Focus on the Facts

2021 ◽  
Vol 22 (3) ◽  
Author(s):  
Shay Menuchin
Keyword(s):  
Data Analysis ◽  
Analysis Process ◽  
Short Essay

This Essay highlights the importance of the data analysis process (the curation) as part of analyzing and determining the strength of tax positions. This short Essay will review some of these challenges and discuss what can be done about them.

Architecture of a distributed system for processing heterogeneous data from social networks

2020 ◽  
Vol 4 ◽  
pp. 97-100
Author(s):  
A.P. Pronichev ◽  
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Distributed System ◽  
Heterogeneous Data ◽  
Analysis Process ◽  
Support Costs

The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

scholarly journals Hubungan Budaya Sekolah Dan Keteladanan Guru Dengan Karakter Siswa Sekolah Dasar Negeri 050772 Kecamatan Pangkalan Susu Kabupaten Langkat

2021 ◽  
Vol 4 (1) ◽  
pp. 19-26
Author(s):  
Khairani Nasution
Keyword(s):  
Data Analysis ◽  
School Culture ◽  
Primary School ◽  
Positive Relation ◽  
Data Collection Tool ◽  
Know How ◽  
Analysis Process ◽  
Analysis Technique ◽  
Sample Data ◽  
Data Analysis Result

This research aims to know how the relation of primary School 050772 Pangkalan Susu. The sample of this research 30 student where 21% taken  of 141 students. This research uses data collection tool in the form of Likert scale. Questioner is compiled based on variabel and tested to  respondent that is not research sample. Data analysis process is conducted by using quantitative approach through three steps, they are: 1. Describing research variabel data. 2. Testing requirements analysis. 3. Testing hypotesis to reveal the relation the relation beetween research variables using correlation analysis technique. Based on data analysis result and testing process, then the conclusion is (1) the school culture has positive relation with student’s character of primary school 050772 Pangkalan Susu. This case is proved that the correlation r (0,504) > r table (0,3061) while the significance is 0,004 < 0,05; (2) the teacher’s example has positive relation with student’s character of primary School 050772 Pangkalan Susu where correlation coeficient r (0,603) > r table (0,3061) with significncy 0,006, it is fewer than 0,05; (3) School Culture and teacher’s example has positive ration with student’s character of primary school 050772 Pangkalan Susu where correlation coefisiency r (0,662) > r table (0,3061), testing result F shows that F count (10,506) > F table (3,35) while significancy < 0,05 that is 0,000. Based on the research above, it can be stated that student’s character can be formed through school culture and teacher ’s example.

scholarly journals Preprocessing Profiling Model for Visual Analytics

2020 ◽  
Author(s):  
Alessandra Maciel Paz Milani ◽  
Fernando V. Paulovich ◽  
Isabel Harb Manssour
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Visual Analytics ◽  
Data Preprocessing ◽  
Interview Study ◽  
Raw Data ◽  
Important Stage ◽  
Analysis Process

Analyzing and managing raw data are still a challenging part of the data analysis process, mainly regarding data preprocessing. Although we can find studies proposing design implications or recommendations for visualization solutions in the data analysis scope, they do not focus on challenges during the preprocessing phase. Likewise, the current Visual Analytics processes do not consider preprocessing an equally important stage in their process. Thus, with this study, we aim to contribute to the discussion of how we can use and combine methods of visualization and data mining to assist data analysts during the preprocessing activities. To achieve that, we introduce the Preprocessing Profiling Model for Visual Analytics, which contemplates a set of features to inspire the implementation of new solutions. In turn, these features were designed considering a list of insights we obtained during an interview study with thirteen data analysts. Our contributions can be summarized as offering resources to promote a shift to a visual preprocessing.

scholarly journals Pelatihan Pola Ritme Sebagai Strategi Peningkatan Kualitas Pembalajaran Mata Kuliah Instrumen Gitar di Jurusan Musik FSP ISI Yogyakarta

PROMUSIKA ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 20-28
Author(s):  
Kustap Kustap ◽  
Ikhsan Lubis
Keyword(s):  
Data Analysis ◽  
Qualitative Method ◽  
Class Action ◽  
Material Objects ◽  
Rhythmic Pattern ◽  
Training Process ◽  
Interactive Model ◽  
Analysis Process ◽  
Research Material ◽  
Rhythm Pattern

