Ijciet 10 02_032

http://www.iaeme.com/IJCIET/index.asp 291 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 02, February 2019, pp. 291-300, Article ID: IJCIET_10_02_032
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=02
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
MANAGERIAL DECISION SUPPORT
ALGORITHM BASED ON NETWORK
ANALYSIS AND BIG DATA
A. G. Polyakova
Plekhanov Russian University of Economics, Moscow - 117997, Moscow, Russia
Industrial University of Tyumen, Ural Region - 625000, Tyumen, Russia
M. P. Loginov
Ural State University of Economics, Ural Region - 620144, Ekaterinburg, Russia
E. V. Strelnikov
N. V. Usova
ABSTRACT
Social network analysis is a method of big data analysis which reveals the nature
of connections between objects, including implicit connections. This is a tool of interest
since it can be applied to large data sets, manual processing of which is very labor-
intensive, while automated processing through self-learning linguistic engines requires
a lot of resources. In this regard a study was carried out: it was aimed at development
and testing of social network analysis tools and creating a research algorithm which is
applicable to solve a wide range of analytical and search tasks. The current image of
Russia and its activities in the Arctic was chosen as a case.
The research algorithm helps to discover implicit patterns and trends, relate
information flows and events with relevant newsworthy events and news stories to form
a “clear” view of the study object and key actors which this object is associated with.
The work contributes to filling the gap in scientific literature, caused by insufficient
development of applied issues of using social network analysis to solve managerial
tasks, while theoretical papers, which describe the theory and methodology of such an
analysis, are abundant.
Key words: Big Data, Network Analysis, Digitalization, Decision Making,
Monitoring

A. G. Polyakova, M. P. Loginov, E. V. Strelnikov and N. V. Usova
Cite this Article: A. G. Polyakova, M. P. Loginov, E. V. Strelnikov and N. V. Usova,
Managerial Decision Support Algorithm Based on Network Analysis and Big Data,
International Journal of Civil Engineering and Technology, 10(02), 2019, pp. 291–300
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=02
1. INTRODUCTION
In recent years, information systems have become significantly more important to support
managerial decisions. The role of innovative solutions and digitalization as well as the
introduction of new type information systems are shown in Akhmetshin et al. (2018);
Kolmakov et al. (2015); Miheeva et al. (2018); Polyakova et al. (2018); Sycheva et al. (2018)
[1, 2, 13, 16, 21, 26]. Analysis of the information received through traditional tools no longer
meets requirements of efficiency and maximum coverage, which makes these tools ineffective
in planning and controlling. According to Wong and Wang (2003) [27], the success of a
decision support system relies mainly on its capability to process large data sets and efficiently
extract useful knowledge from the data, especially knowledge which is previously unknown to
the decision makers. Tools and infrastructure have already been developed to collect, process
and store big data, which made it possible to build a system to determine and develop indicator
systems that allow decisions to be made taking into account the diversity of input data. Some
solutions and their methodological grounds are described in a number of works. For example,
Brunello et al. (2019) [7] present a data-driven decision support system for front office business
process outsourcing and a way to extend it to a decision management system. Demirkan and
Delen (2013) [9] provide rationale for data management systems’ integration with decision
support systems and propose a specific look at data management as a service within a company.
There are more and more studies aimed at finding alternative or specific tools to
substantiate solutions, as well as studies which show development software evaluation criteria
(Power, 2008; Bonczek et al., 1981) [22, 6]. For instance, in Seydel (2006) [23], the
multicriteria decision problem is described, and a typically descriptive (rather than
prescriptive) tool, data envelopment analysis, is summarized, along with a hypothetical but
typical example of a multicriteria decision. Authors discuss issues related to building a
decision-making model that assists in trade-offs between the pros and cons of open data
(Zuiderwijk and Janssen, 2015) [28], as well as the specifics of support systems in different
areas: healthcare (Berndt et al., 2003; Sun et al., 2010) [4, 25], construction management (Chau
et al., 2003) [8], security (Oatley et al., 2006) [18].
To discover the nature of signal propagation in technical systems, social network analysis
can also be used as a big data processing tool. In the social area, it can be applied to study
recognition of certain ideas, concepts and images, as well as to identify their distribution
channels. Network building and visualization are quite common in modern works, including
journalistic articles. But they do not fully reveal analytical and search capabilities of social
network analysis.
1.1. Big Data and Its Role in Building Analytics Systems
Today, many people believe that the problem of supporting management decisions can be
solved with digital resources and big data operationalization. Unlocking the potential of big
data – a massive amount of structured and unstructured information that is difficult to process
with traditional methods – has several advantages: online diagnostic results, analysis of the
entire data sets, not samples, the use of machine algorithms that can identify implicit
interactions.

