DOCSLIB.ORG

EURAMET 1027) Th E C O Unt Mean Is Defined As the Arithmetic Mean of the Particle Diameter in the System

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
EURAMET 1027) Th E C O Unt Mean Is Defined As the Arithmetic Mean of the Particle Diameter in the System EURAMET Project 1027 Comparison of nanoparticle number concentration and size distribution report of results, V2.0 Jürg Schlatter Federal Office of Metrology METAS, Lindenweg 50, CH-3084 Wabern, Switzerland 22 September 2009 Report_EURAMET1027_V2.0.docx/2009-09-22 1/29 Table of contents Table of contents 2 Description and goal 3 Participants 4 Measurement procedure 5 Data analysis and reporting 8 Results for particle number concentration 12 Particle size distribution measurement 16 Results from the raw size data (method 1) 19 Results for particle size parameters using methods 2 and 3 22 Particle shape and size with an atomic force microscope 22 Geometric Standard Deviation 23 Further work 25 Annex A: Table of Instrumentation 26 Annex B: Protocol for number concentration measurements 27 Annex C: Protocol for size distribution measurements 28 Report_EURAMET1027_V2.0.docx/2009-09-22 2/29 Description and goal The experimental work for EURAMET Project 1027 "Comparison of nanoparticle number concentration and size distribution" was hosted on 27 and 28 November 2008 at the Federal Office of Metrology METAS, Switzerland. The particle number concentration and size distribution in a neutral combustion aerosol were measured with two types of instruments. First, the number concentration was measured with particle counters that do not evaluate the particle size (e.g. CPC = condensation particle counter). Second, the particle size distribution was measured through the combination of a particle size classifier and a particle counter (e.g. SMPS = scanning mobility particle sizer, ELPI = electrical low pressure impactor). Particle size distributions can be characterized by the (total) particle number concentration and the geometric mean size. However, this analysis depends on instrumental parameters as well as on algorithms incorporated in the manufacturer’s software. Thus, the separate comparison of simple number concentration allows the participants a direct comparison of their particle counters. The comparison used continuously generated combustion aerosol in the size range 50 to 170 nanometres (nm), with number concentrations in the range 1 000 to 1 000 000 particles per millilitre of air (cm-3). A typical measurement with constant aerosol generation lasted 30 minutes. The reported measurements of the comparison were: Average particle number concentrations at several points between 103 and 106 cm-3. The results are referred to actual ambient conditions (22 °C and 950 hPa). Average particle size at several points between 50 nm and 170 nm. The size is defined separately as the geometric mean or mode of the size distribution, and corresponds to the diameter of a sphere having the equivalent electrical mobility diameter. Degrees of equivalence (DoE) were calculated for these quantities. Report_EURAMET1027_V2.0.docx/2009-09-22 3/29 Participants Participation in this comparison was open to metrology institutes and designated institutes in this field (according to CIPM MRA – Appendix A). There were three categories of instruments involved in the comparison: those measuring particle size distributions (particle sizers); those measuring particle number concentration without any size information (particle counters); and imaging instruments providing supplementary size information. Table 1 Participating institutes and instrumentation grouped in following categories: particle sizers (first group), particle counters (second group), and imaging instruments (third group) Institute Person during Instrument Measurand experiment FORCE Technology, Karsten Fuglsang ELPI aerodynamic diameter DK distribution METAS, Jürg Schlatter SMPS electrical mobility CH diameter distribution NPL, Jordan Tompkins, SMPS electrical mobility UK Richard Gilham diameter distribution UBA, Klaus Wirtz SMPS electrical mobility DE diameter distribution AIST, Hiromu Sakurai CPC number concentration JP METAS, Jürg Schlatter Diluter and CPC number concentration CH METAS, Jürg Schlatter CPC number concentration CH NPL, Jordan Tompkins, CPC number concentration UK Richard Gilham UBA, Klaus Wirtz CPC number concentration DE DFM, (Kai Dirscherl) Atomic Force geometric size DK Microscope SMPS: Scanning mobility particle sizer CPC: Condensation particle counter (as independent instrument or an element of SMPS) ELPI: Electrical low pressure impactor Report_EURAMET1027_V2.0.docx/2009-09-22 4/29 Measurement procedure Schedule Setup with testing: Wednesday 26 November 2008 The instruments were installed and tested. A test combustion aerosol was provided by METAS. The necessary calibrations and adjustments of the