=Paper=
{{Paper
|id=Vol-2667/paper53
|storemode=property
|title=Data exchange platform for digital economy applications
|pdfUrl=https://ceur-ws.org/Vol-2667/paper53.pdf
|volume=Vol-2667
|authors=Oleg Surnin,Pavel Sitnikov,Anastasia Khorina,Anton Ivaschenko,Anastasia Stolbova,Nataly Ilyasova
}}
==Data exchange platform for digital economy applications ==
https://ceur-ws.org/Vol-2667/paper53.pdf
Data Exchange Platform for Digital Economy
Applications
Oleg Surnin Pavel Sitnikov Anastasia Khorina
Data analysis department Faculty of infocommunication Faculty of infocommunication
IPSI SEC “Open Code” ITMO University ITMO University
Samara, Russia Saint-Petersburg, Russia Saint-Petersburg, Russia
surnin@o-code.ru sitnikov@o-code.ru anastasiakhorina@mail.ru
Anton Ivaschenko Anastasia Stolbova Nataly Ilyasova
Computer science department Information systems and technologies Image Processing Systems Institute of RAS
Samara State Technical University department - Branch of the FSRC "Crystallography
Samara, Russia Samara National Research University and Photonics" RAS
anton.ivashenko@gmail.com Samara, Russia Samara, Russia
anastasiya.stolbova@bk.ru ilyasova.nata@gmail.com
Abstract—This paper describes an experience of the Data street retail – organization of retail trade in the most
exchange software platform practical use supporting the visited places: on pedestrian streets, ground floors of
modern trends of Digital Economy. The platform was initially buildings;
designed for the suppliers and customers of data sources
providing the up-to-date technologies of big data processing as non-food retail – organization of trade in non-food
an online service. The platform is also open for software products, which in grocery stores, as a rule, are called
developers to upload new algorithms and technologies in order related. Such products include clothing, cosmetics,
to help them to find new areas of application. There is household chemicals, stationery and other categories
presented architecture and its software implementation for an of goods;
intermediary online platform capable of collecting, processing
and analysis of various datasets. Modern companies being the food retail – organization of food trade. This category
members of digital economy can use this platform to process of goods is the most demanded and relates to
their data and produce business analytics. They can become everyday goods;
both suppliers and providers of data, as well as develop and
upload new customized algorithms. First results were achieved network retail – organization with several stores of
in the area of retail and social media analysis. the same chain united by one concept;
electronic retail – organization of trade through the
Keywords—data exchange, digital economy, big data
Internet;
I. INTRODUCTION cellular retail – organization of trade between mobile
Data exchange platform is a new concept to organize an operators.
efficient cooperation between the providers and customers of
Retail industry gains significant benefits of being
various data sets and algorithms based on implementing the
adaptive to customer changing demands. Understanding the
software solution of open service provider [1 – 2].
trends and prediction of changes helps reducing costs and
Considering the results of open service software providers’
increasing competitiveness. Application of modern
practical use [3], there was developed and probated new
technologies for data collecting and processing can solve this
software architecture capable of solving the actual problems
problem.
of business analytics in the different spheres of digital
economy. Retailers use the following mechanisms to successfully
organize business processes that form the main requirements
Such a solution is presented below with an illustration of
for a data processing toolset:
Data exchange platform application in network retail. Data
exchange platform implements the modern technologies of calculation of the most optimal locations for
big data analysis and capable of providing business analytics placement of outlets;
and decision-making support in real time. The proposed
solution architecture and case study intended for processing use of modern commercial equipment;
network retail data, predicting the processes, managing the work with categories of clients;
placement of big data, and planning the computing load
balancing. work with methods of attraction;
II. PROBLEM DOMAIN OVERVIEW development of self-service and reduction of
personnel due to this;
Retail is a popular way to organize distributive trades.
Modern retail industry becomes a promising area of optimization of logistics, work with wholesale
development of network organizations that provide suppliers;
distributed services for a large amount of consumers and
therefore processing the concerned flows of big data. automation of all stages of trade.
