Classifying Big Data Taxonomies: A Systematic Literature Review

Daniel Staegemann, Matthias Volk, Alexandra Grube, Johannes Hintsch, Sascha Bosse,

Robert Häusler, Abdulrahman Nahhas, Matthias Pohl and Klaus Turowski

Magdeburg Research and Competence Cluster Very Large Business Applications, Faculty of Computer Science,

Otto-von-Guericke University Magdeburg, Magdeburg, Germany

{daniel.staegemann, matthias.volk, alexandra.grube, johannes.hintsch, sascha.bosse, robert.haeusler,

Keywords: Big Data, Taxonomy, Literature Review, Classification, Categorisation, Systematization, Data

Characteristics, Structured, Analysis.

Abstract: As big data is a rather young, but growing discipline, lots of confusion about the general nature of this term

exists. Consequently, multiple research endeavours to discover unique characteristics, technologies,

techniques and their interconnections were conducted, resulting in comprehensive classification approaches.

For this purpose, various taxonomies on big data exist in literature. However, due to the multitude of

approaches and partial contradictions, no real clarification is achieved. To overcome this issue, a systematic

literature review was conducted, which identifies and analyses big data taxonomies. As a result, a

classification of those taxonomies is proposed, which additionally tracks sub-domains that are not yet covered

by the existing taxonomies so far. Eventually, the publication at hand serves as a starting point for further

taxonomy related research endeavours in the big data domain.

1 INTRODUCTION

The term big data, as known today, was coined

approximately 20 years ago (Diebold 2012). Whether

it is in astronomy where telescopes produce hundreds

of gigabyte of data every night (Kremer et al. 2017),

in healthcare with organizations trying to find

patterns in their data to improve services (Wang et al.

2018), in disaster warning were social media data is

exploited (Wu and Cui 2018), or in the endeavour to

improve urban transportation management (Fiore et

al. 2019), big data turned into an omnipresent part of

today’s society. Furthermore, it is a key technology in

industry 4.0 (Dobos et al. 2018; Wang et al. 2016) and

the economic value of its application is scientifically

substantiated (Müller et al. 2018; Brynjolfsson et al.

2011; Bughin 2016). Though there are many

definitions of big data available, it is often

characterized by volume, referring to the massive size

of data to handle, velocity, which stands for the speed

data is being produced or transmitted, variety,

representing the spectrum of data formats and sources

and variability, indicating the ongoing changes that

occur in the data. Overall big data describes ways to

acquire, store, process and analyse large-scale data

for which traditional techniques are not suitable and

therefore new system landscapes emerged (NIST

2019). Over time, many existing technologies,

concepts and application areas have been

scientifically analysed, and numerous new

approaches, techniques, systems and research

questions have been developed and described. As a

result, the body of literature for big data analytics

constantly increases and widens its range (Chen and

Zhang 2014; Staegemann et al. 2019b). However,

there are still many scientific gaps and one of them is

a lack of clarity, necessitating means of supporting

understanding and application of big data to facilitate

its application (Volk et al. 2019). To bridge this gap,

several taxonomies and classifications have been

developed and presented in certain big data related

topics and application areas, such as (Miller 2013;

Hartmann et al. 2016; Kumari et al. 2018).

A taxonomy is a classification, which divides its

subject into different categories, classes or families

(Nickerson et al. 2013). If applicable, further

hierarchical ramifications are listed. In that way,

many different fields are systematized, analysed and

overviewed. In the publication at hand, the

abovementioned big data taxonomies are the main

focus. Even though, those structures organize their

respective areas of interest, they themselves are not

Staegemann, D., Volk, M., Grube, A., Hintsch, J., Bosse, S., Häusler, R., Nahhas, A., Pohl, M. and Turowski, K.

Classifying Big Data Taxonomies: A Systematic Literature Review.

DOI: 10.5220/0009390102670278

In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 267-278

ISBN: 978-989-758-426-8

267

classified with regards to each other. This however,

would allow for a straightforward overview of the

domain and therefore increase accessibility.

