278
www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2022.56.08.27
How to Cite:
Tymchyshyn, A., Semeniaka, A., Bondar, S., Akhtyrska, N., & Kostiuchenko, O. (2022). The use of big data and data mining in
the investigation of criminal offences. Amazonia Investiga, 11(56), 278-290. https://doi.org/10.34069/AI/2022.56.08.27
The use of big data and data mining in the investigation of criminal
offences
Застосування Big Data та Data Mining у розслідуванні кримінальних
правопорушень
Received: September 10, 2022 Accepted: October 07, 2022
Written by:
Andriy Tymchyshyn111
https://orcid.org/0000-0002-9591-8273
Anna Semeniaka112
https://orcid.org/0000-0001-9366-8234
Serhii Bondar113
https://orcid.org/0000-0002-0497-4457
Nataliia Akhtyrska114
https://orcid.org/0000-0003-3357-7722
Olena Kostiuchenko115
https://orcid.org/0000-0002-2243-1173
Abstract
The aim of this study was to determine the
features and prospects of using Big Data and
Data Mining in criminal proceedings. The
research involved the methods of a systematic
approach, descriptive analysis, systematic
sampling, formal legal approach and forecasting.
The object of using Big Data and Data Mining
are various crimes, the common features of
which are the seriousness and complexity of the
investigation. The common tools of Big Data and
Data Mining in crime investigation and crime
forecasting as interrelated tasks were identified.
The creation of databases is the result of the
processing of data sources by Data Mining
methods, each being distinguished by the
specifics of use. The main risks of implementing
Big Data and Data Mining are violations of
human rights and freedoms. Improving the use of
Big Data and Data Mining requires
standardization of procedures with strict
adherence to the fundamental ethical,
organizational and procedural rules. The use of
Big Data and Data Mining is a forensic
111
PhD in Law Sciences, Associate Professor, Department of Law, Separate Structural Subdivision of Higher Education Institution
“Open International University of Human Development Ukraine” Ivano-Frankivska Branch, Ivano-Frankivsk, Ukraine.
112
Postgraduate student, Tavria National University named after V. I. Vernadskyi, Kyiv, Ukraine.
113
PhD in Law Sciences, Senior Research Fellow, Department for the organization of scientific activities and protection of
intellectual property rights, National Academy of Internal Affairs, Kyiv, Ukraine.
114
PhD in Law Sciences, Associate Professor, Department of Criminal Process and Criminalistics, Educational and Scientific
Institute of Law of Taras Shevchenko National University of Kyiv, Kyiv, Ukraine.
115
PhD in Law Sciences, Associate Professor, Department of Criminal Process and Criminalistics, Educational and Scientific
Institute of Law of Taras Shevchenko National University of Kyiv, Kyiv, Ukraine.
Tymchyshyn, A., Semeniaka, A., Bondar, S., Akhtyrska, N., Kostiuchenko, O. / Volume 11 - Issue 56: 278-290 / August, 2022
Volume 11 - Issue 56
/ August 2022
279
https://www.amazoniainvestiga.info ISSN 2322- 6307
innovation in the investigation of serious crimes
and the creation of an evidence base for criminal
justice. The prospects for widespread use of these
methods involve the standardization of
procedures based on ethical, organizational and
procedural principles. It is appropriate to outline
these procedures in framework practical
recommendations, emphasizing the
responsibility of officials in case of violation of
the specified principles. The area of further
research is the improvement of innovative
technologies and legal regulation of their
application.
Keywords: criminal analytics, criminal justice,
criminal offenses, investigation, working with
data.
Introduction
The law enforcement agencies are moving from
rare partial cases of the use of modern
technologies in criminal proceedings to their
complex application, the development of new
methods of detection and investigation of
criminal offences. This is determined by a
number of factors that are inherent in the vast
majority of proceedings. These are the
intellectualization of crimes and ways of
countering their detection by criminals;
significant data volumes that detectives need to
process; lack of time and the dynamic
investigation environment (Blahuta & Movchan,
2020). Information remains the central issue of
the entire investigation process — its search,
processing, consolidation and use as evidence.
Law enforcement systems generate huge
volumes of information about crimes. These are
demographic, socio-economic, time-space,
geographic data (Butt et al., 2020). Detectives get
a significant part of them from social networks,
which occupy a special place among the sources
of criminally significant data (Zhou et al., 2021).
That is why a data management model or
technique is so important for crime prevention
decision-making (Hussain & Aljuboori, 2022).
In this context, only the latest methods and
technologies can give law enforcement agencies
the opportunity to quickly and efficiently
investigate and detect criminal offences.
Big Data and Data Mining occupy a special place
among innovations, the use of which is
determined by the specifics of the information
society in which crimes are committed. In a
generalized sense, Big Data is a concept that
represents a huge amount of structured,
unstructured and semi-structured data (Usha et
al., 2020). In the era of Big Data, there is a
transition to modern ways of collecting and
integrating small-scale data contained in various
sources (Zhao & Tang, 2017). Data Mining is a
method of working with large arrays of data
using computer technologies with subsequent
identification of their significance,
comprehensive analysis and generalization to the
required informational result (Pokhriyal et al.,
2020). In this context, Data Mining is a powerful
tool with practical potential. Thanks to it,
investigators can focus on the most important
information about the crime (Hassani et al.,
2016).