Pola ritme merupakan elemen musik utama yang berkaitan langsung dengan gerakan waktu dalam musik, di mana waktu selalu bergerak dan mengatur kestabilan rasa musikal. Pola ritme sangat mentukan ketepatan hitungan dalam permainan gitar tunggal, dan bermain anasambel gitar. Pola ritme merukan objek material penelitian sedangkan proses pelatihan pola rime dalam pembelajaran mata kuliah instrumen gitar merupakan objek formal. Metode yang digunakan dalam penelitian ini adalah metode kualitatif dan tindakan kelas dengan teknik pengupulan data triangulasi, sedangkan proses analisis data menggunakan model interaktif. Hasil yang diperoleh dari penelitian ini adalah strategi latihan pola ritme yang disesuaikan dengan tanda sukat yang ada pada materi yang berupa lagu yang akan dilatih dengan instrumen gitar. moving and regulating musical stability. Rhythm patterns really determine the accuracy of counts in a single guitar game, and play guitar ensembles. Rhythm patterns are research material objects while the rime training process in learning guitar instrument subjects is a formal object. The method used in this study is a qualitative method and class action with the technique of collecting triangulation data, while the data analysis process uses an interactive model. The results obtained from this study are the rhythmic pattern training strategies that are tailored to the signs of sukat that are in the material in the form of songs that will be trained with guitar instruments.Keywords: Rhythm pattern; training; learning; guitar

Embracing tension: using Bakhtinian theory as a means for data analysis

2016 ◽  
Vol 17 (1) ◽  
pp. 20-36 ◽  
Author(s):  
Xiaoli Hong ◽  
Michelle M Falter ◽  
Bob Fecho
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Qualitative Data ◽  
Multiple Perspectives ◽  
Qualitative Data Analysis ◽  
Research Participants ◽  
Analysis Process ◽  
Bakhtinian Theory ◽  
Ongoing Dialogue

In this article we introduce tension as a means for qualitative data analysis based on Mikhail Bakhtin’s dialogical theory. We first explain the foundations of Bakhtin’s theory and show the inevitability of tension in our lives and qualitative data analysis. We then offer a review of how Bakhtin’s notion of tension has manifested itself in qualitative research, which prompts us to establish a tensional approach to qualitative data analysis. Finally, we outline our framework for a tensional approach to data analysis and illustrate examples of putting this approach into practice in our own study. Our tensional approach (1) explores key moments of tension; (2) seeks out unease and discomfort; (3) involves researcher and research participants in ongoing dialogue; (4) and embraces multiple perspectives on a range of tensions during the data analysis process. It encourages uncertainties and questions instead of pursuing certainty of meaning and fixed conclusions.

A systematic view on data descriptors for the visual analysis of tabular data

2016 ◽  
Vol 16 (3) ◽  
pp. 232-256 ◽  
Author(s):  
Hans-Jörg Schulz ◽  
Thomas Nocke ◽  
Magnus Heitzler ◽  
Heidrun Schumann
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Data Gathering ◽  
Climate Impact ◽  
General Concept ◽  
Basic Knowledge ◽  
Data Types ◽  
Process Data ◽  
Tabular Data ◽  
Analysis Process

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.

PMTools – a new program for paleomagnetic data analysis and visualization

2021 ◽  
Author(s):  
Ivan Efremov ◽  
Roman Veselovskiy
Keyword(s):  
Data Analysis ◽  
Paleomagnetic Data ◽  
Use Case ◽  
Quality Performance ◽  
Analysis Process ◽  
Data Formats ◽  
Cross Platform ◽  
The Mean ◽  
Near Future ◽  
Best Fit

&lt;p&gt;There are many programs for the analysis and visualization of paleomagnetic data, but each of them is good only in a certain use case and does not allow to perform a full cycle of paleomagnetic operations. Therefore, one has to resort to using a number of programs to complete the full path of processing paleomagnetic data. You often have to convert data from one format to another, manually vectorize charts, and generally spend more time and effort than could theoretically be spent. Thus, there is a long overdue need for a universal program capable of fast, convenient and high-quality performance of a full cycle of paleomagnetic operations. A set of programs written by Randy Enkin (Enkin, 1996) for DOS was taken as a time-tested example of such a program. The choice fell on them, since these programs (although they are very outdated) allow performing a full cycle of paleomagnetic operations and do it as conveniently and efficiently as possible for that time.&lt;/p&gt;&lt;p&gt;Our goal is to create a program devoid of all of the above disadvantages and capable of developing indefinitely as modular opensource software by the efforts of all people interested in this.&lt;/p&gt;&lt;p&gt;The result of our work is PMTools &amp;#8211; a cross-platform software for statistical analysis and visualization of paleomagnetic data. PMTools supports all widely used paleomagnetic data formats and allows you to work with them simultaneously. All charts created in PMTools are vector, adapted for direct using in publications and presentations, and can be exported in both vector and raster formats. At the same time, PMTools implements a full cycle of routine paleomagnetic operations: from finding the best-fit directions to calculating the mean paleomagnetic poles. Moreover, all operations can be performed both with a mouse through a graphical user interface and with hotkeys, which significantly speeds up the data analysis process.&amp;#160;&lt;/p&gt;&lt;p&gt;In the near future, PMTools will become a modular open source application, so that each user will be able to add its own modules, thereby expanding the program's functionality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Enkin, R.J., 1996. A Computer Program Package for Analysis and Presentation of Paleomagnetic Data, Pacific Geoscience Center, Geological Survey of Canada, http://www.pgc.nrcan.gc.ca/tectonic/enkin.htm.&lt;/p&gt;

