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

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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 5862

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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

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Published Papers (2 papers)

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18 pages, 789 KiB

Open AccessArticle

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 2 | Viewed by 2597

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 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

Open AccessArticle

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 8 | Viewed by 2655

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. 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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