Aim
In view of the foregoing, the aim of this study is
to consider the specifics of the use of Big Data
and Data Mining in the investigation of criminal
offences, as well as to determine problematic
aspects in the field of human rights protection.
The aim involves the following research
objectives:
− identify the substance and tasks of Big Data
and Data Mining as methods of investigating
crimes and predicting criminal activity;
− identify criminal, procedural and human
rights components of the application of these
methods;
− determine prospects for the introduction of
standards for the use of Big Data and Data
Mining as forensic innovations in the
investigation of crimes.
280
www.amazoniainvestiga.info ISSN 2322- 6307
Literature review
The use of Big Data and Data Mining enables
covering a number of well-known algorithms of
intellectual analysis, which are involved in the
detection and investigation of criminal offences.
These include text analysis (Pramanik et al.,
2017) (natural language processing (Chaudhary
& Bansal, 2022), content processing through the
development and application of a criminal
thesaurus (Das et al., 2021), topic
modelling (Zhao & Tang, 2017)); analysis of
competing hypotheses during
investigation (Oatley et al., 2020); studying the
specifics of the connection between the crime
and the territory in which it is committed
(Hussain & Aljuboori, 2022), which is used to
form geographic clusters (Usha et al., 2020);
structural analysis of social networks (Pramanik
et al., 2017), etc. Artificial intelligence is a
promising Data Mining tool, which supplements
the forensic capabilities of law enforcement
agencies for processing information and its
further analysis (Dupont et al., 2018). In
particular, Data Mining by means of artificial
intelligence includes Big Data processing for
profiling and forecasting criminal behaviour;
predicting crime rates (Oatley, 2022), etc.
The active use of Big Data and Data Mining has
led to the creation of intelligent platforms for the
coordination of law enforcement activities and
information provision of current and planned
policing (Norouzi & Ataei, 2021). One of them
is the EU law enforcement agency’s Secure
Information Exchange Network Application
(SIENA) platform. It ensures the exchange of
operational and strategic information on crime
between: Europol analysts and experts; EU
member states; third countries with which
Europol has cooperation agreements or working
arrangements (Europol, 2022). Open Source
Intelligence (OSINT), which is used to analyse
publicly available sources of information, is one
of the solutions to counter terrorist activities on
the Internet (Chaudhary & Bansal, 2022).
The main focus is, however, on the technological
aspects of Big Data and Data Mining. Although
their application in criminal proceedings is
covered, the criminal law, procedural and human
rights aspects remain insufficiently studied.
In particular, it is necessary to pay attention to the
types of crimes, to determine their most
significant features in order to make innovations
in the field of criminal justice more effective
(Das et al., 2021). Specialists mainly focus on the
advantages of using Big Data and Data Mining to
detect and investigate certain types of acts: fraud
and other economic crimes in the business
environment (Dehtiarovai & Yevdokimov,
2018), terrorist activity in social networks
(Chaudhary & Bansal, 2022) etc. The results of
technological counteraction to organized crime
in Ukraine have been made public. It is about
putting an end to the pirated online resources,
exposing a fraudulent financial exchange,
arresting criminals for the abuse of minors and
distribution of relevant content on the closed
Internet, suppression of the largest platform for
the sale of personal data on the darknet, etc.
(Blahuta & Movchan, 2020). There are, however,
no classifications of crimes in the investigation
of which it is appropriate to use Big Data and
Data Mining. This entails a lack of general
procedures for the use of information and
telecommunication technologies and failure to
use all opportunities for international law
enforcement cooperation. The last aspect is
extremely important, because modern organized
crime is transnational. This determines the need
for comprehensive support of investigations, in
particular, joint investigative teams (European
Parliament and the Council of the European
Union, 2018).
The procedural aspect is the next problematic
issue, that is enshrinement of the results of the
use of Big Data and Data Mining in criminal
proceedings. The matter is primarily about digital
evidence, which is obtained on the basis of
processing large information volumes.
Conventional analytical methods are not
appropriate for managing such data effectively
(Usha et al., 2020). Such evidence includes
electronic documents (text documents, graphic
images, plans, photographs, video and sound
recordings, etc.), websites, text, multimedia and
voice messages, metadata, databases and other
digital information (Blahuta & Movchan, 2020,
p. 112).
Crime forecasting is closely related to the
problems of criminal investigation. It is
extremely difficult to detect crimes and
investigate large-scale criminal activities of
organized groups without proper organization of
analytical work in this area. Forecasting crime is
one of the most difficult tasks in law
enforcement. In particular, the Big Data method
has shown the potential of generalizing such
indicators as geography, education, housing
availability, urbanization, and population
structure to predict the risk of crime in large cities
(Wang et al., 2020). Trying to estimate hidden
(latent) crime indicators is a separate problem
(Jha et al., 2021). This is the reason for the
Volume 11 - Issue 56
/ August 2022
281
https://www.amazoniainvestiga.info ISSN 2322- 6307
experts to emphasize the relevance of an
intellectual expert system that involves methods
of intellectual data analysis to predict the
criminogenic situation (Norouzi & Ataei, 2021).