The logic of this information processing is mainly Depending on the specifics of retail problem domain and
influenced by the processes of retail service. The following other factors, a retail development strategy is determined.
areas of retail are distinguished: The strategy operates with critical factors, by solving the
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
Data Science
problems of determining the selling price of goods sold, low cost of its application. For example, to sell an
managing the assortment of outlets, and determining their expensive product, you need to put it next to an even
location. more expensive product. Another fact that has been
studied is that the customer has a high probability that
A comparison of the most frequently used business he or she will not have a single price, so a small
intelligence systems in the retail sector for a number of the difference in price should be left for similar products.
most important criteria is shown in Table 1. Psychological techniques include managing delivery
TABLE I. PLATFORMS COMPARATIVE ANALYSIS
prices. It is very often necessary to arrange delivery.
Modern research shows that most shoppers leave the
Data store basket. The offer of free delivery or promotional
Qlik Klip Tab Power
Feature ex goods when making delivery positively affects the
View folio leau BI
change
SQL and
customer;
software
+ - - - + predictive pricing is a fairly effective approach, the
develoment
skills main task of which is to determine how price affects
Own demand. The approach allows you to model changes
programming + - - - + in demand depending on the price, taking into
language account various factors, including other methods
Connectivity to involved in pricing: psychological pricing, the
heterogeneous + + + + + influence of competitors, behavioral models and
data sources
Data storage + + + + -
categories of current retailer customers, current trends
Data (in the fashion industry or global trends in lifestyle
+ - + changes).
aggregation
A variety of
dashboards for + + + + + III. STATE OF THE ART
visualization
Graphical
The results of [3] show that retailers are moving to more
programming - - - - + innovative strategies to offer modern consumer solutions
language based on technological advances. The high level of property
Intelligent rights, due to the enormous number of patents, forces
algorithms and - - - - + retailers to invest more in the acquisition of patented
data processing technologies to achieve advantages over competitors or to
Specialization
in retail - - - - +
introduce new management methods.
analytics The need to analyze retail data in a highly competitive
Possibility of
monetization of
environment is shown in [4]. Advances in machine learning
- - - - + and big data lead to the use of data analytics management
data, analytics
and algorithms systems in many organizations and industries.
As a rule, large organizations can devote more resources
Currently, electronic retail is popular and its popularity is to this, and the software used is more suitable for large
growing according to the main trends of digital economy enterprises. However, the growing business pressure is
development. Electronic retail includes trade in both food forcing small and medium-sized enterprises to implement
and non-food products, which leads to serious competition data analytics, which is new to them and leads to a number of
among retailers. Price is the main factor that allows problems considered in [5].
companies to stand out among their competitors and attract
buyers, so pricing strategies should be the most flexible. Social networks have become a part of life for most
people around the world. Retailers (and not only) are actively
There are a variety of approaches to pricing: using them to share information about their products with
personalized approach to customers with the customers. As a result of the growth of social networks, the
formation of individual offers: this approach is based need for monitoring, data mining and analysis is increasing.
on the analysis of the consumer behavior of each So, in determining the tasks and opportunities of network
specific buyer, the determination of his needs, retail, the issues of integration with social networks,
financial capabilities and, ultimately, the formation of determining a development strategy and studying the life
individual price offers for him; cycle of clients are relevant [6, 7]. The study of customer
behavior models requires taking into account completely
promotion management: the approach is aimed at the diverse data and improving their analysis.
formation of special price offers for goods or groups
of goods according to certain criteria in order to In [8], the questions of creating a body of knowledge are
attract customers. In applying this approach, it is considered, the concept and key methods of data analysis are
important not to create a reputation as a discounter, studied in the field of retail network, the creation of data
which can negatively affect the perception of the exchange methods [9], such as shopping basket analysis [10]
retailer by customers, so promotions should be and sales analysis in general using business intelligence
temporary; tools.