components were executed. Comparison Measurements: Thursday and Friday 27 and 28 November 2008 The measurements consisted of a series of 14 measuring points with a “natural” particle size distribution (σ g ≈ 1.6) and a series of 6 measuring points with a “monodisperse” particle size distribution (σ g ≈ 1.1). The aerosol was prepared and fed to the instruments for 30 minutes for each size/concentration combination. All the relevant concentrations at a specific particle size distribution were supplied before changing the size distribution. The general schedule is shown in Figure 1, with the full schedule shown in Table 2. Dismantling: Friday 28 November 2008 The Instruments were dismantled at the second day just after the final measurements. new particle size First concentration: Size fresh new Second concentration: fresh new Third concentration: Size fresh stabilization distribution measurement air conc Size distribution air conc distribution measurement air 15 minutes 30 minutes 5 min 5 min measurement 5 min 5 min 30 minutes 5 min 0 min 30 min 60 min 90 min 120 min Figure 1 Measurement schedule for one particle size with three number concentrations. Table 2 Full measurement schedule. Time frame for Time frame for Time frame for Time frame for the measuring the measuring the measuring the measuring Date σ g Run [nm] [nm] point with point with point with point with [hPa] g 3 -3 4 -3 5 -3 6 -3 d 10 cm 10 cm 10 cm 10 cm Ambient pressure Nominal geo. Mean 27.11.2008 A 100 1.6 959 9:00 to 9:30 9:40 to 10:10 10:20 to 10:50 11:00 to 11:30 27.11.2008 B 140 1.6 957 13:00 to 13:30 13:41 to 14:11 14:20 to 14:50 - 27.11.2008 C 170 1.6 955 15:37 to 16:08 16:49 to 17:20 17:30 to 18:00 - 28.11.2008 D 70 1.6 943 8:15 to 8:45 8:55 to 9:25 9:35 to 10:05 10:15 to 10:45 28.11.2008 E 70 1.1 940 11:40 to 12:10 12:15 to 12:45 - - 28.11.2008 F 50 1.1 937 13:45 to 14:16 14:20 to 14:50 - - 28.11.2008 G 180 1.1 936 15:15 to 15:45 15:50 to 16:20 - - Report_EURAMET1027_V2.0.docx/2009-09-22 5/29 Technica l infrast ruct ure For the generation of “natural” and “monodisperse” particle distributions two types of setup were necessary (Figure 2). The "monodisperse" particle size distribution is not actually monodisperse, it contains also larger particles. When particles of a certain electrical mobility are selected by a DMA, the selection also contains double charged particles with larger diameter (Figure 3). CAST 4-way Aerosol source valve with 2.0 L/min Diluter- neutralizer 20 L/min Air 241 Am source Aerosol manifold with dumping Diluter volume DDS 560 a 20 :1 Tubes (1.5 m to 3.5 m) 2.0 L/min to instruments DMA TSI 3071 Diluter-neutralizer CAST 20 L/min Air Qsh = 241 Aerosol source 20 L/min Am source Aerosol manifold with 2.0 L/min with dumping Qae = 2.0 L/min volume U = variable b Tubes (1.5 m to 3.5 m) to instruments Figure 2 a: Setup for the combustion aerosol with a "natural" size distribution ( ≈ 1.6). b: Setup for the combustion aerosol with a “monodisperse” size distribution ( ≈ 1.1) DMA – differential mobility analyser. Example 70 nm, natural: σg = 1.6 Example 70 nm, "monodisperse", σg ≈ 1.1 tion tion Size distribu 10 100 1000 Electrical mobility diameter (nm) Figure 3 Examples of “natural” and the “mon odisperse” size distributions. Report_EURAMET1027_V2.0.docx/2009-09-22 6/29 The distance between the generator and the measuring instruments was minimized (Figure 4). Nevertheless, connection with long tubes was necessary. Diffusion losses mean that tube lengths were adapted to the flow rate as shown in Annex A. NPL SMPS: 0.3 L/min UBA SMPS: 1.0 L/min NPL CPC 0.3 L/min UBA CPC: 1.0 L/min Cupboard 3 Cupboard 2 AIST CPC 1.0 L/min FORCE: ELPI 10 L/min DFM AFM 0.5 L/min n n n 1 ST ST CA tubing: Tygon R-3603 diameter: 4.8 x 8.0 mm VKL-CPC: 0.3 L/mi VKL-CPC: Cupboard METAS SMPS 0.3 L/min + 0.3 SMPS METAS Grimm CPC+C 0.3 L/min + 1.7 L/mi CPC+C Grimm Figure 4 Arrangement of the measuring instruments. Report_EURAMET1027_V2.0.docx/2009-09-22 7/29 Data analysis and reporting Particle Size Distribution Parameter The size distribution from the instruments’ software consists of a table of values. Particle measurements are normally given with logarithmic spaced size bins d1, d2, d3 … dn normally with constant di+1 / di. For the particle number concentration for each bin i, two measures are used: the first is the average differential number concentration density ci (also indicated as or or and second is the number concentration C in log log ln i the size bin (also indicated as dW). Differential number concentration density is log ln . log ln Here the base chosen for the logarithm affects the result, but the concentration density can log ln be transformed as ln 10 log ln The bin particle number concentration is log ln . As the bin particle number concentration depends on the size bins, the number of bins per size range is important for any comparison (e.g.