In this regard, Data Mining enables combining
formalized approach and informal analysis, as
well as quantitative and qualitative data analysis
(Dehtiarovai & Yevdokimov, 2018).
So, innovations in the investigation of criminal
offences, including modern crime forecasting
capabilities, allow for a better allocation of law
enforcement resources (Hou et al., 2022).
Therefore, it is emphasized that the correct use of
Big Data and Data Mining can provide
significant savings of public funds that are
allocated to the field of security (Hassani et al.,
2016).
The prospects for the widespread use of Big Data
and Data Mining in the investigation of criminal
offenses encounter difficulties that can be
divided into several groups:
− lack of qualified personnel. The use of Data
Mining is affected by the growth of Big Data
volumes, but for people who do not have
data analysis skills and do not have special
knowledge (Hassani et al., 2016) the
admissibility of such work for the
investigation of criminal offenses is
doubtful;
− a certain subjectivity in the selection and
assessment of primary data. Detectives and
experts still have certain prejudices about the
collection and analysis of DNA,
fingerprints, electronic messages, etc.
(Oatley et al., 2020);
− the impact of the latency of crimes on the
formation of databases, which leads to an
inadequate analysis of the criminal situation
(Guariglia, 2020);
− a time factor affecting the reliability of the
results of using the latest methods. For
example, the use of Data Mining is effective,
but mostly in small time intervals
(Dehtiarovai & Yevdokimov, 2018).
Along with this, there is a danger of violation of
human rights and freedoms during the
investigation of crimes using Big Data and Data
Mining. For example, facial recognition systems
can be used to covertly collect data not only on
criminals, but also on citizens who have never
been in trouble with the law. Besides, scanning
the profiles of social media users gives law
enforcement officers access to the private lives of
millions of people (Blahuta & Movchan, 2020).
This is why some states impose restrictions on
the use of the latest technologies in law
enforcement activities. For example, starting in
2020, some cities in the US significantly limited
the allocation of resources for policing in
accordance with analytics that can predict future
crime locations, potential victims, and criminals
(Guariglia, 2020).
So, the use of Big Data and Data Mining in the
investigation of criminal offences is an urgent
problem that has both huge positive prospects
and objective difficulties. The legal dimension of
this problem draws attention to criminal law,
procedural and human rights aspects. It is
necessary to settle them for the widespread use of
Big Data and Data Mining in the field of criminal
justice.
Methodology and methods
The literature that covers the legal, procedural,
and technological aspects of using Big Data and
Data Mining in the investigation of criminal
offences, as well as forecasting criminal activity
was selected to achieve the aim set in the article
and fulfil its objectives. Their analysis made it
possible to identify the main components of the
subject under research, which reflect the legal
dimension of the problem.
The article also involved a generalization of the
practice of international law enforcement
organizations regarding the results of the use of
Big Data and Data Mining in the field of criminal
justice in terms of the requirements for building
an evidence base in criminal proceedings. This
gave grounds to determine the main prospects for
making the application of these methods for the
investigation of criminal offences and
forecasting of criminal activity more effective.
The aim of the research was achieved through the
following methods:
− systemic approach was used to study the
tasks and technologies of Big Data and Data
Mining in the field of criminal justice in
terms of human rights protection;
− descriptive analysis was used to identify the
specifics of Big Data and Data Mining as
innovative forensic methods;
− systematic sampling and doctrinal approach
enabled identifying and describing the
features of criminal offences which can be
investigated with the use of Big Data and
Data Mining;
− forecasting was used to determine the
prospects for making the use of Big Data and
Data Mining as methods of investigating
282
www.amazoniainvestiga.info ISSN 2322- 6307
crimes and predicting criminal activity more
effective.
Results
Innovations in the methods of detection and
investigation of criminal offences reflect the
intensive use of technologies in criminal
activities and the demand for digital evidence in
criminal proceedings. The application of Big
Data and Data Mining, which enable organizing
and using significant arrays of structured and
unstructured information, in combating crime is
a many-sided problem. It includes:
a) features of crimes that can be investigated
using Big Data and Data Mining;
b) crime combating objectives that can be
fulfilled with the help of these methods;
c) the specifics of using Big Data and Data
Mining methods and technologies in
criminal proceedings;
d) requirements for the application results;
e) compliance with basic human rights and
freedoms.
Defining the range of crimes is complicated by
the heterogeneity and number of their types that
can be considered in this context. Big Data and
Data Mining cover a wide range — from simple
theft to international criminal activity. At the
same time, information about suspects can be
obtained and stored in different countries and
cover significant periods of time (Hassani et al.,
2016). It follows that the detection of such crimes
usually requires cooperation with foreign states
and coordination of international organizations,
for example, Europol or Interpol. The conceptual
documents contain only an approximate list of
such crimes, for example, in Annex 1 to the
Regulation (EU) 2018/1727 of The European
Parliament and of The Council (2018). It is
considered that the relevant criminal offences
can be classified according to the following
criteria: a) territorial affiliation; b) the nature of
the act; c) subject composition. At the same time,
classification groups do not exclude each other,
but describe actions in different aspects. The
following can be considered as the main common
feature of all acts: a) the complexity of their
investigation, which necessitates the use of the
latest technologies; b) dangerousness, as a result
of which they are classified as serious (serious)
crimes punishable by imprisonment (see Figure
1).