psychological techniques that affect the perception of Analysis of factors of customer satisfaction and loyalty,
price. This approach is common for the retail industry the image of retailers and the relationships between them
as a whole and is quite popular, due to the ease and lead to the emergence of models for creating a satisfactory
experience for consumers, which is a priority for retailers
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[11]. The influence of fluctuations in demand and purchasing According to the commission of the Code of good
power on macroeconomic regulation and state control are practices, it is known that retailers do not want to share sales
considered in [12]. data of individual outlets with suppliers, which leads to an
inaccurate assessment of their activities, loss of productivity,
Research on the effects of loyalty programs on the and, consequently, loss of income.
decision to purchase goods at different periods of time is
relevant, since an important factor for multichannel grocery IV. SOLUTION ARCHITECTURE
retailers is which promotion strategy to choose across all
channels. The tendency of product buyers to continue to visit Having considered the tasks and problems of network
an offline store after they start buying in the online store of retail, strategies for solving them, we can conclude that the
the network means that promotions in one channel can have creation of a data exchange platform is a necessary step for
a significant negative impact on the behavior of customers in the business development, and its intellectualization is an
another channel, especially if promotions differ by channel integral part of the process.
[13]. Intellectualization includes the introduction of the
To solve the above mentioned problems of network retail following technologies into the data exchange:
there can be used a variety of data sources. For example, it is 1. Face recognition. Biometric assessment systems can
possible to analyze data obtained in the following areas: optimize salary costs, allow you to calculate the costs of non-
1. Acquiring data which includes information about the staff employees and minimize the risks of dishonest actions
commission of the acquiring bank, categories of outlets, and counteractions. The introduction of face recognition can
payment card options, which allows you to determine the also significantly increase understanding of the
dependence of the availability of sales outlets on the characteristics of the average buyer. Indeed, many algorithms
availability of POS terminals; allow you to evaluate gender, age, race, and thereby form a
portrait of the buyer. To solve such problems, a camera is
2. Retailer data, which include: used, aimed close-up at incoming visitors, with a face
detection function. Additionally, such algorithms in
information on loyalty programs and promotions to combination with the display of advertising can give an
determine the dependence of demand on the understanding of the effectiveness of advertising on visitors.
availability of various special offers for goods;
2. Creating smart baskets. Built-in cameras that can
information about regular customers and users of recognize and scan products, sensors that detect the detection
loyalty programs to analyze the dependence of the of an object in the basket, as well as scales that allow you to
purchased goods on the categories of customers, to get rid of additional weighing of fruits and vegetables. On
form the most advantageous offers for both the buyer the trolley screen, the buyer will be able to see all the
and the retailer. In addition, it is possible to evaluate products taken, as well as their total amount. The data that
the customers in terms of how much money they carts collect (on routes through stores, on the frequency of
spend and how often they make purchases in retailers’ purchases of goods from certain shelves, where customers
stores, which will determine the significance of each are, etc.) would help company partners optimize their stores.
customer for the network and more accurately
formulate personal offers; 3. Analysis of social networks. Analysis of data from
social networks will allow you to analyze customer
date of purchase and time of purchase to analyze the complaints in real time and track user requests and wishes.
dynamics of demand for certain products depending Location and shopping data will allow you to compile the
on seasonality and time of day. In addition, it is most complete portrait of a specific retailer buyer.
necessary to take into account various holidays and
ongoing events in the analyzed region; 4. Internet of things. When it comes to retail, the IoT
infrastructure includes RFID tags, infrared traffic meters in
the list of goods on the check allows you to analyze stores, satellite and Wi-Fi tracking systems, digital
the most frequently purchased goods together; signatures, kiosks or even mobile devices of the customers
the price of the items listed in the check and the total themselves. The Internet of things makes it possible to
purchase amount allow the analysis of the turnover of implement predictive maintenance of equipment,
the outlet; transportation, keeping a warehouse on the basis of demand,
tracking the activity of buyers, creating a smart store,
the trajectory of buyers allows you to determine the behavioral analytics, personalized marketing and real-time
order of purchases at retail outlets and to identify the advertising based on location and purchase history.