Load more

Recommended publications
  • A Review on Outlier/Anomaly Detection in Time Series Data
    A review on outlier/anomaly detection in time series data ANE BLÁZQUEZ-GARCÍA and ANGEL CONDE, Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Spain USUE MORI, Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain JOSE A. LOZANO, Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain and Basque Center for Applied Mathematics (BCAM), Spain Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique. Additional Key Words and Phrases: Outlier detection, anomaly detection, time series, data mining, taxonomy, software 1 INTRODUCTION Recent advances in technology allow us to collect a large amount of data over time in diverse research areas. Observations that have been recorded in an orderly fashion and which are correlated in time constitute a time series. Time series data mining aims to extract all meaningful knowledge from this data, and several mining tasks (e.g., classification, clustering, forecasting, and outlier detection) have been considered in the literature [Esling and Agon 2012; Fu 2011; Ratanamahatana et al.
    [Show full text]
  • Mean, Median and Mode We Use Statistics Such As the Mean, Median and Mode to Obtain Information About a Population from Our Sample Set of Observed Values
    INFORMATION AND DATA CLASS-6 SUB-MATHEMATICS The words Data and Information may look similar and many people use these words very frequently, But both have lots of differences between What is Data? Data is a raw and unorganized fact that required to be processed to make it meaningful. Data can be simple at the same time unorganized unless it is organized. Generally, data comprises facts, observations, perceptions numbers, characters, symbols, image, etc. Data is always interpreted, by a human or machine, to derive meaning. So, data is meaningless. Data contains numbers, statements, and characters in a raw form What is Information? Information is a set of data which is processed in a meaningful way according to the given requirement. Information is processed, structured, or presented in a given context to make it meaningful and useful. It is processed data which includes data that possess context, relevance, and purpose. It also involves manipulation of raw data Mean, Median and Mode We use statistics such as the mean, median and mode to obtain information about a population from our sample set of observed values. Mean The mean/Arithmetic mean or average of a set of data values is the sum of all of the data values divided by the number of data values. That is: Example 1 The marks of seven students in a mathematics test with a maximum possible mark of 20 are given below: 15 13 18 16 14 17 12 Find the mean of this set of data values. Solution: So, the mean mark is 15. Symbolically, we can set out the solution as follows: So, the mean mark is 15.
    [Show full text]
  • Accelerating Raw Data Analysis with the ACCORDA Software and Hardware Architecture
    Accelerating Raw Data Analysis with the ACCORDA Software and Hardware Architecture Yuanwei Fang Chen Zou Andrew A. Chien CS Department CS Department University of Chicago University of Chicago University of Chicago Argonne National Laboratory [email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION The data science revolution and growing popularity of data Driven by a rapid increase in quantity, type, and number lakes make efficient processing of raw data increasingly impor- of sources of data, many data analytics approaches use the tant. To address this, we propose the ACCelerated Operators data lake model. Incoming data is pooled in large quantity { for Raw Data Analysis (ACCORDA) architecture. By ex- hence the name \lake" { and because of varied origin (e.g., tending the operator interface (subtype with encoding) and purchased from other providers, internal employee data, web employing a uniform runtime worker model, ACCORDA scraped, database dumps, etc.), it varies in size, encoding, integrates data transformation acceleration seamlessly, en- age, quality, etc. Additionally, it often lacks consistency or abling a new class of encoding optimizations and robust any common schema. Analytics applications pull data from high-performance raw data processing. Together, these key the lake as they see fit, digesting and processing it on-demand features preserve the software system architecture, empower- for a current analytics task. The applications of data lakes ing state-of-art heuristic optimizations to drive flexible data are vast, including machine learning, data mining, artificial encoding for performance. ACCORDA derives performance intelligence, ad-hoc query, and data visualization. Fast direct from its software architecture, but depends critically on the processing on raw data is critical for these applications.