As already mentioned, Big Data and Data Mining
are methods that are used not only in the
investigation of criminal offenses, but also in
predicting crime. These two tasks are closely
related, because the element of delinquency is
crime. In this context, crime forecasting should
be considered as a logical operation with a
purpose of identifying and, as a result,
investigating particular criminal offences. The
regularities that are revealed through the analysis
of crime are also of significant importance at the
level of a separate crime. The prediction of
connections is a key area of research in complex
social systems, which can be implemented by
assessing the possibility of the non-obvious
connections between pairs of objects. This can
provide an effective means of detecting hidden
connections in criminal networks and
conspiratorial criminal groups (Assouli et al.,
2021).
Figure 2 shows the relationship between crime
investigation and crime forecasting in the context
of Big.
Data and Data Mining
A large number of data sources with the entire set
of structured and unstructured data contained
cause the urgent need to use Big Data and Data
Mining for crime forecasting and crime
investigation. These can be open sources and
those that require permission to work with them.
They can belong to the state, law enforcement
agencies, commercial entities, public
organizations, individuals (Blahuta & Movchan,
2020). They are the source material to be
processed by law enforcement agencies through
Data Mining to create various data bases (banks)
that are actively used in the crime investigation.
This is especially important for international
investigations. For example, the General
Secretariat of Interpol has created and operates
data banks that contain information:
a) about persons wanted for crimes, missing
persons, persons subject to identification, in
particular, unidentified corpses, etc.;
b) about vehicles stolen on the territory of
Interpol member states;
c) about stolen/lost identification documents,
as well as stolen/lost forms of administrative
documents;
d) about works of art, antiques, other cultural
values stolen on the territory of Interpol
member states;
e) about DNA recovered from crime scenes on
the territory of Interpol member states and
from criminals;
f) fingerprints recovered from crime scenes on
the territory of Interpol member states and
from criminals;
Volume 11 - Issue 56
/ August 2022
283
https://www.amazoniainvestiga.info ISSN 2322- 6307
g) that enables identification of pornographic
images;
h) a bank of pornographic images created with
the involvement of minors;
i) a bank of images of counterfeit payment
cards and their elements, as well as other
relevant information regarding forgery of
payment cards, etc. (Interpol, n.d.).
Figure 3 presents the general classification of the
most popular data sources and their relationship
with databases. At the same time, it does not
contradict the appropriateness of analysing data
from many sources (Multi-Source Analysis)
(Blahuta & Movchan, 2020).
Figure 1. Classification of crimes which can be investigated using Big Data and Data Mining (built based
on European Parliament and of The Council (2018))
Be territorial affiliation
domestic
(committed within the borders of one state)
transnational
(related to crossing the state border)
against the rights and
freedoms of a person
(trafficking in people,
taking hostages, etc.)
financial (fraud, money
laundering, etc.)
against security
(proliferation of weapons,
terrorist acts, etc.)
By the nature of the act
violent acts (murders or grievous
bodily harm, sexual crimes
against children, etc.)
computer crimes
violent (murders or grievous bodily
harm, sexual crimes against children,
etc.)
environmental (including
pollution from ships)
international (genocide, war crimes,
etc.)
By the subject composition
committed by one person
committed by an organized group
national group
transnational group
the members may be citizens of one or more
states
284
www.amazoniainvestiga.info ISSN 2322- 6307
Figure 2. Crime forecasting and investigation as a Big Data and Data Mining task (developed by authors)
Figure 3. Classification of data sources and their relationship with data bases (banks) (based on Blahuta
and Movchan (2020))
Technologically, Big Data are processed using
Data Mining methods and technologies
implemented through computer tools. A certain
combination of methods is determined by the
analyst taking into account the task and specifics
of the criminal offense under investigation.
While classification is the most popular method
of Data Mining in analysing delinquency
(Hassani et al., 2016), the most popular methods
in criminal proceedings are: a) pattern
identification; b) cluster analysis (clustering); c)
association analysis; d) classification; e) social
network analysis. In turn, visualization and
machine learning are the main technologies with
which Data Mining methods are implemented.
Visualization is used to find exceptions, general
trends and dependencies, helps in obtaining data
at the initial stage of a particular project. Machine
learning is further used to find dependencies in
the project that has already been launched
(Dehtiarovai & Yevdokimov, 2018). The
specifics of the main Data Mining methods in
relation to criminal proceedings are presented in
Figure 4.