optimal location of goods in the store;
5. Adaptive Acquiring. The sources of such a data
3. Social media that help identification of fashion trends exchange are intelligent systems. Data processing can give
and identify the dependence of purchases as well as the most conflicting results in different contexts and in the absence of
popular brands and products; data, but the results obtained should provide decision-making
support. Technologies without data will not give the desired
4. Geo data that can be used to determine the dependence
result, as well as data without applying the necessary
of consumer activity, demand for goods, payment methods
technologies to them. So, the data exchange can be presented
depending on the territorial location of the outlet and the
as a platform for interfacing digital intelligent services.
nearby infrastructure, such as the area (sleeping, tourist,
business centers and others), categories of users visiting or The proposed software solution for a data exchange
living in the area. platform is presented in Fig. 1. It was implemented using
Java (IntelliJ IDEA 15.0.3 Integrated Software Development
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Data Science
Environment (Community Edition)) and supports JavaScript, In the considered example, users were searched for the
CoffeeScript, HTML / XHTML / XAML, CSS / SASS / following product groups: 3 (energy granola bars), 4 (instant
LESS, XML / XSL / XPath, YAML, ActionScript / MXML, foods), 5 (marinades meat preparation). As can be seen from
Python, Ruby, Haxe, Groovy, Scala, SQL, PHP, Kotlin, Figure 2, for the buyer with the number 141848112, you can
Clojure, C, C ++. create an offer for goods from group 3 (energy granola bars),
and for the buyer with the number 78310286 – 4 (instant
foods).
Fig. 1. Software architecture.
Additional libraries and frameworks include Apache
Hadoop, Apache Spark, and Django. Apache Hadoop
(HDFS) is a file system designed to store large files, block-
by-block distributed between nodes of a computing cluster.
Apache Spark is an open source framework for
implementing distributed processing of unstructured and
weakly structured data, which is part of the Hadoop project
ecosystem.
The developed data exchange platform provides the
functionality for pre-processing and analysis of network
retail data. As a part of data pre-processing the system
implements the methods of data structuring, omission
processing, and data dimension reduction, trend highlighting,
and correlation analysis.
To solve the problems of network retail analysis, there is
a possibility to use the following:
Apriori, FPG (Frequent-Pattern Tree) methods help Fig. 2. Project Results.
solving the problem of analysing a shopping cart;
Another popular task in the field of network retail
Linear regression methods are used to solve the analysis is the sales forecast. The following fields were
forecasting problem; selected as the initial data of the problem to be solved: store
The Mean method is used to calculate average values; identifier, purchase date, purchase amount.
To solve the problem, there were combined the basic
K_means method is used for various kinds of
methods powered by the developed data exchange platform:
clustering.
Besides, a wide range of methods such as ARIMA, FB python_basic_filter – the method allows you to
Prophet and others are used to analyse time series. select sales data for one store from many;
python_basic_resample – the method allows you to
V. IMPLEMENTATION AND TESTS aggregate data by day, summing them up;
The following example illustrates the results of Data
exchange platform development. We create a project for python_timeseries_holt_winters – the method allows
personal offers on selected categories of goods formed by a forecasting using the Holt-Winters additive model
retailer for users of a social network with loyalty cards. (triple exponential smoothing).
Forming this request is useful if you need to increase sales python_plotly_forecast – a method for plotting.
for certain categories of goods available to the retailer.
The ability to combine methods makes it possible to
The result of this query is a list of users who are choose the most suitable analysis algorithm, taking into
interested in purchasing goods for selected groups of goods account the specifics of the source data. The project drawn
with an indication of the group. By analyzing the results up in the query designer is shown in Fig. 3.
obtained, the retailer can formulate personal offers for the
groups of goods chosen by him for the most suitable
customers.
VI International Conference on "Information Technology and Nanotechnology" (ITNT-2020) 246
Data Science
Sitnikov), analysis of its perspective for digital economy (A.
Ivaschenko) based on implementation of data exchange
model (A. Stolbova) and algorithms of semantic (A.
Khorina) and statistics (N. Ilyasova) analysis.
ACKNOWLEDGMENT
This work was financially supported by the Russian
Foundation for Basic Research under grant # 19-29-01135
and by the Ministry of Science and Higher Education within
the State assignment to the FSRC “Crystallography and
Photonics” RAS.
Fig. 3. Predictive analysis project.
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