Big data analysis and modelling
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Title: Big data analysis and modelling Lesson: Evaluation of Computer Systems Professor: Dr. Fatemi Presenter: Keivan Mahdavi University of Isfahan
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For example, today it takes any pharmaceutical company upto 12 years and $2 billion to bring a single new drug to market. Despite the huge spend, scientists in Pharma don’t have a way to find the data on the work which is already done. They just redo the whole thing, wasting money on duplicate work.
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Video of the presentation: https://nuigalway.mediaspace.kaltura.com/media/OSW2021A+OSCG+Open+Scholarship+Prize+-+The+Final!/1_d7ekd3d3/121659351#t=56:08
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Assigning semantic labels to data sources
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Giraph++: From "Think Like a Vertex" to "Think Like a Graph"
To meet the challenge of processing rapidly growing graph and
network data created by modern applications, a number of distributed graph processing systems have emerged, such as Pregel and GraphLab. All these systems divide input graphs into partitions, and employ a “think like a vertex” programming model to support iterative graph computation. This vertex-centric model is easy to program and has been proved useful for many graph algorithms. However, this model hides the partitioning information from the users, thus prevents many algorithm-specific optimizations. This often results in longer execution time due to excessive network messages (e.g. in Pregel) or heavy scheduling overhead to ensure data consistency (e.g. in GraphLab). To address this limitation, we
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Introduction_OF_Hadoop_and_BigData
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. It has become a business strategy for leveraging information resources generated by social media, scientific instruments, mobile devices, sensors, and networks. While more data can be collected than ever before, the challenges lie in managing, analyzing, summarizing, visualizing, and discovering knowledge from the data in a timely and scalable way. Hadoop is an open-source software framework that addresses these challenges through distributed storage and processing of large datasets across clusters of computers using simple programming models. It provides reliable storage of data via its Hadoop Distributed File System and scalable processing of that data using the MapReduce programming model.
Statistics
This document provides an introduction to statistics and key statistical concepts. It defines statistics as the collection, organization, analysis and presentation of numerical data to make meaningful predictions. It discusses how data can be collected from entire populations or samples, and distinguishes between raw and secondary data. It introduces common statistical tools like frequency distribution tables, grouped frequency tables, measures of central tendency (mean, median, mode), graphical representations (bar graphs, histograms, frequency polygons), and class marks.
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1) The document discusses using linear regression on 1 terabyte of data by leveraging Amazon Web Services' free tier and distributed computing algorithms in Python and R.
2) It notes the challenges of going beyond linear models with big data, including better prediction and real-time analytics.
3) A proposed solution is "universal association discovery" to find relationships between random variables regardless of form using functions on observation graphs, though this approach currently only works for continuous variables.
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Understandung Firebird optimizer, by Dmitry Yemanov (in English)
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Giraph++: From "Think Like a Vertex" to "Think Like a Graph"
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Linear regression on 1 terabytes of data? Some crazy observations and actions
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The document discusses clustering high dimensional data using an efficient approach called "Big Data Clustering using k-Mediods BAT Algorithm" (KMBAT). KMBAT simultaneously considers all data points as potential exemplars and exchanges real-valued messages between data points until a high-quality set of exemplars and corresponding clusters emerges. It is demonstrated on Facebook user profile data stored in an HDInsight Hadoop cluster. KMBAT finds better clustering solutions than other methods in less time for high dimensional big data.
Big Data & DS Analytics for PAARL
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The document discusses open government data portals and their evolution from initial publishing of data to supporting reuse and impact. It describes the key stages in developing portals, including the first portal launched over 13 years ago and the current European data portal. The document outlines work done to support the entire data value chain, analyze portal usage, develop guidelines to make portals more user-centric, and measure their effectiveness in promoting reuse. Examples are provided for how portals can better organize data, promote reuse, and co-locate documentation to support users.
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Africa Information Highway Live Exchange #Session 7
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The AIH Live Exchange between the Africa Information Highway Team, partners and countries is a free monthly webinar hosted by the African Development Bank to discuss topics related to government data and statistics. This webinar series is the main platform for countries to share their experiences and best practices around open data including using their Open Data Platform of the AIH.
This session is co-organized with the Luxembourg Institute of Science and Technology (LIST) which is a mission-driven Research and Technology Organization (RTO) that develops advanced technologies and delivers innovative products and services to industry and society. These innovations can also be used to solve several societal challenges, particularly in the areas of the environment, security, education and culture, sustainable development, as well as the efficient use of resources.
Official statistical data are recognized as high-value datasets for the society and economy, to enrich research, inform decision making or develop new products and services. The use of these authoritative data sources contributes to building a society with more empowered people, better policies, more effective and accountable decision-making, greater participation and stronger democratic mechanisms.
Official statistics are produced to be used and re-used to make an impact on society through a higher degree of openness and transparency while ensuring confidentiality and, at the same time, providing equal access to information to citizens.