Furthermore, potential contradictions among the

numerous taxonomies could be revealed, setting the

foundation for future work to increase the

corresponding consistency. Therefore, the following

research question is addressed in the course of this

work:

RQ: How can the existing big data taxonomies be

classified in relation to each other and which sub-

domains constitute a research gap in that regard?

To find a suitable answer for this RQ, first, it is

necessary to obtain an overview about existing

taxonomies. After that, an investigation about their

similarities and differences needs to be carried out. In

doing so, open research areas, for which no relevant

taxonomies have been found, may be discovered.

Hence, to answer the RQ, the following sub-research

questions (SRQ) will be answered:

SRQ1: Which big data taxonomies do currently

exist in the literature and which subject do they

cover?

SRQ2: How can the identified taxonomies be

classified?

SRQ3: In which way are taxonomies from the

same category complementary and state the same

conclusion?

SRQ4: Which sub-domains are not covered by a

taxonomy, but could benefit from the provided

clarity?

While SRQ1 has the purpose to identify the

according literature as a necessary foundation and

SRQ2 brings them into a joint systematic, SRQ3 aims

at investigating, to which extend there exists an

academic consensus regarding a topic. Finally, SRQ4

provides subsequent scientists with potential avenues

for further research, facilitating the advancement of

the domain.

To approach those questions, a systematic

literature review has been conducted (Webster and

Watson 2002; Levy and Ellis 2006). For this purpose,

a two-stepped refinement process was implemented.

While the first step is the collection of publications

whose titles comprise relevant keywords, in the

second step a qualitative analysis was carried out. To

ensure transparency, its process (Vom Brocke et al.

2009) as well as the results are thoroughly

documented in the following sections. While this

section constitutes the introduction and functions as a

motivation, the second section describes the search

for literature according to the topic as well as the

review and selection approach in detail. This has not

only been done to understand and comprehend the

results of this paper, but also to indicate the limits of

this literature search and define a clear border to

research articles, not considered in this work. The

third section presents and analyses the publications

obtained through the literature review. It sets existing

big data taxonomies and classifications in relation to

each other, in which way a bigger picture of the

existing literature arises and facilitates the

opportunity to outline the topic’s current state of

research. Furthermore, each taxonomy will be

described and the characteristics are highlighted.

Additionally, the taxonomies are reviewed and

eventual dissented views or gaps in the literature are

considered. The fourth section highlights the overall

findings and points out identified opportunities for

further research. In the end, a conclusion is given and

a possible way of expanding on the publication at

hand addressed.

2 LITERATURE REVIEW

To provide a transparent and expedient overview of

the current existing taxonomies in big data, this

structured literature review has been written in the

concept-focused style suggested by Jane Webster and

Richard T. Watson (2002).

2.1 Review Protocol

For locating and accessing the relevant literature, an

internet-based search has been conducted, using

different search engines and databases. Here, dblp

and IEEE Xplore were utilized to provide search

results from specifically computer science related

databases. In addition, to cover various resources and

different databases, the widely used scientific search

engine Google Scholar has been used as an all-around

search solution. To round off the search range, the

database searches from the scientific publishers

Springer (access via SpringerLink) and Elsevier

(access via ScienceDirect) were used as well.

Utilizing five different search engines from three

different contexts facilitates a representative view of

currently existing and relevant literature and therefore

ensures the quality of the search results.

To obtain the most relevant results, this literature

review is based on two search terms, consisting of

three keywords. The first search term is “big data

taxonomy” and the second is “big data taxonomies”.

Big data defines the field that has been targeted, while

taxonomy defines the actual objective that has been

aimed to be investigated. To limit the initial amount

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of found out papers and increase their relevancy, the

keywords had to appear in the title of the literature

item. This way a priority has been set to academic

publications, whose central research questions deal

with the development and presentation of taxonomies

related to big data. However, no other search

configuration such as time range, citations or similar

has been amended or set. Furthermore, all items

listed, including citation entries (appearing in Google

Scholar) have been selected to be reviewed. Using

this approach, an initial literature corpus has been

obtained. Table 1 shows the results of the search

attributed to each search engine.