Managerial Decision Support Algorithm Based on Network Analysis and Big Data
Big data analytics technologies make it possible to process and systematize large
unstructured data sets and reveal hidden patterns. Basically, we can say that dealing with big
data is related to solving three classes of tasks:
– storage of data that cannot be used efficiently through conventional relational databases;
– structuring or clustering data of various types (text, images, etc.)
– processing and analysis of data sets to identify hidden patterns, search for new
information (data mining), verify and adjust existing forecast and analytical models and make
predictions.
There are various data processing methods such as predictive analytics tools, query and
reporting tools, reconstruction tools with mathematical analogies, translation, analytical
processing, etc. All of them are associated with specific algorithms determined by the goals
and objectives of the analysis. In particular, images, social networks, geographical location
data, texts, statistical data, voice data can be subject to analytical processing. Both traditional
analytics tools and modern machine learning methods are applicable to receive deeper and less
obvious outcomes: A/B testing, association rule analysis, classification, cluster analysis, data
fusion and integration, data mining, genetic algorithms, machine learning, network analysis,
optimization, pattern recognition, sentiment analysis, signal processing, spatial analysis,
directional learning, etc. In some cases, data modeling can be applied. Modeling technologies
include artificial intelligence, cognitive neural networks and predictive models.
Social network analysis based on big data concepts creates special opportunities. It
efficiently supplements traditional approaches to data analysis, forecasting various socio-
economic processes and goal setting based on known functional dependencies.
1.2. Big Data Network Analysis as a Decision-Making Support Tool
Network analysis is based on graph theory, which is the study of graphs in discrete
mathematics. It was invented by L. Euler who introduced the problem of Seven Bridges of
Königsberg in 1736. A graph is represented as a set of vertices (nodes) connected by edges, a
network is defined as a set of nodes represented by any actors (individuals, organizations,
objects, etc.), and the nodes are interconnected by arcs (directed or indirected).
One of the first theories that formed the basis of network analysis is the six degrees of
separation theory proposed by S. Milgram and J. Travers in 1969. According to this theory,
any two people on the Earth are separated by only five levels of common acquaintances. The
modern concept of social network analysis is founded by Fienberg, Meyer and Wasserman
(1985) [10], further developed by Haythornthwaite (1996) [12] whose idea was to identify
information exchange routes to improve the delivery of information services. Later Otte and
Rousseau (2002) [19] postulated that social network analysis was not a formal theory in
sociology but rather a strategy for investigating social structures. Thy proposed its applicability
to transportation, services and communications development.
In the last decade, new data storage and processing algorithms and tools were developed.
They allow networks to be built based on big data. Building networks can lead to the following
results:
– Determination of agents of influence in networks (in social systems – opinion leaders);
– measuring the effectiveness of information distribution channels;
– Operational monitoring of reactions on different relevant issues;
– Analysis of transactions and other interactions between actors and their groups;
– object classification and clustering.