PENGGUNAAN DIKSI PADA BERITA ONLINE DETIK.COM

Buana Bastra ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 49-53
Author(s):  
Pitaloka Wibawani Puspa ◽  
Luluk Isani Kulup
Keyword(s):  
Data Analysis ◽  
Human Life ◽  
Online News ◽  
Research Approach ◽  
Word Choice ◽  
Language Function ◽  
Research Focus ◽  
Descriptive Research ◽  
Analysis Process ◽  
Data Source

Language is of vital importance matter in human life. One of language function is to communicate. Along growth and advancement of technology, form of communication alsofollows expand and growing sophisticated. In other hand, can also getting the quickerinformation pass by internet that is from online news. Background this Research overshadows by existence of irrelevancy the usage of diction (word choice) that used by at onlinenews. As for research focus this is about diction (diction choice) at Detik.com online news?.Research Target this is describe the usage of diction (word choice) at Detik.com online news.By using descriptive research approach qualitative, this research takes data of have the shapeof phrase or sentence related to diction (word choice), whereas data source at research this isthe set of Detik.com online news. data collecting technique as used in research this is thedocumentation with steps collect online newses and then read citation from news referred as.At technique of data analysis, process of data of analysis have the shape of accurate dataobtained/got, classifying and coding data, interpret data, and conclude data. Base result ofdata analysis and discussion, can be concluded that existed some inaccurates in the usage ofdiction at Detik.com online news, that is at meaning connotative, synonym, antonym, publicword, special word, and foreign word.

Principles on Symbolic Data Analysis

2009 ◽  
pp. 74-81
Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Analysis ◽  
Missing Values ◽  
Symbolic Data Analysis ◽  
Human Errors ◽  
Symbolic Data ◽  
Analysis Process ◽  
New Type ◽  
Null Value ◽  
Internal Variation ◽  
Different Sources

Today’s technology allows storing vast quantities of information from different sources in nature. This information has missing values, nulls, internal variation, taxonomies, and rules. We need a new type of data analysis that allows us represent the complexity of reality, maintaining the internal variation and structure (Diday, 2003). In Data Analysis Process or Data Mining, it is necessary to know the nature of null values - the cases are by absence value, null value or default value -, being also possible and valid to have some imprecision, due to differential semantic in a concept, diverse sources, linguistic imprecision, element resumed in Database, human errors, etc (Chavent, 1997). So, we need a conceptual support to manipulate these types of situations. As we are going to see below, Symbolic Data Analysis (SDA) is a new issue based on a strong conceptual model called Symbolic Object (SO). A “SO” is defined by its “intent” which contains a way to find its “extent”. For instance, the description of habitants in a region and the way of allocating an individual to this region is called “intent”, the set of individuals, which satisfies this intent, is called “extent” (Diday 2003). For this type of analysis, different experts are needed, each one giving their concepts.

Fuzzy Clustering with Repulsive Prototypes

2010 ◽  
pp. 332-346
Author(s):  
Frank Rehm ◽  
Roland Winkler ◽  
Rudolf Kruse
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Experimental Results ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Analysis Process ◽  
Fuzzy C Means Clustering ◽  
The Right

A well known issue with prototype-based clustering is the user’s obligation to know the right number of clusters in a dataset in advance or to determine it as a part of the data analysis process. There are different approaches to cope with this non-trivial problem. This chapter follows the approach to address this problem as an integrated part of the clustering process. An extension to repulsive fuzzy c-means clustering is proposed equipping non-Euclidean prototypes with repulsive properties. Experimental results are presented that demonstrate the feasibility of the authors’ technique.

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