Improving Data Preparation for Business Analytics Applying Technologies and Methods for Establishing Trusted Data Assets for More Productive Users
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Mapreduce-Based D ELT Framework to Address the Challenges of Geospatial Big Data
International Journal of Geo-Information Article MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data Junghee Jo 1,* and Kang-Woo Lee 2 1 Busan National University of Education, Busan 46241, Korea 2 Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-51-500-7327 Received: 15 August 2019; Accepted: 21 October 2019; Published: 24 October 2019 Abstract: The conventional extracting–transforming–loading (ETL) system is typically operated on a single machine not capable of handling huge volumes of geospatial big data. To deal with the considerable amount of big data in the ETL process, we propose D_ELT (delayed extracting–loading –transforming) by utilizing MapReduce-based parallelization. Among various kinds of big data, we concentrate on geospatial big data generated via sensors using Internet of Things (IoT) technology. In the IoT environment, update latency for sensor big data is typically short and old data are not worth further analysis, so the speed of data preparation is even more significant. We conducted several experiments measuring the overall performance of D_ELT and compared it with both traditional ETL and extracting–loading– transforming (ELT) systems, using different sizes of data and complexity levels for analysis. The experimental results show that D_ELT outperforms the other two approaches, ETL and ELT. In addition, the larger the amount of data or the higher the complexity of the analysis, the greater the parallelization effect of transform in D_ELT, leading to better performance over the traditional ETL and ELT approaches. Keywords: ETL; ELT; big data; sensor data; IoT; geospatial big data; MapReduce 1. -
A Survey on Data Collection for Machine Learning a Big Data - AI Integration Perspective
1 A Survey on Data Collection for Machine Learning A Big Data - AI Integration Perspective Yuji Roh, Geon Heo, Steven Euijong Whang, Senior Member, IEEE Abstract—Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research. Index Terms—data collection, data acquisition, data labeling, machine learning F 1 INTRODUCTION E are living in exciting times where machine learning expertise. This problem applies to any novel application that W is having a profound influence on a wide range of benefits from machine learning. -
Best Practices in Data Collection and Preparation: Recommendations For
Feature Topic on Reviewer Resources Organizational Research Methods 2021, Vol. 24(4) 678–\693 ª The Author(s) 2019 Best Practices in Data Article reuse guidelines: sagepub.com/journals-permissions Collection and Preparation: DOI: 10.1177/1094428119836485 journals.sagepub.com/home/orm Recommendations for Reviewers, Editors, and Authors Herman Aguinis1 , N. Sharon Hill1, and James R. Bailey1 Abstract We offer best-practice recommendations for journal reviewers, editors, and authors regarding data collection and preparation. Our recommendations are applicable to research adopting dif- ferent epistemological and ontological perspectives—including both quantitative and qualitative approaches—as well as research addressing micro (i.e., individuals, teams) and macro (i.e., organizations, industries) levels of analysis. Our recommendations regarding data collection address (a) type of research design, (b) control variables, (c) sampling procedures, and (d) missing data management. Our recommendations regarding data preparation address (e) outlier man- agement, (f) use of corrections for statistical and methodological artifacts, and (g) data trans- formations. Our recommendations address best practices as well as transparency issues. The formal implementation of our recommendations in the manuscript review process will likely motivate authors to increase transparency because failure to disclose necessary information may lead to a manuscript rejection decision. Also, reviewers can use our recommendations for developmental purposes to highlight which particular issues should be improved in a revised version of a manuscript and in future research. Taken together, the implementation of our rec- ommendations in the form of checklists can help address current challenges regarding results and inferential reproducibility as well as enhance the credibility, trustworthiness, and usefulness of the scholarly knowledge that is produced. -
The Risks of Using Spreadsheets for Statistical Analysis Why Spreadsheets Have Their Limits, and What You Can Do to Avoid Them