The results of the application of Data Mining
methods and technologies in criminal
proceedings must be subject to certain
requirements. They are determined by the tasks
of certain components of the work of
investigators and experts. Data Mining creates
conditions for minimal user intervention in
obtaining results. This is useful for analysts and
practitioners to make important decisions
(Norouzi & Ataei, 2021) regarding crime
investigations. In particular, it is about the
objectification of the process of advancing
versions, the choice of tactical methods of
investigative actions, etc. The exchange of
information between law enforcement agencies
is an important component of work with Big Data
and Data Mining. The specifics of crimes for
which Big Data and Data Mining are used for
investigation sets a requirement that the results of
Delinquency forecasting
Crime forecasting
Crime detection
Crime investigation
Common tool –
detection of hidden connections
Data sources
open
closed
social
networks
mass
media
official resources of
state, commercial
and public entities
operational
secret-service
intelligence
The use of Data Mining methods and technologies
Data bases (banks)
Volume 11 - Issue 56
/ August 2022
285
https://www.amazoniainvestiga.info ISSN 2322- 6307
the application of these methods facilitate
communication between law enforcement
officers of different states (Europol, 2022). In
general, evidence in criminal proceedings, which
will be recognized in court as admissible, reliable
and sufficient for deciding a case on its merits
should be the main result.
Figure 4. The main Data Mining methods in the context of the investigation of criminal offences (Hassani
et al., 2016)
In this context, human rights issues are of
particular importance. The use of forensic
innovations in criminal proceedings determines
the discourse on the provision of human rights
and freedoms both in relation to participants in
criminal proceedings and in relation to persons
whose interests may be affected by the
investigation.
The privacy and personal data protection are
among the most vulnerable areas. Access to data
plays an important role in the effectiveness of
Data Mining as a forensic method. However, the
need to keep the information confidential causes
problems (Hassani et al., 2016). In particular,
regulatory acts regulate this issue at the EU level.
It is noted that personal data must be processed
in a legal, fair and transparent manner in relation
to the data subject; such data must be relevant
and limited to the purposes for which they are
collected; they must be stored in such a way as to
ensure the security of personal data, including
their protection from unauthorized or illegal
processing (European Union, 2018).
Controversial issues of excessive interference in
the private life of vulnerable categories of
persons (children, the elderly, persons in need of
international protection, etc.) may arise during
the processing of personal data in criminal
proceedings. Such issues must be resolved with
full respect for human dignity and integrity
(European Parliament and the Council of the
European Union, 2019). The use of this method
of Data Mining as analysis of social networks
urges the issue of freedom of speech and
expression on the Internet (Guariglia, 2020), etc.
Figure 5 summarizes risks of violation of human
rights and freedoms caused by the use of Big
Data and Data Mining in the investigation of
crimes.
Data Mining methods
Pattern identification
automatically identifies
structured and unstructured
information.
Lexical search tools are
most in demand (natural
language processing for
extracting information from
unstructured text has shown
accuracy of up to 87% for
crime scene analysis)
Cluster analysis
used for grouping data in
structured sources. The most
popular are tools for
identifying the influence of
various factors on the
commission of crimes (proved
effectiveness in identifying
areas where crimes are most
often committed; finding out
whether different crimes are
committed by the same
persons)
Association analysis
detects relationships in Big
Data according to specified
criteria.
The most popular is the
analysis of associations in
the materials of various
criminal proceedings in
order to identify repeated
and group crimes.
Classification
one of the fundamental methods.
The most popular technologies are:
- decision tree (applied in fraud, computer
crime proceedings),
- artificial neural networks (applied to assess
the credibility of the testimony of the
participants in the proceedings).
Social network analysis
allows detection and investigation of crimes, the
commission of which is based on the use of
network structures, electronic messages in
communication networks. The most demanded is
the identification of unique characteristics and
active members of criminal organizations.
286
www.amazoniainvestiga.info ISSN 2322- 6307
Figure 5. The main risks of using Big Data and Data Mining in criminal proceedings in the context of
human rights protection (based on European Union (2018), European Parliament and the Council of the
European Union (2019))
The mentioned aspects enabled determining the
main prospects for improving the use of Big Data
and Data Mining in the investigation of criminal
offences, including in crime forecasting. They
are seen to be related to the standardization of
procedures for their use. The following principles
should be the basis of these standards: a) ethical;
b) organizational; c) procedural (see Figure 6).
Figure 6. Prospects for improving the use of Big Data and Data Mining in the investigation of criminal
offences (developed by authors)
In view of the above, it is considered appropriate
to talk about the development of standard
procedures for the use of Big Data and Data
Mining in the investigation of criminal offences.
These procedures should be based on ethical,
organizational and procedural principles. It is
appropriate to set out the relevant framework
procedures in practical recommendations for
authorized persons of law enforcement agencies,
noting that violation of their principles will entail
responsibility. This will enable to actively apply
Big Data and Data Mining in criminal
proceedings and use the results for the needs of
national and international justice.
Discussion
Studies on the use of Big Data and Data Mining
in the investigation of criminal offences is mainly
focused on the software features, algorithms and
their improvement. The legal aspects of the
problem are much less studied. However, in
general, the professional discussion is conducted
in the context of the appropriateness and
effectiveness of using the latest technologies in
combating criminal activity. The focus if the
issue of crime forecasting, much less often —
forecasting the commission of individual crimes.