    [Show full text]
  • Big Data and Open Data As Sustainability Tools a Working Paper Prepared by the Economic Commission for Latin America and the Caribbean
    Big data and open data as sustainability tools A working paper prepared by the Economic Commission for Latin America and the Caribbean Supported by the Project Document Big data and open data as sustainability tools A working paper prepared by the Economic Commission for Latin America and the Caribbean Supported by the Economic Commission for Latin America and the Caribbean (ECLAC) This paper was prepared by Wilson Peres in the framework of a joint effort between the Division of Production, Productivity and Management and the Climate Change Unit of the Sustainable Development and Human Settlements Division of the Economic Commission for Latin America and the Caribbean (ECLAC). Some of the imput for this document was made possible by a contribution from the European Commission through the EUROCLIMA programme. The author is grateful for the comments of Valeria Jordán and Laura Palacios of the Division of Production, Productivity and Management of ECLAC on the first version of this paper. He also thanks the coordinators of the ECLAC-International Development Research Centre (IDRC)-W3C Brazil project on Open Data for Development in Latin America and the Caribbean (see [online] www.od4d.org): Elisa Calza (United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT) PhD Fellow) and Vagner Diniz (manager of the World Wide Web Consortium (W3C) Brazil Office). The European Commission support for the production of this publication does not constitute endorsement of the contents, which reflect the views of the author only, and the Commission cannot be held responsible for any use which may be made of the information obtained herein.
    [Show full text]
  • Have You Seen the Standard Deviation?
    Nepalese Journal of Statistics, Vol. 3, 1-10 J. Sarkar & M. Rashid _________________________________________________________________ Have You Seen the Standard Deviation? Jyotirmoy Sarkar 1 and Mamunur Rashid 2* Submitted: 28 August 2018; Accepted: 19 March 2019 Published online: 16 September 2019 DOI: https://doi.org/10.3126/njs.v3i0.25574 ___________________________________________________________________________________________________________________________________ ABSTRACT Background: Sarkar and Rashid (2016a) introduced a geometric way to visualize the mean based on either the empirical cumulative distribution function of raw data, or the cumulative histogram of tabular data. Objective: Here, we extend the geometric method to visualize measures of spread such as the mean deviation, the root mean squared deviation and the standard deviation of similar data. Materials and Methods: We utilized elementary high school geometric method and the graph of a quadratic transformation. Results: We obtain concrete depictions of various measures of spread. Conclusion: We anticipate such visualizations will help readers understand, distinguish and remember these concepts. Keywords: Cumulative histogram, dot plot, empirical cumulative distribution function, histogram, mean, standard deviation. _______________________________________________________________________ Address correspondence to the author: Department of Mathematical Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA, E-mail: [email protected] 1;
    [Show full text]
  • The Reproducibility of Statistical Results in Psychological Research: an Investigation Using Unpublished Raw Data
    Psychological Methods © 2020 American Psychological Association 2020, Vol. 2, No. 999, 000 ISSN: 1082-989X https://doi.org/10.1037/met0000365 The Reproducibility of Statistical Results in Psychological Research: An Investigation Using Unpublished Raw Data Richard Artner, Thomas Verliefde, Sara Steegen, Sara Gomes, Frits Traets, Francis Tuerlinckx, and Wolf Vanpaemel Faculty of Psychology and Educational Sciences, KU Leuven Abstract We investigated the reproducibility of the major statistical conclusions drawn in 46 articles published in 2012 in three APA journals. After having identified 232 key statistical claims, we tried to reproduce, for each claim, the test statistic, its degrees of freedom, and the corresponding p value, starting from the raw data that were provided by the authors and closely following the Method section in the article. Out of the 232 claims, we were able to successfully reproduce 163 (70%), 18 of which only by deviating from the article’s analytical description. Thirteen (7%) of the 185 claims deemed significant by the authors are no longer so. The reproduction successes were often the result of cumbersome and time-consuming trial-and-error work, suggesting that APA style reporting in conjunction with raw data makes numerical verification at least hard, if not impossible. This article discusses the types of mistakes we could identify and the tediousness of our reproduction efforts in the light of a newly developed taxonomy for reproducibility. We then link our findings with other findings of empirical research on this topic, give practical recommendations on how to achieve reproducibility, and discuss the challenges of large-scale reproducibility checks as well as promising ideas that could considerably increase the reproducibility of psychological research.