The risks of using spreadsheets for statistical analysis Why spreadsheets have their limits, and what you can do to avoid them Let’s go Introduction Introduction Spreadsheets are widely used for statistical analysis; and while they are incredibly useful tools, they are Why spreadsheets are popular useful only to a certain point. When used for a task 88% of all spreadsheets they’re not designed to perform, or for a task at contain at least one error or beyond the limit of their capabilities, spread- sheets can be somewhat risky. An alternative to spreadsheets This paper presents some points you should consider if you use, or plan to use, a spread- sheet to perform statistical analysis. It also describes an alternative that in many cases will be more suitable. The learning curve with IBM SPSS Statistics Software licensing the SPSS way Conclusion Introduction Why spreadsheets are popular Why spreadsheets are popular A spreadsheet is an attractive choice for performing The answer to the first question depends on the scale and calculations because it’s easy to use. Most of us know (or the complexity of your data analysis. A typical spreadsheet 1 • 2 • 3 • 4 • 5 think we know) how to use one. Plus, spreadsheet programs will have a restriction on the number of records it can 6 • 7 • 8 • 9 come as a standard desktop computer resource, so they’re handle, so if the scale of the job is large, a tool other than a already available. spreadsheet may be very useful. A spreadsheet is a wonderful invention and an An alternative to spreadsheets excellent tool—for certain jobs. -
Data Migration (Pdf)
PTS Data Migration 1 Contents 2 Background ..................................................................................................................................... 2 3 Challenge ......................................................................................................................................... 3 3.1 Legacy Data Extraction ............................................................................................................ 3 3.2 Data Cleansing......................................................................................................................... 3 3.3 Data Linking ............................................................................................................................. 3 3.4 Data Mapping .......................................................................................................................... 3 3.5 New Data Preparation & Load ................................................................................................ 3 3.6 Legacy Data Retention ............................................................................................................ 4 4 Solution ........................................................................................................................................... 5 4.1 Legacy Data Extraction ............................................................................................................ 5 4.2 Data Cleansing........................................................................................................................ -
Advanced Data Preparation for Individuals & Teams
WRANGLER PRO Advanced Data Preparation for Individuals & Teams Assess & Refine Data Faster FILES DATA VISUALIZATION Data preparation presents the biggest opportunity for organizations to uncover not only new levels of efciency but DATABASES REPORTING also new sources of value. CLOUD MACHINE LEARNING Wrangler Pro accelerates data preparation by making the process more intuitive and efcient. The Wrangler Pro edition API INPUTS DATA SCIENCE is specifically tailored to the needs of analyst teams and departmental use cases. It provides analysts the ability to connect to a broad range of data sources, schedule workflows to run on a regular basis and freely share their work with colleagues. Key Benefits Work with Any Data: Users can prepare any type of data, regardless of shape or size, so that it can be incorporated into their organization’s analytics eforts. Improve Efciency: Make the end-to-end process of data preparation up to 10X more efcient compared to traditional methods using excel or hand-code. By utilizing the latest techniques in data visualization and machine learning, Wrangler Pro visibly surfaces data quality issues and guides users through the process of preparing their data. Accelerate Data Onboarding: Whether developing analysis for internal consumption or analysis to be consumed by an end customer, Wrangler Pro enables analysts to more efciently incorporate unfamiliar external data into their project. Users are able to visually explore, clean and join together new data Reduce data Improve data sources in a repeatable workflow to improve the efciency and preparation time quality and quality of their work. by up to 90% trust in analysis WRANGLER PRO Why Wrangler Pro? Ease of Deployment: Wrangler Pro utilizes a flexible cloud- based deployment that integrates with leading cloud providers “We invested in Trifacta to more efciently such as Amazon Web Services. -
Preparation for Data Migration to S4/Hana