In general, the interrelationship of these aspects
Fundamental human rights and freedoms that can be
violated by the use of Big Data and Data Mining
respect for
private life
freedom of
thought,
conscience
and religion
freedom of
expression
freedom of
assembly and
association
prohibition of
discrimination
General principles of standard procedures for the use of Big Data та Data Mining
Orginizational:
- development of unified software standards;
- ensuring staff training standards;
- ensuring coordination and assistance from
countries with advanced technologies in the
field of criminal justice
Ethical:
- minimization of risks of violation of human
rights and freedoms; non-discrimination;
- prevention of stigmatization based on
previous criminal experience;
Procedural:
- development of standards for the use of Big Data and Data
Mining results in investigative actions depending on the
current technical state of law enforcement agencies
Volume 11 - Issue 56
/ August 2022
287
https://www.amazoniainvestiga.info ISSN 2322- 6307
for the fulfilment law enforcement objectives is
not studied.
One should agree with the general initial thesis
that the evolution of criminal behaviour led to the
use of the latest technologies not only to commit
crimes, but also to avoid punishment (Hassani et
al., 2016). In this regard, Big Data and Data
Mining, which are aimed at identifying accurate,
simple, expedient and understandable patterns
and models (Belesiotis et al., 2018), enable
automating the detection of patterns and
relationships in large data sets regarding crime. It
is rightly noted that these methods play a
significant role in informational support for the
decision-making regarding crime control and
crime prevention (Norouzi & Ataei, 2021).
In this study, the position regarding the
prevalence of open data sources over closed ones
was confirmed, because law enforcement
agencies receive from 35% to 95% of data from
open sources (Blahuta & Movchan, 2020). At the
same time, it is debatable that the authors mainly
focus on certain types of data. In particular,
attention is paid to the prospect of reconnaissance
into large groups of people using sensor devices,
which provides information on the dynamics of
social processes (Zhou et al., 2021); much
attention is paid to geolocation in the context of
proactive decision-making and prevention (Butt
et al., 2020; Hussain & Aljuboori, 2022), etc. In
this regard, one should agree with the opinion
that forecasting crime requires significant
improvement in the quality of Big Data,
including the analysis of housing prices,
population density, traffic conditions, and the
unemployment rate (Hajela et al., 2021; Hou et
al., 2022) etc. Crime-related events reveal spatio-
temporal patterns that can also be used for
prediction and subsequent decision-making
(Kadar et al., 2019). However, other authors are
sceptical about the accuracy of the prediction
based on the inadequacy of the processed data
(Wang et al., 2020).
But in relation to the investigation of criminal
offences, the data obtained in the course of
different criminal proceedings require a
comprehensive analysis. First of all, the mutual
connections of the participants in several
investigations are revealed (Blahuta & Movchan,
2020). The analysis of such data as the time of
day, season of the year, weather data, types of
victims and features of places where crimes are
committed is also promising. This helps to
conclude when and where crimes are most likely
to be committed (Guariglia, 2020). In view of the
foregoing, the position should be shared that the
efficiency of Data Mining can be significantly
increased only through the combination of data
from several sources of information (Belesiotis et
al., 2018). Accordingly, an approach that offers a
combination of Data Mining methods
(Dehtiarovai & Yevdokimov, 2018) focused on
structured and unstructured data (Hassani et al.,
2016) is promising.
This research confirmed that the use of Big Data
and Data Mining encounters objective
difficulties. However, one cannot agree on the
predominant attention to technological aspects:
the formation of clusters (regions) according to
the criterion of the average score of the risk of
becoming a victim of certain illegal actions (Soni
et al., 2019); analysis of data sources on some
types of criminal activity with unsatisfactory
content quality (Hassani et al., 2016). For some
types of crimes, the appropriateness of using the
latest technologies is generally doubtful because
of the uniqueness of criminal activity, as there is
simply not enough data for analysis (Dupont et
al., 2018). In such cases, the legal dimension of
the problem cannot be covered.
Another problematic aspect is that insufficient
attention is paid in the literature to the micro level
— a specific criminal offense. In particular, the
authors emphasize that although criminals
constantly improve their criminal activities, they
find new ways of committing crimes and evading
social control (Dupont et al., 2018). Therefore,
technology cannot predict crime. This only
undermines trust between the police and society,
discriminates against vulnerable populations and
creates a greater risk of crime (Guariglia, 2020).
Such views are a certain exaggeration and
generally call into question the intellectual
methods of investigating criminal offenses.
In contrast, the discourse on the human rights
component of the use of Big Data and Data
Mining in the investigation of criminal offenses
is more realistic. Today, huge data sets can be
collected and analysed secretly (Blahuta &
Movchan, 2020). The non-transparent data
processing tools, which lead to the creation of
certain ratings regarding the risk of committing
crimes by persons with criminal experience, are
subject to sound criticism (Guariglia, 2020). One
must, however, agree that these ethical and
organizational issues must be addressed before
new technologies become widely used and
implemented in all criminal justice procedures
(Dupont et al., 2018). This thesis was confirmed
and developed in the results of our research.
288
www.amazoniainvestiga.info ISSN 2322- 6307
In general, these considerations can be the basis
of the legal, organizational and procedural
aspects of the application of Big Data and Data
Mining in the investigation of criminal offences.
