PAC 2019 virtual Srivalli Aparna

The document discusses an orientation program on data mining using R programming. It covers various topics related to data science including data analysis, data mining, R programming, and basic R commands. Some key points: - It discusses the differences between data, information, and knowledge. Data is processed to get information, and information combined with experience leads to knowledge. - The steps in data analysis are explained as collect, clean, organize, explore, and model data to get insights and make decisions. - The objectives and roles of R programming in data science are discussed. R is a popular language for statistical computing and data analysis. - Basic R commands for vectors, importing/exporting CSV files, and coercion

will see the small description of machine learning on an easy slide.For an any kind of presentation on ML we can have a small bunch of knowledge.

Slides covered during Analytics Boot Camp conducted with the help of IBM, Venturesity. Special credits to Kumar Rishabh (Google) and Srinivas Nv Gannavarapu (IBM)

This document provides an overview of topics to be covered in R Programming including variables, data types, data import/export, logical statements, loops, functions, data plotting and visualization, and basic statistical functions and packages. It then goes on to introduce R, explaining that it is a programming language for statistical analysis and graphical display. It discusses why R is useful for data analysis and exploration due to its large collection of tools, ability to handle big data, and open source community support. The document also covers installing R and RStudio, defining variables, common data types like vectors, matrices, arrays, lists and data frames, and basic operations and control structures like if/else statements and loops.

Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot. Level: Fundamental Requirements: One should have some knowledge of programming and some statistics.

Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.

Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data. A talk given by Julian Hyde at DataWorks Summit, San Jose, on June 14th 2017.

Deep Reinforcement Learning (DRL) is a thriving area in the current AI battlefield. AlphaGO by DeepMind is a very successful application of DRL which has drawn the attention of the entire world. Besides playing games, DRL also has many practical use in industry, e.g. autonomous driving, chatbots, financial investment, inventory management, and even recommendation systems. Although DRL applications has something in common with supervised Computer Vision or Natural Language Processing tasks, they are unique in many ways. For example, they have to interact (explore) with the environment to obtain training samples along the optimization, and the method to improve the model is usually different from common supervised applications. In this talk we will share our experience of building Deep Reinforcement Learning applications on BigDL/Spark. BigDL is a well-developed deep learning library on Spark which is handy for Big Data users, but it has been mostly used for supervised and unsupervised machine learning. We have made extensions particularly for DRL algorithms (e.g. DQN, PG, TRPO and PPO, etc.), implemented classical DRL algorithms, built applications with them and did performance tuning. We are happy to share what we have learnt during this process. We hope our experience will help our audience learn how to build a RL application on their own for in their production business.

Matplotlib adalah pustaka plotting 2D Python yang menghasilkan gambar berkualitas publikasi dalam berbagai format cetak dan lingkungan interaktif di berbagai platform.

Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data. A talk given by Julian Hyde at Apache: Big Data, Miami, on May 16th 2017.

Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data.

This document discusses JSON and NoSQL databases. It provides an overview of JSON, including its use for serializing data objects and storing semi-structured data. It also discusses some key features of NoSQL databases, including flexible schemas, quicker setup times, massive scalability, and relaxed consistency compared to traditional relational databases. The document uses MongoDB as an example NoSQL database and highlights its use of collections and documents similar to tables and rows in relational databases.

This document provides an introduction and overview of Neo4j, a graph database. It discusses trends in big data, NoSQL databases, and different types of NoSQL databases like key-value stores, column family databases, and document databases. It then defines what a graph and graph database are, and introduces Neo4j as a native graph database that uses a property graph model. It outlines some of Neo4j's features and provides examples of how it can be used to represent social network, spatial, and interconnected data.

This document provides an overview of evaluation measures for information retrieval systems. It discusses why evaluation is important for improving systems and measuring user satisfaction. Key points include: - Common set-based measures include recall, precision, and F-measure. Ranked retrieval measures include average precision (AP), normalized discounted cumulative gain (nDCG), expected reciprocal rank (ERR), and Q-measure for graded relevance. - Measures for diversified search aim to balance relevance and diversity across different user intents. Examples given include α-nDCG, ERR-IA, D#-nDCG, and U-IA. - Statistical significance testing allows determining whether differences between systems are likely real or due to chance. The t

Travis Oliphant, author of NumPy, presents an introduction into NumPy and SciPy tools for statistical analysis including scipy.stats.

It’s a data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand. Weka is a collection of machine learning algorithms for data mining tasks. Weka is open source software issued under the GNU General Public License

This document discusses machine learning algorithms in R. It provides an overview of machine learning, data science, and the 5 V's of big data. It then discusses two main machine learning algorithms - clustering and classification. For clustering, it covers k-means clustering, providing examples of how to implement k-means clustering in R. For classification, it discusses decision trees, K-nearest neighbors (KNN), and provides an example of KNN classification in R. It also provides a brief overview of regression analysis, including examples of simple and multiple linear regression in R.

The document discusses continuous performance optimization of Java applications. It proposes adding an optimization step to the continuous integration pipeline to evolve performance testing beyond just finding regressions. This would allow configurations to be adapted to new application features and releases to find performance improvements. The approach is demonstrated on a flight search microservice, where different garbage collection algorithms and configuration parameters are evaluated to optimize throughput, response times, memory usage and stability under increasing load.

The document discusses the concept of observability in performance engineering and its importance for understanding application performance. It defines observability as watching application behavior using response metrics and resource utilization metrics to understand the digital user experience. The document provides examples of integrating load testing tools with application performance monitoring tools to actively monitor applications in production and observe performance across releases. It emphasizes the need to analyze raw metrics from multiple perspectives to gain useful insights.

This document discusses measuring and addressing CPU throttling in containerized environments. It describes how CPU limits work at the kernel level and how to measure throttling using cgroup metrics. The author proposes adding 1.3 times the maximum throttled CPUs to the container's CPU limit to eliminate throttling. A case study shows this approach reduced response times and garbage collection pauses. The document also discusses how the JVM can increase demand as CPU limits increase and the importance of tailoring limits to workloads.

This document discusses using Keptn to automate service level indicator (SLI) evaluation and performance validation with service level objectives (SLOs). It describes two use cases: 1) automating SLI evaluation over a timeframe, and 2) integrating performance validation as a self-service capability. The document outlines how Keptn works underneath, including defining SLIs and SLOs in YAML and scoring SLIs against SLO criteria. It demonstrates integrating Keptn with existing pipelines and monitoring tools. Finally, it discusses options for installing only the Keptn quality gate functionality or the full Keptn platform.