Yet several issues arise regarding business administration and decision-making. Bonchi et
al. (2011) [5] mention the lack of understanding of the potential business applications of mining
social networks and a gap between the techniques developed by the research community and
their deployment in real-world applications.
One specific usage of SNA adopted in this paper is known as “consensus formation, as well
as of collaborative decision-making process”, as in Shum et al. (2013) [24] who describe it as
the unique feature of combining knowledge organization with social mapping to provide
interesting insights on the social processes activated within a collaborative decision-making
initiative.
2. DATA USED AND DECISION-MAKING MODELING
In the empirical part of the study, the goal was to test social network analysis tools and develop
the research algorithm which can be applied to solve a wide range of analytical and search
tasks. It was achieved. Scientific works describe general models that can facilitate data
integration and implementation of results, as well as be the basis of implementation of
particular solutions. For example, in Liang (1985) [15], general framework for model
management was suggested. It can integrate model management and data management and
handle issues in model management such as model creation, model modification and model
use. A task to identify the image of Russia and its perception abroad in terms of its activities in
the Arctic became a pilot. It was assumed that reliable and detailed information based on
identified implicit connections would help to make right managerial decisions and form a state
program to support the activities of the Russian Federation in the Arctic.
In order to identify opinion leaders, study the architecture of connections between them
and make managerial decisions on the need for corrective actions or lack thereof, a list of
keywords or phrases that could become markers for further search was identified. To assess the
image of Russia in the Arctic, a set of 50 phrases was formed with the expert approach. These
phrases presumably characterize how Russia and its activities in the region are perceived
abroad. The study of relationships through a set of associations will allow the "cloud"
describing overall problems to be reached through several nodes.
The set involves all active actors who speak about the studied issues. This is a global
segment geographically represented by any country, but limited to the English-speaking
audience. Since the study object is the image of Russia in the Arctic abroad, and, as for
geopolitical interest, the most significant is the opinion of representatives from the North
Atlantic region (Europe, North America), Twitter was chosen as a media source.
The implementation of the managerial decision support algorithm based on network
analysis and big data involved:
– Assessment of the existing basic approaches to investigation of processes, based on multi-
criteria measurements
– Creation of the architecture of an innovative information analytical system to collect and
process big data extracted from the Internet
– Development of algorithms to collect, process, filter and analyze big data which
characterize the studied process
– Development of the concept of big data usage to support managerial decisions based on
prompt tracking and process assessment
– Justification of proposals for the integration of the proposed system into the information
support infrastructure for managerial decision-making.

In the generalized algorithm of an information analytical system based on big data, there
are several components that involve data collection, storage, analysis and result visualization.
They are aimed at data collection from the Internet, enriching texts through linguistic
processing, fact extraction, transformations based on statistical research and transition from
business analysis to intuitive research. This classification is quite generalized since each
element can be represented as a subsystem and divided into subcomponents, and functions of
the subcomponents can migrate based on specifics of problems and software.
In order to solve the set tasks, particular software solutions were developed – data collection
and processing algorithms, as well as their implementation as a user interface. The functionality
of the developed software solutions ensured automation of the following actions:
1. Data collection algorithms for the following social networks: Twitter, Facebook,
Instagram.
2. Input data binding module (actors from different sources in one system).
3. Module for displaying / filtering / received database.
4. Tool for data export in XML / JSON / CSV for further analysis.
These procedures resulted in the database which the network is built on.
3. RESULTS AND DISCUSSION
The visualization of the actor network built on the keyword “Arctic” is shown in Figure 1 and
has over 4,000 edges in total. It should be noted that all actors and their messages are identified.
Their specific addresses and messages are not presented in the figure (only visual limitation).
However, they are contained in the database and each of them can be identified if necessary.
Figure 1. Unstructured visualization of the source data set network
Obviously, without structuring through special algorithms, the network gives limited
opportunities to interpret its connections and identify more or less homogeneous groups of
actors. Thus, in Figure 2, it becomes apparent that there are groups of actors which form some
sort of clusters.

Figure 2 Structured visualization of the source data set network
The use of clustering algorithms makes it possible to determine that over 40 clusters are
allocated in the studied data set, the largest of them are represented by a large number of actors
and the connections between them. The most representative are Clusters 1 (red in the center of
Figure 3), 2 (in the upper part of Figure 4) and Cluster 4 (in the lower part of Figure 3).
Figure 3 Clustering results
Clusters 1 and 2 have a common denominator related to global warming and Arctic ice
preservation. In particular, the first cluster is presented by the following message by the United
Nations Environment Programme and the social activity around it: "Arctic sea ice may vanish
during the summer this century even if #ParisAgreement target is met – scientists"). It is this