Table 1: Search engine results overview.

Search Term Search Engine Results

Results

without

Duplicates

Big Data

Taxonomy

Google Scholar 51 42

dblp 16 16

IEEE Xplore 7 7

SpringerLink 2 2

ScienceDirect 4 4

Big Data

Taxonomies

Google Scholar 5 4

dblp 1 1

IEEE Xplore 0 0

SpringerLink 0 0

ScienceDirect 0 0

Total Unique Items

While Google Scholar, due to its nature as a meta

database, obtained the most results, it also contained

nine duplicates. Therefore, in a first processing step,

those duplicates had to be removed. Besides that,

some of the publication were found in more than one

search engine, necessitating further cleansing.

Finally, considering those adjustments, 47 unique

papers have been found as the output of the first step

of the literature review.

2.2 Qualitative Analysis

Since it is highly likely, that not all of those 47

obtained literature items are relevant in the context of

the publication at hand, they had to be further filtered

by the means of a qualitative analysis. For this

purpose, in the second step of the refinement process,

each one was completely read and subsequently

evaluated based on the inclusion and exclusion

criteria depicted in Table 2. This means, for a

publication to be included in the literature corpus, all

the inclusion criteria had to be met, while at the same

time, not a single one of the exclusion criteria applied.

Table 2: Inclusion and exclusion criteria.

Inclusion Criteria Exclusion Criteria

Literature is published in a

journal, conference, book, a

white paper or report (latest

edition only).

Publications refer or cite

taxonomies, that have not

been created for big data

research purposes.

Literature provides at least

one relevant big data

specific taxonomy.

Literature that presents a

taxonomy of which big data

is part, but not the main

research field.

If the publication is of an

interdisciplinary nature, the

discussed taxonomy

provides relevant

perspectives on big data.

Taxonomy/classification is

part of the data-set that is

examined in the publication

itself or in a data mining

context (for example attribute

value taxonomy).

Publication is written in a

language other than English

or German.

Literature items, that name

their research work a

taxonomy to describe the

synoptical and holistic nature

of the work, but do not

provide a specific taxonomy.

As a result of the filtering based on those criteria,

a set of relevant literature has been determined, which

comprises 28 items. Their breakdown based on the

type of literature is shown in Table 3. The table also

indicates the number of publications that were

removed in the previously mentioned step.

Table 3: Breakdown of the obtained literature items.

Type After Step 1 After Step 2

Journal Paper 24 19

Conference Paper 9 5

Book 5 1

Report/White Paper 5 3

Thesis 2 0

Unknown 2 0

Total included 28

Those 28 publications, as the findings of the

described structured literature review, constitute the

most appropriate and relevant works regarding the

research questions. Therefore, they are also the

foundation for the following considerations.

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3 CLASSIFICATION OF THE BIG

DATA TAXONOMIES

After obtaining the relevant literature, a thorough

analysis is conducted. On the one hand this provides

an overview of the existing taxonomies on an

individual level and on the other hand, it allows for

them to be transferred into a common structure,

showing existing research gaps.

3.1 General View

The qualitative analysis of the 28 paper revealed two

main classes, each reviewed taxonomy can be

assigned to. Those are “Technological” and

“Characteristics and Requirements”, which describe

the main nature of paper the taxonomy is embedded

in. The classification in its entirety is depicted in

Figure 1.

In this illustration, a classification tree is shown,

which assigns each taxonomy provided in the

literature to a research area of big data. The

classifications are not based on a complete division of

big data disciplines, however they show the thematic

reference of the taxonomies reviewed.

The category Technological classifies taxonomies

that either relate directly or have been created to lead

to existing or emerging software, software

architectures, tools, systems or algorithms. In most

cases they are named in the classification itself or are

subject of discussion, for which the taxonomy was

used. Hence, it takes a clear technical point of view.

