scholarly journals Combination of tactile devices for data analytics

2019 ◽  
Vol Volume 8, Issue 1, Special... ◽  
Author(s):  
Gary Perelman ◽  
Marcos Serrano ◽  
Christophe Bortolaso ◽  
Célia Picard ◽  
Mustapha Derras ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Analytics ◽  
Interaction Techniques ◽  
Nous Proposons ◽  
New Techniques ◽  
Limited Size ◽  
Display Surface ◽  
International Audience ◽  
The Creation ◽  
Tactile Vocabulary

International audience Although ubiquitous data analysis is a promising approach, analyzing data in spreadsheets on tablets is a tedious task due to the limited size of the display and tactile vocabulary. In this article, we present the design and evaluation of new interaction techniques based on the combination of a tablet containing the data and a smartphone used as a mediator between the user and the tablet. To do this, we propose to use stacking gestures, i. e. to place a smartphone on top of a tablet. Stacking is an inexpensive, easy to implement, efficient and effective way to improve the analysis of data on tablets, increasing the vocabulary and broadening the display surface by using smartphones that are always available. We first explore stacking-based solutions to delimit the possible interaction vocabulary and present the manufacture of a conductive shell for smartphones. Then, we propose new techniques based on stacking to perform data analysis of a spreadsheet, i.e. the creation of pivot tables and their manipulation. We evaluate our stacking techniques against the tactile interactions provided by current mobile spreadsheet applications. Our studies reveal that some of our interaction techniques are 30% faster than touch to create pivot tables. Bien que l'analyse ubiquitaire de données soit une approche prometteuse, l'analyse des données dans des tableurs sur des tablettes est une tâche fastidieuse en raison de la taille limitée de l'affichage et du vocabulaire tactile. Dans cet article, nous présentons la conception et l'évaluation de nouvelles techniques d'interaction reposant sur la combinaison d'une tablette contenant les données et d'un smartphone utilisé comme médiateur entre l'utilisateur et la tablette. Pour ce faire, nous proposons d'utiliser des gestes de "stacking", c'est-à-dire de poser une arrête d'un smartphone sur l'écran de la tablette. Le stacking est un moyen peu coûteux, facile à mettre en oeuvre, efficace, et basé sur l'utilisation des smartphones toujours disponibles pour améliorer l'analyse des données sur des tablettes, en augmentant le vocabulaire utilisé et en élargissant la surface d'affichage. Nous explorons d'abord des solutions basées sur le stacking pour délimiter le vocabulaire d'interaction possible et présenter la fabrication d'une coque conductive pour smartphone. Ensuite, nous proposons de nouvelles techniques basées sur le stacking pour réaliser l'analyse de données d'un tableur, c'est-à-dire la création de tableaux croisés dynamiques et leur manipulation. Nous évaluons nos techniques de stacking par rapport aux interactions tactiles fournies par les applications de tableur mobiles actuelles. Nos études révèlent que certaines de nos techniques d'interaction sont 30% plus rapides que le toucher pour créer des tableaux croisés dynamiques.

Accounting-Specific Data Analytics: A Framework for Addressing AACSB Standard A5 and Industry Demand

2021 ◽  
Author(s):  
Gregory P. Tapis ◽  
Christopher S. Hines
Keyword(s):  
Data Analytics ◽  
Learning Objectives ◽  
Specific Data ◽  
International Standard ◽  
Advisory Boards ◽  
Accounting Courses ◽  
Structured Analysis ◽  
The Creation ◽  
Colleges Of Business ◽  
Industry Needs

Arguably, data analytics is the “hot topic” for both accounting programs and Colleges of Business. Firms and advisory boards are requesting increased incorporation of data analytics into accounting programs. Furthermore, the Association to Advance Collegiate Schools of Business (AACSB) International Standard A5 requires accounting programs to focus on agility and adaptability when incorporating data analytics into the accounting program. In this paper, we propose a framework for balancing industry needs and Standard A5 that incorporates accounting-specific data analytics and satisfies existing course learning objectives. This framework was developed through the creation of a stand-alone Data Analytics in Accounting course. Our framework emphasizes students moving from more structured manual calculations to less structured analysis using Excel, and then to more unstructured analysis using specialized accounting-specific data analytics software. Additionally, we provide specific examples of how this framework can be applied to multiple accounting courses.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

scholarly journals Epistemic Matrices In The Construction Of Knowledge On Learning in Organizations

