional neural netwo...
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www.nexgenproject.com
Interaction Paradigms
Introduction to interaction paradigms in computational devices.
Presentation by Sigrun Ritter, Sebastian Weinhold
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3. Satellite images play a major role in event detection like changing landscapes, monitoring glaciers, and detecting disasters. The project aims to detect land changes over time, store and classify the data, and retrieve it using defined mechanisms.
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Introduction
- 1. Introduction Principles of Multimedia Systems Sethserey SAM Computer Science Department
- 2. `
- 5. Multimedia is Multidisciplinary Image, audio, speech processing Computer vision, pattern recognition Computer networks, operating system Human computer interaction Computer graphics Multimedia computing
- 6. Example Multimedia Systems Real Estate in MIT’s Project Athena
- 7. Example Multimedia Systems Chronoscope in MIT’s Project Athena
- 8. Example Multimedia Systems Navigation Learning Environment in MIT’s Project Athena
- 9. Example Multimedia Systems Photosynth of Microsoft Live Labs.
- 11. Data Compression Raw image takes about 6M bytes (without header information) 24k bytes with jpeg, Q=50
- 12. “JPEG” Illustration r g b Transform to frequency domain Quantization Lossless compression … 0010001 …. decoder r’ g’ b’ u v
- 14. Tracking Object by Image Matching
- 15. Multiple Object Tracking Toys Squash Lab Double Squash
- 16. Finding Objects in Clutter
- 17. Object Recognition David G. Lowe, "Object recognition from local scale-invariant features," ICCV99.
- 18. Posture Detection in Images Template Image
- 19. Dealing with Multiple Objects
- 21. Finding Action Clusters in Image Database
- 22. Finding Action Clusters (con’t)
- 23. Action Recognition video1 video2 Ramanan, D., Forsyth, D. A., Zisserman, A. " Strike a Pose: Tracking People by Finding Stylized Poses. " Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005. video3
- 27. Q & A