A Survey on Data Collection for Machine Learning a Big Data - AI Integration Perspective
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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. -
Synthpop: Bespoke Creation of Synthetic Data in R
synthpop: Bespoke Creation of Synthetic Data in R Beata Nowok Gillian M Raab Chris Dibben University of Edinburgh University of Edinburgh University of Edinburgh Abstract In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example. Keywords: synthetic data, disclosure control, CART, R, UK Longitudinal Studies. This introduction to the R package synthpop is a slightly amended version of Nowok B, Raab GM, Dibben C (2016). synthpop: Bespoke Creation of Synthetic Data in R. Journal of Statistical Software, 74(11), 1-26. doi:10.18637/jss.v074.i11. URL https://www.jstatsoft. org/article/view/v074i11. 1. Introduction and background 1.1. Synthetic data for disclosure control National statistical agencies and other institutions gather large amounts of information about individuals and organisations. Such data can be used to understand population processes so as to inform policy and planning. The cost of such data can be considerable, both for the collectors and the subjects who provide their data. Because of confidentiality constraints and guarantees issued to data subjects the full access to such data is often restricted to the staff of the collection agencies. Traditionally, data collectors have used anonymisation along with simple perturbation methods such as aggregation, recoding, record-swapping, suppression of sensitive values or adding random noise to prevent the identification of data subjects. -
Bootstrapping Regression Models
Bootstrapping Regression Models Appendix to An R and S-PLUS Companion to Applied Regression John Fox January 2002 1 Basic Ideas Bootstrapping is a general approach to statistical inference based on building a sampling distribution for a statistic by resampling from the data at hand. The term ‘bootstrapping,’ due to Efron (1979), is an allusion to the expression ‘pulling oneself up by one’s bootstraps’ – in this case, using the sample data as a population from which repeated samples are drawn. At first blush, the approach seems circular, but has been shown to be sound. Two S libraries for bootstrapping are associated with extensive treatments of the subject: Efron and Tibshirani’s (1993) bootstrap library, and Davison and Hinkley’s (1997) boot library. Of the two, boot, programmed by A. J. Canty, is somewhat more capable, and will be used for the examples in this appendix. There are several forms of the bootstrap, and, additionally, several other resampling methods that are related to it, such as jackknifing, cross-validation, randomization tests,andpermutation tests. I will stress the nonparametric bootstrap. Suppose that we draw a sample S = {X1,X2, ..., Xn} from a population P = {x1,x2, ..., xN };imagine further, at least for the time being, that N is very much larger than n,andthatS is either a simple random sample or an independent random sample from P;1 I will briefly consider other sampling schemes at the end of the appendix. It will also help initially to think of the elements of the population (and, hence, of the sample) as scalar values, but they could just as easily be vectors (i.e., multivariate). -
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........................................................................................................................ -
A First Step Into the Bootstrap World
INSTITUTE FOR DEFENSE ANALYSES A First Step into the Bootstrap World Matthew Avery May 2016 Approved for public release. IDA Document NS D-5816 Log: H 16-000624 INSTITUTE FOR DEFENSE ANALYSES 4850 Mark Center Drive Alexandria, Virginia 22311-1882 The Institute for Defense Analyses is a non-profit corporation that operates three federally funded research and development centers to provide objective analyses of national security issues, particularly those requiring scientific and technical expertise, and conduct related research on other national challenges. About This Publication This briefing motivates bootstrapping through an intuitive explanation of basic statistical inference, discusses the right way to resample for bootstrapping, and uses examples from operational testing where the bootstrap approach can be applied. Bootstrapping is a powerful and flexible tool when applied appropriately. By treating the observed sample as if it were the population, the sampling distribution of statistics of interest can be generated with precision equal to Monte Carlo error. This approach is nonparametric and requires only acceptance of the observed sample as an estimate for the population. Careful resampling is used to generate the bootstrap distribution. Applications include quantifying uncertainty for availability data, quantifying uncertainty for complex distributions, and using the bootstrap for inference, such as the two-sample t-test. Copyright Notice © 2016 Institute for Defense Analyses 4850 Mark Center Drive, Alexandria, Virginia 22311-1882 -
Notes Mean, Median, Mode & Range
Notes Mean, Median, Mode & Range How Do You Use Mode, Median, Mean, and Range to Describe Data? There are many ways to describe the characteristics of a set of data. The mode, median, and mean are all called measures of central tendency. These measures of central tendency and range are described in the table below. The mode of a set of data Use the mode to show which describes which value occurs value in a set of data occurs most frequently. If two or more most often. For the set numbers occur the same number {1, 1, 2, 3, 5, 6, 10}, of times and occur more often the mode is 1 because it occurs Mode than all the other numbers in the most frequently. set, those numbers are all modes for the data set. If each number in the set occurs the same number of times, the set of data has no mode. The median of a set of data Use the median to show which describes what value is in the number in a set of data is in the middle if the set is ordered from middle when the numbers are greatest to least or from least to listed in order. greatest. If there are an even For the set {1, 1, 2, 3, 5, 6, 10}, number of values, the median is the median is 3 because it is in the average of the two middle the middle when the numbers are Median values. Half of the values are listed in order. greater than the median, and half of the values are less than the median. -
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 -
Common Data Set 2020-2021 University of Pennsylvania
Common Data Set 2020-2021 University of Pennsylvania Table of Contents A General Information page 3 B Enrollment and Persistence 5 C First Time, First Year Admission 8 D Transfer Admission 14 E Academic Offerings and Policies 16 F Student Life 18 G Annual Expenses 20 H Financial Aid 22 I Instructional Faculty and Class Size 28 J Disciplinary Areas of Degrees Conferred 30 Common Data Set Definitions 32 Common Data Set 2020-21 1 25 Jun 2021 Common Data Set 2020-21 2 25 Jun 2021 A. General Information return to Table of Contents A1 Address Information A1 University of Pennsylvania A1 Mailing Address: 1 College Hall, Room 100 A1 City/State/Zip/Country: Philadelphia, PA 19104-6228 A1 Main Phone Number: 215-898-5000 A1 Home Page Address www.upenn.edu A1 Admissions Phone Number: 215-898-7507 A1 Admissions Office Mailing Address: 1 College Hall A1 City/State/Zip/Country: Philadelphia, PA 19104 A1 Admissions Fax Number: 215-898-9670 A1 Admissions E-mail Address: [email protected] A1 Online application www.admissions.upenn.edu A2 Source of institutional control (Check only one): A2 Public A2 Private (nonprofit) x A2 Proprietary A3 Classify your undergraduate institution: A3 Coeducational college A3 Men's college A3 Women's college A4 Academic year calendar: A4 Semester x A4 Quarter A4 Trimester A4 4-1-4 A4 Continuous A5 Degrees offered by your institution: A5 Certificate x A5 Diploma A5 Associate x A5 Transfer Associate A5 Terminal Associate x A5 Bachelor's x A5 Postbachelor's certificate x A5 Master's x A5 Post-master's certificate x A5 Doctoral degree - research/scholarship x A5 Doctoral degree - professional practice x A5 Doctoral degree - other x A5 Doctoral degree -- other Common Data Set 2020-21 3 25 Jun 2021 Common Data Set 2020-21 4 25 Jun 2021 B. -
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.