DATAWAREHOUSE.pptx
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Knowledge discovery in databases involves the non-trivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze exponentially increasing data volumes. Knowledge discovery aims to extract useful and certain knowledge efficiently from data through approaches related to databases, statistics, and scientific discovery. It has various applications in fields like science, business, marketing, investment, and fraud detection.
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Knowledge discovery in databases involves the nontrivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze exponentially increasing data volumes. Knowledge discovery aims to extract useful and certain knowledge efficiently from data through approaches related to databases, statistics, and scientific discovery. It has various applications in fields like science, marketing, investment, and fraud detection.
Knowledge Discovery in Databases
Knowledge discovery in databases involves the non-trivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze growing data volumes and extract useful knowledge. Knowledge discovery aims to find certain, interesting, and efficient patterns in data and is related to approaches like database management, expert systems, statistics, and scientific discovery. It has many applications in fields such as science, marketing, investments, and fraud detection.
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The document provides an overview of data science, big data, data mining, and data mining techniques. It defines data science as a multi-disciplinary field that uses scientific methods to extract knowledge from structured and unstructured data. Big data is described as large, diverse datasets that are too large for traditional databases to handle. Common data mining tasks like prediction, classification, clustering and association rule mining are summarized. Finally, specific techniques like decision trees, k-means clustering, and association rule mining are overviewed.
intro to data science Clustering and visualization of data science subfields ...
This document provides an introduction to the field of data science. It defines data science as an interdisciplinary field that uses scientific methods and processes to extract knowledge and insights from large amounts of structured and unstructured data. The document discusses what data science is, why it has grown in importance recently due to massive data collection and computing power, and what skills and roles are involved in data science work. It also presents models of the data science process and team composition.
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"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.
Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
To convert data to knowledge, a convergence of knowledge management, information architecture, and data science is necessary. This is called knowledge architecture. Knowledge architecture involves designing and implementing an organization's intellectual infrastructure to capture, organize, analyze, and utilize information. It transforms information into knowledge through applying context. NASA faces challenges with large amounts of growing and siloed data. Opportunities for knowledge architecture at NASA include improving search capabilities, developing metadata standards, using analytics and visualization, and creating a lessons learned knowledge graph. This could help NASA make better decisions by leveraging past knowledge and reducing waste.
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dbms.pdf
Knowledge discovery in databases involves the nontrivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze exponentially increasing data volumes. Knowledge discovery aims to extract useful and certain knowledge efficiently from data through approaches related to databases, statistics, and scientific discovery. It has various applications in fields like science, marketing, investment, and fraud detection.
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Knowledge discovery in databases involves the non-trivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze growing data volumes and extract useful knowledge. Knowledge discovery aims to find certain, interesting, and efficient patterns in data and is related to approaches like database management, expert systems, statistics, and scientific discovery. It has many applications in fields such as science, marketing, investments, and fraud detection.
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Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
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Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
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Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
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Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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DATAWAREHOUSE.pptx
- 1. Knowledge Discovery in Databases By: Diwas Kandel- 17618843 Type B
- 2. Knowledge Discovery • Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful, information from data. • Exponentially increasing data/information • Hard to analyse the data due to its increasing volume.
- 3. Knowledge Discovery • It has been estimated that the amount of information in the world doubles every 20 months. • Characteristics of Knowledge Discovery • Certainty • Interesting • Efficiency
- 4. Comparison of various terms
- 5. RelatedApproaches • Database management • ExpertSystems • Statistics • Scientific Discovery
- 6. Need of KDD • There is an urgent need for a new generation of computational theories and tools to assist humans in Extracting useful Information (knowledge) from the rapidly growing volumes of digital data. • Use in various fields such as science and businesses • Marketing • Investment • Fraud Detection • Telecommunications
- 7. Review of Papers • Norton, M. J. (1999). Knowledge discovery in databases. Library Trends, 48(1), 9-21. • Fayyad,U., Piatetsky-Shapiro,G., & Smyth, P.(1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37. • Frawley,W.J., Piatetsky-Shapiro,G., & Matheus,C. J. (1992). Knowledge discovery in databases:An overview. AI magazine, 13(3), 57.