Serto before Serto: Reexamining the Earliest Development of Syriac Script

Abstract Scholars have traditionally categorised early Syriac manuscripts as either Estrangela or Serto. The same categories dominate the prevailing narrative of how Syriac script is thought to have developed. Most see Estrangela as the earliest strata of Syriac and Serto as a later development. More recent scholarship explores how early manuscripts support neither this stark division between script styles nor a sequential development. Of particular challenge to this paradigm are a series of securely dated colophons and notes which use a script style different than the main part of the text. But previous work has looked at only five examples of this phenomenon. By expanding this investigation to 30 examples and drawing upon a recent compiled digital corpus of over 100,000 early Syriac letter forms, the present article explores how large data sets, digital analysis, and visual analytics can help one better understand the development of Aramaic scripts.

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Visual Analytics in Effects of Gross Domestic Product to Human Immunodeficiency Virus Using Tableau

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1038 ◽

2021 ◽

Vol 11 (3) ◽

pp. 219-223

Author(s):

Eunbi Kim ◽

◽

Ching-Yu Huang

Keyword(s):

Human Immunodeficiency Virus ◽

Gross Domestic Product ◽

Visual Analytics ◽

Open Data ◽

Large Data ◽

Large Data Sets ◽

Domestic Product ◽

Data Sets ◽

Visualization Software ◽

Immunodeficiency Virus

As data becomes more accessible, visualization methods are needed to help make it easier to understand the information. Analyzing and visualizing data makes it easier to understand a dataset without having to read through it, and elucidate connections between two or more different datasets. Tableau is one of the most popular interactive data visualization software. By using Tableau, it is easy to find correlations between datasets, reorganize datasets through pivoting or joining them, and create visualizations such as geo map charts, geo bubble charts, table charts, line charts, pie charts, and treemap charts. This project aims to show the correlation between a country’s gross domestic product (GDP) and human immunodeficiency virus (HIV) through Tableau. Large data sets related to the GDP and HIV were gathered from open data sources. The data will be cleaned through Tableau and Excel, and correlations between datasets will be shown through variable charts with Tableau.

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Meeting Big Data challenges with visual analytics

Records Management Journal ◽

10.1108/rmj-01-2014-0009 ◽

2014 ◽

Vol 24 (2) ◽

pp. 122-141 ◽

Cited By ~ 14

Author(s):

Victoria Louise Lemieux ◽

Brianna Gormly ◽

Lyse Rowledge

Keyword(s):

Big Data ◽

Visual Analytics ◽

Visual Analysis ◽

Large Data ◽

Large Data Sets ◽

Records Management ◽

Data Sets ◽

Exploratory Research ◽

Content Type ◽

Management Controls

Purpose – This paper aims to explore the role of records management in supporting the effective use of information visualisation and visual analytics (VA) to meet the challenges associated with the analysis of Big Data. Design/methodology/approach – This exploratory research entailed conducting and analysing interviews with a convenience sample of visual analysts and VA tool developers, affiliated with a major VA institute, to gain a deeper understanding of data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and analysing key emergent themes related to data challenges to map them to records management controls that may be used to address them. Findings – The authors identify key data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and identify records management controls that may be used to address these data-related issues. Originality/value – This paper discusses a relatively new field, VA, which has emerged in response to meeting the challenge of analysing big, open data. It contributes a small exploratory research study aimed at helping records professionals understand the data challenges faced by visual analysts and, by extension, data scientists for the analysis of large and heterogeneous data sets. It further aims to help records professionals identify how records management controls may be used to address data issues in the context of VA.

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An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010008794x ◽

1991 ◽

Vol 49 ◽

pp. 726-727

Author(s):

John A. Hunt

Keyword(s):

Quantitative Analysis ◽

Large Data ◽

Difference Spectrum ◽

Large Data Sets ◽

Foil Thickness ◽

Data Sets ◽

Analysis Techniques ◽

Spectrum Imaging ◽

Normal Spectrum ◽

Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100132078 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1488-1489

Author(s):

Thomas W. Shattuck ◽

James R. Anderson ◽

Neil W. Tindale ◽

Peter R. Buseck

Keyword(s):

Cluster Analysis ◽

Chemical Reactivity ◽

Large Data ◽

Large Data Sets ◽

Particle Analysis ◽

Data Sets ◽

Halogen Chemistry ◽

Complete Study ◽

Components Analysis ◽

Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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Faculty Opinions recommendation of Detecting novel associations in large data sets.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13805958.793484294 ◽

2014 ◽

Author(s):

Daniel Lee

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Novel Associations

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NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

Visnyk Universytetu “Ukraina” ◽

10.36994/2707-4110-2019-2-23-03 ◽

2019 ◽

Author(s):

Mykhajlo Klymash ◽

Olena Hordiichuk — Bublivska ◽

Ihor Tchaikovskyi ◽

Oksana Urikova

Keyword(s):

Distributed Systems ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Data Decomposition ◽

Distributed Information ◽

Software Model ◽

Computing Performance ◽

Mapreduce Model ◽

Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of computational efficiency on distributed systems were obtained using the MPI messaging protocol and MapReduce node interaction software model. Were analyzed the efficiency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

Proceedings of the Water Environment Federation ◽

10.2175/193864718824828128 ◽

2018 ◽

Vol 2018 (6) ◽

pp. 38-39

Author(s):

Austa Parker ◽

Yan Qu ◽

David Hokanson ◽

Jeff Soller ◽

Eric Dickenson ◽

...

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Statistical Tools

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Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽

10.3390/computers10040047 ◽

2021 ◽

Vol 10 (4) ◽

pp. 47

Author(s):

Fariha Iffath ◽

A. S. M. Kayes ◽

Md. Tahsin Rahman ◽

Jannatul Ferdows ◽

Mohammad Shamsul Arefin ◽

...

Keyword(s):

Source Code ◽

Large Data ◽

Large Data Sets ◽

Detection Technique ◽

Data Sets ◽

Plagiarism Detection ◽

Source Codes ◽

Efficient Detection ◽

Mathematical Problems ◽

Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

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