message and the users' reactions to it – the further distribution of the message across its subnet
– form the most significant information driver in the data sample in the considered period.
Cluster 4 is also close to environmental issues, but, as part of it, problems of a possible
hydrocarbon exploration ban in Arctic Norway are discussed. It should be noted that the
division of actors into clusters does not mean the "uniqueness" of the subject of their discussion.
Thus, climate and environmental issues are predominant in the context of the word “Arctic” in
the English segment of Twitter. Other contexts include Arctic flora and fauna reflected in photo
collections. Very few actors consolidated around the opposition to the current US President D.
Trump consider the Arctic as a scene of geopolitical battles.
There are no politically oriented actors among ten actors with the highest popularity index
(in-degree) (see Table 1).
Table 1 Characteristics of the most popular actors in the sample
Actor In-degree
Prevalent subjects
of posts
Amount
Followers Posts
@unep
UN Environment
Program
116 Climate 604,000 21,300
@awwcuteness Aww Cuteness 59 Animal photos 332,000 23,300
@climatecentral Climate Central 54 Climate 60,600 46,800
@mwobs
Mount Washington
Observatory
52 Climate 9,205 1,851
@bendahanl Luci 39
Natura photos,
incl. the Arctic
42,600 119
@mjventrice Michael Ventrice 38
Метеорология
Meteorology
10,700 11,500
@foeeurope Friends of the Earth 34
Environmental
protection
24,900 7,070
@crystal_fishy Crystal Fish 34 Animal photos 37,100 364,000
@travelinglens Vivienne Gucwa 28 Nature photos 47,900 16,500
@unfccc
United Nations climate
change secretariat
26 Climate 368,000 19,000
The identified structure of information space does not show signs of a system policy, since,
in this network discourse, Russia's activities in the Arctic evince stereotypes about the country.
4. CONCLUSION
Promising areas and opportunities of research include:
1. Potential reconfiguration of connections based on building networks excluding impacts
from certain factors. The correspondence with the desired image can require reconfiguring the
connections on the basis of filtering influential factors. Assessment of the sensitivity of the
existing communication architecture and their strengths (network metrics) to a set of factors is
a task that allows an image to be modeled. Assessment of factor influence should make it
possible to develop recommendations for updating the strategy of promoting and developing a
new “collective” image.
2. One of the promising applications for the study results may be segmentation of the
information field, both according to obvious (geography, age, etc.) and non-obvious criteria
(information dissemination models, key actors and clusters, specifics of interests of a relevant
group).

3. It is also advantageous to compare the structure of networks built on a similar principle,
but for other objects (e.g. the perception of the US role in the Middle East). It is likely that
existing patterns of information dissemination and the formation of associative pairs may be of
interest in terms of their adaptation to development of the study problems. This area involves
the search for successful practices in the territory development by countries, the search for
successful brand promotion experience, identification of architectures of the existing networks
which describe these practices, and their distribution for existing problems.
4. The study of certain subgraphs (networks within a network) may be required in terms of
particular areas of influence on some aspects of the considered problem. For example, the study
of the subgraph of a political leader will help to form targeted recommendations. Furthermore,
many researchers associate further scientific progress in the issue with studying possible
communication architectures, their modeling and connections with specific situations.
A promising area for the development of systems based on big data may be their integration
with broader software and hardware solutions, which are information and analytical platforms
for unstructured data, including geo-informational, social behavioral data and natural language
data. Opportunities of new approaches to the study of phenomena in different spheres are
shown in studies by Kolmakov et al. (2019); Nikiforov et al. (2018); Polyakova and Simarova
(2014) [14, 17, 20]. This will help to fully use analytics based on available information,
visualize, aggregate and accumulate obtained results and process them with intelligent
mechanisms detecting hidden dependencies and patterns.
A significant area of the development of the functionality is integration with geographic
information systems and services that consider geolocation of objects, which can enrich
information and analytical resources of the managerial decision-making system through
potential analysis of movements using secondary data, spatial dynamics of public opinion,
accurate referencing, and systematization. This will allow processes to be modeled in dynamics
and in the future, taking into account the spatial factor.
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