Conclusions
The conducted research gave grounds for
drawing a number of conclusions regarding the
use of Big Data and Data Mining for the
detection and investigation of criminal offences.
It was established that the mentioned criminal
offences have different criminal law
characteristics. They can be combined into a
single group based on two factors —
severity (their commission is punishable by
imprisonment) and the complexity of the
investigation. The features of Big Data and Data
Mining make it possible to use them both for
crime investigation and crime forecasting, which
are interrelated tasks in the field of law
enforcement. It is shown that the processing of
heterogeneous open and closed data sources
through Data Mining enables creating data bases
(banks) used in law enforcement activities. The
procedure for the use of Data Mining represents
the use of methods and relevant procedures. The
main Data Mining methods used in investigation
of criminal offences, as well as aspects of their
most frequent application are shown. It was
established that the use of Big Data and Data
Mining is associated with the risks of violation of
basic human rights and freedoms. The most
vulnerable objects of violations in this area were
identified. The further implementation of Big
Data and Data Mining in criminal proceedings is
connected with the standardization of procedures
for the use of particular methods or their set.
The standardization of procedures is proposed in
order to unify the results of using Big Data and
Data Mining in the context of preparing evidence
for national and international courts. It is
proposed to develop the ethical, organizational
and procedural principles of each procedure and
present them in practical recommendations for
authorized persons of law enforcement agencies.
Responsibility for violation of the specified
principles shall be a separate aspect of those
procedures.
Prospects for further research of forensic
innovations in the investigation of criminal
offences include standardization of their use to
prepare an evidence base in the interests of
criminal justice. A separate promising direction
is the specialized training of specialists for the
development, implementation and use of the
latest technologies in criminal proceedings.
Bibliographic References
Assouli, N., Benahmed, Kh., & Gasbaoui, B.
(2021). How to predict crime - informatics-
inspired approach from link prediction.
Physica A: Statistical Mechanics and its
Applications, 570, 125795.
https://doi.org/10.1016/j.physa.2021.125795
Belesiotis, A., Papadakis, G., & Skoutas, D.
(2018). Analyzing and predicting spatial
crime distribution using crowdsourced and
open data. ACM Transactions on Spatial
Algorithms and Systems, 3(4), 1-31.
https://doi.org/10.1145/3190345
Blahuta, R., & Movchan, A. (2020). The latest
technologies in the investigation of crimes:
The current state and problems of use. Lviv:
Lviv State University of Internal Affairs.
Butt, U. M., Letchmunan, S., Hassan, F. H.,
Ali, M., Baqir, A., & Sherazi, H. H. R.
(2020). Spatio-Temporal Crime HotSpot
detection and prediction: А systematic
literature review. IEEE Access, 8, 166553-
166574.
Chaudhary, M., & Bansal, D. (2022). Open
source intelligence extraction for terrorism-
related information: A review. WIREs. Data
Mining and Knowledge Discovery, Online
version, e1473.
https://doi.org/10.1002/widm.1473
Das, P., Das, A. K., Nayak, J., Pelusi, D., &
Ding, W. (2021). Incremental classifier in
crime prediction using bi-objective Particle
Swarm Optimization. Information Sciences,
562, 279-303.
https://doi.org/10.1016/j.ins.2021.02.002
Dehtiarovai, Y. V., & Yevdokimov, Y. (2018).
Data mining methods and models for social
and economic processes forecasting.
Mechanism of Economic Regulation, 2,
34-44.
https://doi.org/10.21272/mer.2018.80.03
Dupont, B., Stevens, Y., Westermann, H., &
Joyce, M. (2018). Artificial intelligence in the
context of crime and criminal justice.
Montreal university [Université de
Montréal].
http://dx.doi.org/10.2139/ssrn.3857367
European Parliament and the Council of the
European Union. (2018). Regulation (EU)
2018/1727 of the European parliament and of
the council of 14 November 2018 on the
European Union Agency for Criminal Justice
Cooperation (Eurojust), and replacing and
repealing Council Decision 2002/187/JHA.
Retrieved from https://eur-
lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:32018R
1727&from=IT
Volume 11 - Issue 56
/ August 2022
289
https://www.amazoniainvestiga.info ISSN 2322- 6307
European Parliament and the Council of the
European Union. (2019). Regulation (EU)
2019/817 of the European parliament and of
the council of 20 May 2019 on establishing a
framework for interoperability between EU
information systems in the field of borders
and visa and amending Regulations (EC) No
767/2008, (EU) 2016/399, (EU) 2017/2226,
(EU) 2018/1240, (EU) 2018/1726 and (EU)
2018/1861 of the European Parliament and of
the Council and Council Decisions
2004/512/EC and 2008/633/JHA. Retrieved
from https://eur-lex.europa.eu/legal-
content/EN/TXT/HTML/?uri=CELEX:3201
9R0817
European Union. (2018). The General Data
Protection Regulation: Regulation (EU)
2016/679. Retrieved from https://eur-
lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:32016R
0679
Europol. (2022). Secure Information Exchange
Network Application: Ensuring the secure
exchange of sensitive and restricted
information. Retrieved from
https://www.europol.europa.eu/operations-
services-and-innovation/services-
support/information-exchange/secure-
information-exchange-network-application-
siena
Grechkina, O., Kornyushkina, A.,
Naruzhnaya, E., Tonkov, E., & Turanin, V.