The value of data lies in its use and re-use. In this interactive webinar, you will learn new techniques to improve the use and re-use of your statistical data, going beyond the provision logic and adopting the ecosystem mindset. You will:
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This session is an opportunity for Regional members countries to '' Sharpen their skills in making data used and re-used by developing an ecosystem mindset to effectively build sustainable community of users around their Open Data Platform thus promoting transparency and better decision-making”
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--The webinar was held as part of ASIRA (Access to Scientific Information Resources in Agriculture) Online Course for Low-Income Countries--
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Let me share the Beginner's Guide to Data Science which will be really helpful to you.
Also Checkout: http://bit.ly/2Mub6xP
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DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION
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phd research proposal should be written in such a way that it makes a positive and powerful first impression about your potential to become a good researcher and allows the university to assess whether you are a good match for the mentors or supervisors and their areas of research expertise.
Check out the scope for future research proposal topics in big data 2023 - https://rb.gy/6yoy0
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ppt1.pptx
This document proposes a theme on big data analytics research. It motivates the importance of big data due to the exponential growth of digital data and limitations of traditional databases. The power of big data analytics is discussed through its wide applications in health, policymaking, smart cities, education and robotics. The objectives are outlined as large-scale machine learning, distributed computing, theory development, and multi-disciplinary analytics. Hong Kong is well positioned for this research due to its institutions, industries and potential collaborators. A multi-university and interdisciplinary approach is advocated to tackle big data challenges and transform society through new technologies, applications, insights and knowledge.
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About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
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Survey of the Euro Currency Fluctuation by Using Data Mining
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
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This document proposes a theme on big data analytics research. It notes that the world's data storage capacity doubles every 40 months and discusses how big data can provide value across many areas like health, policymaking, education and more. The proposal recommends that Hong Kong develop a state-of-the-art big data platform to make a difference in areas like smart cities and support aging populations. It outlines objectives like large-scale machine learning from big data and discusses how Hong Kong is well-positioned for this research with experts across universities and potential collaborators in industry. The expected outcomes include new methodologies, applications impacting society and industry, and educational programs to cultivate big data leaders.
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Big data analysis and modelling
- 1. Hello! I am Keivan Mahdavi You can find me at: @kvnmahdavi Course: Evaluation of Computer Systems Professor: Dr. Fatemi Year: 2017
- 5. What is Big Data? ○ Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. http://www.gartner.com/it-glossary/big-data
- 8. Data analysis ○ The most important phase in the value chain of big data, with the purpose of extracting useful values, providing suggestions or decisions.
- 9. Traditional Data Analysis Means to use proper statistical methods to analyze massive first-hand data, to concentrate, extract, and refine useful data hidden in a batch of chaotic data, and to identify the inherent law of the subject matter, so as to develop functions of data to the greatest extent and maximize the value of data.
- 10. Several representative traditional data analysis methods ○ Cluster Analysis ○ Factor Analysis ○ Correlation Analysis ○ Regression Analysis ○ A/B Testing ○ Statistical Analysis ○ Data Mining
- 11. “Big Data Analytic Methods” how to rapidly extract key information ○ Bloom Filter ○ Hashing ○ Index ○ Triel ○ Parallel Computing
- 12. Architecture for Big Data Analysis ○ Real-Time vs. Offline Analysis ○ Analysis at Different Levels ○ Analysis with Different Complexity
- 13. Tools for Big Data Mining and Analysis What Analytics, Data mining, Big Data software you used in the past 12 months for a real project” of 798 professionals made by KDNuggets in 2012 ○ R (30.7%) ○ Excel (29.8%) ○ Rapid Rapidminer (26.7%) ○ KNIME (21.8%) ○ Weka (14.8%)
- 14. 3. Example Global Climate Change Studying Based on Big Data Analysis of Antarctica
- 16. Elementary Data
- 17. ArcGIS software ArcGIS is a geographic information system for working with maps and geographic information. It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and ... ○Developer(s): Esri ○License: Proprietary commercial software ○Written in: C++ ○Stable release: 10.5 / December 15, 2016; 4 months ago ○Initial release: December 27, 1999; 17 years ago Wikipedia
- 18. Represent the situation ○ use ArcGIS software to divide Antarctica into 15 regions based on data and within each region including a site.
- 20. Model Building ○ Express the relationship between: temperature, pressure and vapor pressure with the weighted average temperature
- 21. Model Building ○ Calculate saturation vapor pressure ○ Relation of saturation vapor pressure and vapor pressure is following:
- 22. Fitting
- 23. 4. Conclusion
- 24. “ Big Data Analysis traditional data analysis methods What is Big Data? Architecture & Tools Example how to rapidly extract key information
- 25. Thanks! Any questions? You can find me at @kvnmahdavi
- 26. Critical analysis of Big Data challenges and analytical methods, 2016 Uthayasankar Sivarajah, , Muhammad Mustafa Kamal , Zahir Irani , Vishanth Weerakkody Big Data Related Technologies, Challenges and Future Prospects, 2014 Chapter 5: Big Data Analysis - Pages 51-58 Authors: Chen, M., Mao, S., Zhang, Y., Leung, V.C. Proceedings of the Fourth International Forum on Decision Sciences, 2017 Global Climate Change Studying Based on Big Data Analysis of Antarctica - Pages 39-45 Xiang Li Xiaofeng Xu http://www.gartner.com https://www.wikipedia.org