PREPARATION FOR DATA MIGRATION TO S4/HANA JONO LEENSTRA 3 r d A p r i l 2 0 1 9 Agenda • Why prepare data - why now? • Six steps to S4/HANA • S4/HANA Readiness Assessment • Prepare for your S4/HANA Migration • Tips and Tricks from the trade • Summary ‘’Data migration is often not highlighted as a key workstream for SAP S/4HANA projects’’ WHY PREPARE DATA – WHY NOW? Why Prepare data - why now? • The business case for data quality is always divided into either: – Statutory requirements – e.g., Data Protection Laws, Finance Laws, POPI – Financial gain – e.g. • Fixing broken/sub optimal business processes that cost money • Reducing data fixes that cost time and money • Sub optimal data lifecycle management causing larger than required data sets, i.e., hardware and storage costs • Data Governance is key for Digital Transformation • IoT • Big Data • Machine Learning • Mobility • Supplier Networks • Employee Engagement • In summary, for the kind of investment that you are making in SAP, get your housekeeping in order Would you move your dirt with you? Get Clean – Data Migration Keep Clean – SAP Tools Data Quality takes time and effort • Data migration with data clean-up and data governance post go-live is key by focussing on the following: – Obsolete Data – Data quality – Master data management (for post go-live) • Start Early – SAP S/4 HANA requires all customers and vendors to be maintained as a business partner – Consolidation and de-duplication of master data – Data cleansing effort (source vs target) – Data volumes: you can only do so much -
Preparing a Data Migration Plan a Practical Introduction to Data Migration Strategy and Planning
Preparing a Data Migration Plan A practical introduction to data migration strategy and planning April 2017 2 Introduction Thank you for downloading this guide, which aims to help with the development of a plan for a data migration. The guide is based on our years of work in the data movement industry, where we provide off-the- shelf software and consultancy for organisations across the world. Data migration is a complex undertaking, and the processes and software used are continually evolving. The approach in this guide incorporates data migration best practice, with the aim of making the data migration process a little more straightforward. We should start with a quick definition of what we mean by data migration. The term usually refers to the movement of data from an old or legacy system to a new system. Data migration is typically part of a larger programme and is often triggered by a merger or acquisition, a business decision to standardise systems, or modernisation of an organisation’s systems. The data migration planning outlined in this guide dovetails neatly into the overall requirements of an organisation. Don’t hesitate to get in touch with us at [email protected] if you have any questions. Did you like this guide? Click here to subscribe to our email newsletter list and be the first to receive our future publications. www.etlsolutions.com 3 Contents Project scoping…………………………………………………………………………Page 5 Methodology…………………………………………………………………………….Page 8 Data preparation………………………………………………………………………Page 11 Data security…………………………………………………………………………….Page 14 Business engagement……………………………………………………………….Page 17 About ETL Solutions………………………………………………………………….Page 20 www.etlsolutions.com 4 Chapter 1 Project Scoping www.etlsolutions.com 5 Preparing a Data Migration Plan | Project Scoping While staff and systems play an important role in reducing the risks involved with data migration, early stage planning can also help. -
Analyzing the Web from Start to Finish Knowledge Extraction from a Web Forum Using KNIME
Analyzing the Web from Start to Finish Knowledge Extraction from a Web Forum using KNIME Bernd Wiswedel [email protected] Tobias Kötter [email protected] Rosaria Silipo [email protected] Copyright © 2013 by KNIME.com AG all rights reserved page 1 Table of Contents Analyzing the Web from Start to Finish Knowledge Extraction from a Web Forum using KNIME ..................................................................................................................................................... 1 Summary ................................................................................................................................................. 3 Web Analytics and the Desire for Extra-Knowledge ............................................................................... 3 The KNIME Forum ................................................................................................................................... 4 The Data .............................................................................................................................................. 5 The Analysis ......................................................................................................................................... 6 The “WebCrawler” Workflow .................................................................................................................. 7 The HtmlParser Node from Palladian .................................................................................................. 7 The -
Automating Exploratory Data Analysis Via Machine Learning: an Overview
