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Selected papers from the Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2020)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 8002

Special Issue Editors

Dr. Nelson Baloian

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Guest Editor
Department of Computer Science
Interests: Data Science; Learning Analytics; Collaborative Computing
Dr. Wolfram Luther

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Guest Editor
University of Duisburg-Essen
Interests: Mathematical methods for computer sciences, computer graphics
Special Issues, Collections and Topics in MDPI journals
Dr. Ashot Harutyunyan

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Guest Editor
American University of Armenia, VMWare Armenia
Interests: Information theory and coding, data science, machine learning
Dr. Tomoo Inoue

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Guest Editor
University of Tsukuba
Dr. Hrant Khachatrian

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Guest Editor
YerevaNN; Yerevan State University
Interests: deep learning; natural language processing; graph theory

Special Issue Information

Dear Colleagues,

The 2nd Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2020) will be held at the American University of Armenia AUA, 25–28 August 2020 in Yerevan, Armenia. The workshop aims at exchanging knowledge about Ambient Intelligence (AmI) research based on advances in sensors and sensor networks, pervasive computing, embedding computational capability into everyday objects, artificial intelligence bringing cognitive capabilities to a new generation of devices, and sensors and controllers with their interfaces in smart environments (SmE) growing in their capabilities and easing collaboration among people. The main source and asset for making smart systems is data, which our information age has made easily accessible. The next main challenge we face is to effectively and efficiently extract knowledge from huge amounts of data from heterogeneous sources to make the systems self-contained and autonomous.

Authors of selected high-qualified papers from the conference will be invited to submit extended versions of their original papers (50% extensions of the contents of the conference paper) and contributions.

Potential topics include but are not limited to the following:

  • Track on collaborative technologies with applications in smart cities;
  • Track on data science and information theoretic approaches for smart systems;
  • Track on technical challenges for smart environments;
  • Track on artificial intelligence, neural networks, and deep learning;
  • Track on smart human-centered computing.

Dr. Nelson Baloian
Dr. Wolfram Luther
Dr. Ashot Harutyunyan
Dr. Tomoo Inue
Dr. Hrant Khachatrian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 789 KiB  
Article
Forecasting Key Retail Performance Indicators Using Interpretable Regression
by Belisario Panay, Nelson Baloian, José A. Pino, Sergio Peñafiel, Jonathan Frez, Cristóbal Fuenzalida, Horacio Sanson and Gustavo Zurita
Sensors 2021, 21(5), 1874; https://doi.org/10.3390/s21051874 - 8 Mar 2021
Cited by 5 | Viewed by 3783
Abstract
Foot traffic, conversion rate, and total sales during a period of time may be considered to be important indicators of store performance. Forecasting them may allow for business managers plan stores operation in the near future in an efficient way. This work presents [...] Read more.
Foot traffic, conversion rate, and total sales during a period of time may be considered to be important indicators of store performance. Forecasting them may allow for business managers plan stores operation in the near future in an efficient way. This work presents a regression method that is able to predict these three indicators based on previous data. The previous data includes values for the indicators in the recent past; therefore, it is a requirement to have gathered them in a suitable manner. The previous data also considers other values that are easily obtained, such as the day of the week and hour of the day of the indicators. The novelty of the approach that is presented here is that it provides a confidence interval for the predicted information and the importance of each parameter for the predicted output values, without additional processing or analysis. Real data gathered by Follow Up, a customer experience company, was used to test the proposed method. The method was tried for making predictions for up to one month in the future. The results of the experiments show that the proposed method has a comparable performance to the best methods proposed in the past that do not provide confidence intervals or parameter rankings. The method obtains RMSE of 0.0713 for foot traffic prediction, 0.0795 for conversion rate forecasting, and 0.0757 for sales prediction. Full article
(This article belongs to the Special Issue Selected papers from the Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2020))
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28 pages, 8592 KiB  
Article
An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning
by Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, Clement Pang, George Oganesyan, Sirak Ghazaryan and Narek Hovhannisyan
Sensors 2021, 21(5), 1590; https://doi.org/10.3390/s21051590 - 25 Feb 2021
Cited by 12 | Viewed by 3476
Abstract
The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. [...] Read more.
The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments. Full article
(This article belongs to the Special Issue Selected papers from the Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2020))
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