(2019). El lenguaje jurídico como medio de
comunicación intelectual y jurídico. Revista
Científica Del Amazonas, 2(3), 32-38.
Recuperado a partir de
https://revistadelamazonas.info/index.php/a
mazonas/article/view/15
Guariglia, M. (2020). Technology can’t predict
crime, it can only weaponized proximity to
policing. Electronic Frontier Foundation.
Retrieved from
https://www.eff.org/deeplinks/2020/09/techn
ology-cant-predict-crime-it-can-only-
weaponize-proximity-policing
Hajela, G., Chawla, M., & Rasool, A. (2021). A
multi-dimensional crime spatial pattern
analysis and prediction model based on
classification. ETRI Journal, 43(2), 272-287.
https://doi.org/10.4218/etrij.2019-0306
Hassani, H., Huang, X., Silva, E. S., &
Ghods, M. (2016). A review of data mining
applications in crime. Statistical Analysis and
Data Mining, 9(3), 139-154.
https://doi.org/10.1002/sam.11312
Hou, M., Hu, X., Cai, J., Han, X., & Yuan, S.
(2022). An integrated graph model for
spatial–temporal urban crime prediction
based on attention mechanism. ISPRS
International Journal of Geo-Information,
11(5), 294.
https://doi.org/10.3390/ijgi11050294
Hussain, F. S., & Aljuboori, A. F. (2022). A
crime data analysis of prediction based on
classification approaches. Baghdad Science
Journal, 5, 1073-1077.
http://dx.doi.org/10.21123/bsj.2022.6310
Interpol. (n.d.) Our 19 databases. Recovered
from https://www.interpol.int/How-we-
work/Databases
Jha, S., Yang, E., Almagrabi, A. O.,
Bashir, A. K., & Joshi, G. P. (2021).
Comparative analysis of time series model
and machine testing systems for crime
forecasting. Neural Computing and
Applications, 33, 10621-0636.
https://doi.org/10.1007/s00521-020-04998-1
Kadar, C., Maculan, R., & Feuerriegel, S. (2019).
Public decision support for low population
density areas: An imbalance-aware hyper-
ensemble for spatio-temporal crime
prediction. Decision Support Systems, 119,
107-117.
https://doi.org/10.1016/j.dss.2019.03.001
Norouzi, N., & Ataei, E. (2021). Application of
data mining in identifying and discovering
hidden patterns of theft. International Journal
of Innovative Research in the Humanities,
1(1), 29–42.
Oatley, G. C. (2022). Themes in data mining, big
data, and crime analytics. WIREs Data
Mining and Knowledge Discovery, 12(2),
e1432. https://doi.org/10.1002/widm.1432
Oatley, G., Chapman, B., & Speers, J. (2020).
Forensic intelligence and the analytical
process. WIREs Data Mining and Knowledge
Discovery, 10(3), e1354.
https://doi.org/10.1002/widm.1354
Pokhriyal, N., Kumar, N., Verma, R., &
Semwal, A. (2020). Survey on crime data
analysis using a different approach of K-
Means clustering. International Journal of
Advanced Science and Technology, 29(5),
13839-13854.
Pramanik, M. I., Lau, R. Y. K., Yue, Wei T.,
Ye, Y., & Li, C. (2017). Big data analytics for
security and criminal investigations. WIREs
Data Mining and Knowledge Discovery,
7(4), e1208.
https://doi.org/10.1002/widm.1208
Soni, S., Shankar, V. G., Chaurasia, C. (2019).
Route-the safe: A robust model for safest
route prediction using crime and accidental
data. International Journal of Advanced
Science and Technology, 28(16), 1415-1428.
Usha, D., Niveditha, V. R., Kirubadevi, T., &
Thamizhikkavi, P. (2020). Use of predictive
analytical algorithm by crime investigation
290
www.amazoniainvestiga.info ISSN 2322- 6307
team – An Analysis. International Journal of
Advanced Science and Technology, 29(9s),
2986-2992.
Wang, J., Hu, J., Shen, S., Zhuang, J., & Ni, S.
(2020). Crime risk analysis through big data
algorithm with urban metrics. Physica A:
Statistical Mechanics and its Applications,
545, 123627.
https://doi.org/10.1016/j.physa.2019.123627
Zhao, X., & Tang, J. (2017). Modeling temporal-
spatial correlations for crime prediction. In:
E.-P. Lim, & M. Winslett (Eds.), Proceeding
of the 2017 ACM on Conference on
Information and Knowledge Management,
(pp. 497-506). New York, NY: Association
for Computing Machinery.
https://doi.org/10.1145/3132847.3133024
Zhou, B., Chen, L., Zhao, S., Zhou, F., Li, S., &
Pan, G. (2021). Spatio-temporal analysis of
urban crime leveraging multisource crowd
sensed data. Personal and Ubiquitous
Computing. Retrieved from
https://doi.org/10.1007/s00779-020-01456-6