Tutorials SIGMOD ’20, June 14–19, 2020, Portland, OR, USA Automating Exploratory Data Analysis via Machine Learning: An Overview Tova Milo Amit Somech Tel Aviv University Tel Aviv University [email protected] [email protected] ABSTRACT up-close, better understand its nature and characteristics, Exploratory Data Analysis (EDA) is an important initial step and extract preliminary insights from it. Typically, EDA is for any knowledge discovery process, in which data scien- done by interactively applying various analysis operations tists interactively explore unfamiliar datasets by issuing a (such as filtering, aggregation, and visualization) – the user sequence of analysis operations (e.g. filter, aggregation, and examines the results of each operation, and decides if and visualization). Since EDA is long known as a difficult task, which operation to employ next. requiring profound analytical skills, experience, and domain EDA is long known to be a complex, time consuming pro- knowledge, a plethora of systems have been devised over cess, especially for non-expert users. Therefore, numerous the last decade in order to facilitate EDA. lines of work were devised to facilitate the process, in multi- In particular, advancements in machine learning research ple dimensions [10, 24, 25, 31, 42, 46]: First, simplified EDA 1 have created exciting opportunities, not only for better fa- interfaces (e.g., [25], Tableau ) allow non-programmers to cilitating EDA, but to fully automate the process. In this effectively explore datasets without knowing scripting lan- tutorial, we review recent lines of work for automating EDA. guages or SQL. Second, numerous solutions (e.g., [6, 14, 22]) Starting from recommender systems for suggesting a single were devised to improve the interactivity of EDA, by re- exploratory action, going through kNN-based classifiers and ducing the running times of exploratory operations, and active-learning methods for predicting users’ interestingness displaying preliminary sketches of their results. -
Web Data Integration: a New Source of Competitive Advantage
Web Data Integration: A new source of competitive advantage An Ovum white paper for Import.io Publication Date: 29 January 2019 Author: Tony Baer Web Data Integration: A new source of competitive advantage Summary Catalyst Web data provides key indicators into a company’s competitive landscape. By showing the public face of how rivals position themselves and providing early indicators of changing attitudes, sentiments, and interests, web data complements traditional enterprise data in helping companies stay updated on their competitive challenges. The difficulty is that many organizations lack a full understanding of the value of web data, what to look for, and how to manage it so that it can live up to its promise of complementing enterprise data to provide fresh, timely views of a company’s competitive landscape. Ovum view Web Data Integration applies the discipline and automation normally associated with conventional enterprise data management to web data. Web Data Integration makes web data a valuable resource for the organization seeking to understand their competitive position, or understand key challenges in their market, such as the performance of publicly traded companies or consumer perceptions and attitudes towards products. Web Data Integration ventures beyond traditional web scraping by emulating the workings of a modern browser, extracting data not otherwise accessible from the simple parsing of HTML documents. Automation and machine learning are essential to the success of Web Data Integration. Automation allows the workflows for extracting, preparing, and integrating data to be orchestrated, reused, and consistently monitored. Machine Learning (ML) allows non-technical users to train an extractor for a specific website without having to write any code. -
The Landscape of R Packages for Automated Exploratory Data Analysis by Mateusz Staniak and Przemysław Biecek
CONTRIBUTED RESEARCH ARTICLE 1 The Landscape of R Packages for Automated Exploratory Data Analysis by Mateusz Staniak and Przemysław Biecek Abstract The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of fifteen popular R packages to identify the parts of the analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development. Introduction With the advent of tools for automated model training (autoML), building predictive models is becoming easier, more accessible and faster than ever. Tools for R such as mlrMBO (Bischl et al., 2017), parsnip (Kuhn and Vaughan, 2019); tools for python such as TPOT (Olson et al., 2016), auto-sklearn (Feurer et al., 2015), autoKeras (Jin et al., 2018) or tools for other languages such as H2O Driverless AI (H2O.ai, 2019; Cook, 2016) and autoWeka (Kotthoff et al., 2017) supports fully- or semi-automated feature engineering and selection, model tuning and training of predictive models. Yet, model building is always preceded by a phase of understanding the problem, understanding of a domain and exploration of a data set.