2020 ◽  
Vol 21 (3) ◽  
pp. 255-281
Author(s):  
Daniela Giareta Durante ◽  
Antonio Carlos Coelho
Keyword(s):  
Data Analysis ◽  
Qualitative Data ◽  
Graduate Programs ◽  
Analysis Software ◽  
Closed Curves ◽  
Construction Of Knowledge ◽  
Knowledge Based ◽  
The Creation ◽  
Qualitative Data Analysis Software ◽  
Sociological Approaches

Our critical epistemic review examines how researchers deal with the creation of knowledge regarding learning in organizations from the standpoint of cognitive interests. We adopted the epistemic matrices’ analytical model elaborated by Paes de Paula (2016), who handles the creation of knowledge based on cognitive interests and epistemic reconstruction, as an alternative to the paradigms of Burrel and Morgan (1979). The object of analysis were dissertations defended in Brazilian stricto sensu graduate programs in Administration. The identification of cognitive interests in the dissertations was based on sociological approaches classified by the circle of epistemic matrices, which encompasses pure sociological approaches and sets of overlapping closed curves, forming hybrid sociological approaches. The latent and clear content of sociological approaches were collected and analyzed using the qualitative data analysis software Atlas.ti 7. We conclude that the technical and practical interests guide the creation of knowledge regarding learning in organizations – putting aside the emancipatory interest in our discussions. We also conclude that, in order to make contributions for the advancement of knowledge, our study must go beyond the aggregation of cognitive interests; it is necessary to investigate the postulates of sociological approaches to which the interests are connected.

scholarly journals The effect of colloids practicum e-module to improve students’ interest on virtual lab during the covid-19 pandemic

2021 ◽  
Vol 13 (3) ◽  
pp. 172-179
Author(s):  
Nelius Harefa ◽  
◽  
Novia Fransisca Dewi Silalahi ◽  
Leony Sanga Lamsari Purba ◽  
Herna Febrianty Sianipar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Learning Process ◽  
Science Learning ◽  
Learning Activities ◽  
Virtual Labs ◽  
Colloidal Material ◽  
Virtual Lab ◽  
The Creation ◽  
Learning Interest

Practical learning which is generally carried out in the laboratory is one of the important lessons in the science learning process, especially chemistry. The Covid-19 pandemic has caused practical learning activities to not be accommodated in real laboratories. This situation encourages the creation of practical learning innovations, namely the use of virtual labs. In this study, students' learning interest in the use of virtual labs is described which is integrated with the use of e-modules on colloidal material. Based on the results of data analysis, 74.55% of students were interested in using the virtual lab, 10.90% very interested, 12.73% quite interested, and 1.82% lack of interested. These data indicate that the majority of students can make good use of the virtual lab and are able to optimally elaborate on the learning process. However, virtual labs are not intended to replace real laboratories but can be used as supplements and media to support learning in real laboratories.

Data Analytics in Tax Research: Analyzing Worker Agreements and Compensation Data to Distinguish Between Independent Contractors and Employees Using IRS Factors

2020 ◽  
Vol 35 (3) ◽  
pp. 1-23
Author(s):  
A. Faye Borthick ◽  
Lucia N. Smeal
Keyword(s):  
Data Analysis ◽  
Data Analytics ◽  
Internal Revenue Service ◽  
Database Query ◽  
Employment Practices ◽  
Accounting Data ◽  
Independent Contractors ◽  
Client Data ◽  
Tax Research ◽  
Tax Practice

ABSTRACT This case prompts learners to analyze compensation data and worker agreements to assess a company's likely compliance with requirements for classifying workers as independent contractors rather than employees based on the factors the Internal Revenue Service (IRS) uses for compliance with IRS Rev. Rul. 87-41 and Treas. Reg. § 31.3401(c)-1. Students combine tax research and data analysis to identify risky employment practices, recommend corrective action to bring the company into compliance, and estimate potential penalties if the IRS were to declare the company not in compliance. Students complete a data analysis report as a basis for preparing a research memorandum. Students electing tax practice will need to be able to perform similar analyses of client data in advance of IRS audits given that the IRS analyzes accounting data when auditing taxpayers. Given the guidance in the Teaching Notes, no database query experience is necessary on the part of instructors.

Big Data Analytics in Action

2019 ◽  
pp. 266-294 ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insight Into

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

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