    [Show full text]
  • Education Statistics on the World Wide Web 233
    Education Statistics on the World Wide Web 233 Chapter Twenty-two Education Statistics on the World Wide Web: A Core Collection of Resources Scott Walter, Doug Cook, and the Instruction for Educators Committee, Education and Behavioral Sciences Section, ACRL* tatistics are a vital part of the education research milieu. The World Wide Web Shas become an important source of current and historical education statistics. This chapter presents a group of Web sites that are valuable enough to be viewed as a core collection of education statistics resources. A review of the primary Web source for education statistics (the National Center for Education Statistics) is followed by an evaluation of meta-sites for education statistics. The final section includes reviews of specialized, topical Web sites. *This chapter was prepared by the members of the Instruction for Educators Committee, Education and Behavioral Sciences Section, Association of Col- lege and Research Libraries (Doug Cook, chair, 19992001; Sarah Beasley, chair, 2001). Scott Walter (Washington State University) acted as initial au- thor and Doug Cook (Shippensburg University of Pennsylvania) acted as editor. Other members of the committee (in alphabetical order) who acted as authors were: Susan Ariew (Virginia Polytechnic Institute and State Univer- sity), Sarah Beasley (Portland State University), Tobeylynn Birch (Alliant Uni- versity), Linda Geller (Governors State University), Gail Gradowski (Santa Clara University), Gary Lare (University of Cincinnati), Jeneen LaSee- Willemsen (University of Wisconsin, Superior), Lori Mestre (University of Mas- sachusetts, Amherst), and Jennie Ver Steeg (Northern Illinois University). 233 234 Digital Resources and Librarians Selecting Web Sites for a Core Collection Statistics are an essential part of education research at all levels.
    [Show full text]
  • Descriptive Statistics
    Statistics: Descriptive Statistics When we are given a large data set, it is necessary to describe the data in some way. The raw data is just too large and meaningless on its own to make sense of. We will sue the following exam scores data throughout: x1, x2, x3, x4, x5, x6, x7, x8, x9, x10 45, 67, 87, 21, 43, 98, 28, 23, 28, 75 Summary Statistics We can use the raw data to calculate summary statistics so that we have some idea what the data looks like and how sprad out it is. Max, Min and Range The maximum value of the dataset and the minimum value of the dataset are very simple measures. The range of the data is difference between the maximum and minimum value. Range = Max Value − Min Value = 98 − 21 = 77 Mean, Median and Mode The mean, median and mode are measures of central tendency of the data (i.e. where is the center of the data). Mean (µ) The mean is sum of all values divided by how many values there are N 1 X 45 + 67 + 87 + 21 + 43 + 98 + 28 + 23 + 28 + 75 xi = = 51.5 N i=1 10 Median The median is the middle data point when the dataset is arranged in order from smallest to largest. If there are two middle values then we take the average of the two values. Using the data above check that the median is: 44 Mode The mode is the value in the dataset that appears most. Using the data above check that the mode is: 28 Standard Deviation The standard deviation (σ) measures how spread out the data is.
    [Show full text]
  • The Precision of the Arithmetic Mean, Geometric Mean And
    1 The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach1 Mike Thelwall, Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, UK. When comparing the citation impact of nations, departments or other groups of researchers within individual fields, three approaches have been proposed: arithmetic means, geometric means, and percentage in the top X%. This article compares the precision of these statistics using 97 trillion experimentally simulated citation counts from 6875 sets of different parameters (although all having the same scale parameter) based upon the discretised lognormal distribution with limits from 1000 repetitions for each parameter set. The results show that the geometric mean is the most precise, closely followed by the percentage of a country’s articles in the top 50% most cited articles for a field, year and document type. Thus the geometric mean citation count is recommended for future citation-based comparisons between nations. The percentage of a country’s articles in the top 1% most cited is a particularly imprecise indicator and is not recommended for international comparisons based on individual fields. Moreover, whereas standard confidence interval formulae for the geometric mean appear to be accurate, confidence interval formulae are less accurate and consistent for percentile indicators. These recommendations assume that the scale parameters of the samples are the same but the choice of indicator is complex and partly conceptual if they are not. Keywords: scientometrics; citation analysis; research evaluation; geometric mean; percentile indicators; MNCS 1. Introduction The European Union and some countries and regions produce periodic reports that compare their scientific performance with the world average or with appropriate comparators (EC, 2007; Elsevier, 2013; NIFU, 2014; NISTEP, 2014; NSF, 2014; Salmi, 2015).
    [Show full text]
  • Chapter 3: Measures of Central Tendency
    CHAPTER 3 Measures of Central Tendency distribute or post, © iStockphoto.com/SolStock Introduction copy, Education and Central Averages and Energy Consumption Tendency Definitions of Mode, Median, and Mean Central Tendency, Education, Calculating Measures of Central and Your Health Tendency With Raw Data Income and Central Tendency: Calculatingnot Measures of Central The Effect of Outliers Tendency With Grouped Data Chapter Summary Do Copyright ©2020 by SAGE Publications, Inc. 81 This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. 82 Statistics and Data Analysis for Social Science INTRODUCTION How much income does the typical American take home each year? Are SAT math Measures of central scores higher since the implementation of the No Child Left Behind act? Is the typi- tendency: A cal level of education higher for males than for females? These kinds of questions group of are addressed using a group of statistics known as measures of central tendency. statistics that indicate where The most commonly known of these is the mean, or average. Others include the cases tend to mode and the median. They tell us what is typical of a distribution of cases. In other cluster in a words, they describe where most cases in a distribution are located. distribution or what is In this chapter, we learn the three most commonly used measures of central typical in a tendency: the mode, the median, and the mean (Table 3.1). Pay particular attention distribution. The to the circumstances in which each one is used, what each of them tells us, and how most common measures of each one is interpreted.
    [Show full text]
  • Adaptive Query Processing on RAW Data
    Adaptive Query Processing on RAW Data Manos Karpathiotakis Miguel Branco Ioannis Alagiannis Anastasia Ailamaki EPFL, Switzerland {firstname.lastname}@epfl.ch ABSTRACT managed by the query engine. These structures are typically called Database systems deliver impressive performance for large classes database pages and store tuples from a table in a database-specific of workloads as the result of decades of research into optimizing format. The layout of database pages is hard-coded deep into the database engines. High performance, however, is achieved at the database kernel and co-designed with the data processing operators cost of versatility. In particular, database systems only operate ef- for efficiency. Therefore, this efficiency was achieved at the cost of ficiently over loaded data, i.e., data converted from its original raw versatility; keeping data in the original files was not an option. format into the system’s internal data format. At the same time, Two trends that now challenge the traditional design of database data volume continues to increase exponentially and data varies in- systems are the increased variety of input data formats and the ex- creasingly, with an escalating number of new formats. The con- ponential growth of the volume of data, both of which belong in the sequence is a growing impedance mismatch between the original “Vs of Big Data” [23]. Both trends imply that a modern database structures holding the data in the raw files and the structures used system has to load and restructure increasingly variable, exponen- by query engines for efficient processing. In an ideal scenario, the tially growing data, likely stored in multiple data formats, before query engine would seamlessly adapt itself to the data and ensure the database system can be used to answer queries.
    [Show full text]
  • Reconceptualizing Data in Data Science
    Harvard Data Science Review • Issue 1.1, Summer 2019 Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science Sabina Leonelli Published on: Oct 04, 2019 DOI: 10.1162/99608f92.17405bb6 License: Creative Commons Attribution 4.0 International License (CC-BY 4.0) Harvard Data Science Review • Issue 1.1, Summer 2019 Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science ABSTRACT I provide a philosophical perspective on the characteristics of data-centric research and the conceptualization of data that underpins it. The transformative features of contemporary data science derive not only from the availability of Big Data and powerful computing, but also from a fundamental shift in the conceptualization of data as research materials and sources of evidence. A relational view of data is proposed, within which the meaning assigned to data depends on the motivations and instruments used to analyze them and to defend specific interpretations. The presentation of data, the way they are identified, selected and included (or excluded) in databases and the information provided to users to re-contextualize them are fundamental to producing knowledge - and significantly influence its content. Concerns around interpreting data and assessing their quality can be tackled by cultivating governance strategies around how data are collected, managed and processed. Keywords data philosophy, data history, data-centric research, inference, data management, data curation, modelling. There has been much debate around the extent to which the advent of big data and data-intensive methods is heralding the “end of theory” in scientific research, and the start of a “data-driven” approach.
    [Show full text]