Characteristics and Requirements however are

taxonomies that are of a descriptive or distinguishing

nature and focus on a management or character side

of big data and its fields and applications. From the

second level of the technological side, we have the

“Synopsis” subclass, which can be further divided

into “Platforms” and “Multidisciplinary”. While for

the naming of the latter category, contentwise,

Interdisciplinary would have been a slightly better fit,

this term was not used to avoid confusion with

another category of that name, which is introduced

later on. Synopsis refers to taxonomies that function

as an overall overview or are intended to classify big

data characteristics. If the taxonomy classifies big

data specific platforms or architectures, it is

categorised to Platforms. Multidisciplinary includes

overview taxonomies that are related to big data

either in a specific area of application or for a specific

set of data. Another sub-class below technological is

“Data”. Data as the key subject for big data is further

divided into “Data Acquisition”, “Data storage” and

“Data Analytics”. This classification is rather rough

and shall give a broad direction as the definition of

those disciplines in literature is slightly divergent. In

the course of this taxonomy, Data Acquisition is

regarded as the actual process of collecting data and

also comprises the different sources, data can be

extracted from, while Data Storage refers to the

actual process of storing data and the related software,

Figure 1: Categorisation of the taxonomies.

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hardware or structures for this task. Data Analytics

includes all kind of algorithms, techniques, software,

methods and concepts to pre-process or process the

data and gain information. This also includes

“Visualization” techniques, however for a better

overview, methods and ways to visualise data were

defined as another sub-class. “Security and Privacy

Techniques” as a sub-class of Technological

comprises straightforward techniques and methods to

secure the data and system landscape or increase

privacy. While in “Security and Privacy Aspects”

there is a similarly named sub-class below

Characteristics and Requirements, that one is from a

less technical position and more towards an analytical

or descriptive point of view. Another sub-class for

Characteristics and Requirements is “Business”. This

category highlights the economical side of big data,

which is due to its potential, of very high interest for

the public and private sector. The taxonomy further

comprises the “Interdisciplinary” and “Data

Analysis” sub-classes that again refer to big data used

for specific applications or fields and for analysing

large-scale data respectively.

3.2 Detailed View

Big data is a rather young and broad discipline with yet

no universal consensus on definitions and dividing

lines (Timmins et al. 2018). This is also recognisable

in the taxonomies created in this field. They are very

varied in size and details, and even though, sometimes

taxonomies deal with the same subject, they are still

not easily comparable and reflect a different approach

on the topic. However, two main categories were

identified, namely Technological as well as

Characteristics and Requirements. Additionally, for

each of those classes further sub-categories were

identified at which the found out taxonomies are

aligned. In the following subsections, each taxonomy

will be outlined by naming the source and giving a

brief description, to work out its context and

characteristics. Along with the summaries, an

evaluation of the existing concepts and the coverage of

the subfield is given. Because some of the analysed

publications are related to several sub-categories, they

will also appear repeatedly in the following sections.

However, the descriptions will differ in those cases,

since, depending on the context, other parts of the

publication are regarded.

3.2.1 Technological – Synopsis

In (Patgiri 2018), the authors present a classification

of big data into six different fields, which serves as a

synoptically overview that is further explained and

elaborated in sub-taxonomies. One of those is a

semantic depiction of big data, which focuses on the

“V”s big data is often being described with and

broadens them with further characteristics in the same

manner.

Another taxonomy that provides a holistic

overview about definition of big data, putting volume,

velocity, and variety in a relation, is proposed by

(Singh 2018).

In the second volume of the big data

interoperability framework of the National Institute

of Standards and Technology (NIST), a reference

architecture taxonomy is presented, that indicates

technologies, workflows and key roles of big data and

puts those in relation to each other. The report is

addressed to managers, procurement officers,

marketers, technical community and rather records

consensus on big data techniques and concepts than

focusing on a specific research question.

The paper entitled “A survey of big data

management: Taxonomy and state-of-the-art”

(Siddiqa et al. 2016) introduces a synoptic taxonomy.

This classifies big data into data storage, pre-

processing and processing and formulates for each

category three problems as well as a recommendation

of a technique or algorithm that can be applied.

Furthermore, the taxonomy matches six main big data

challenges to each of the solutions provided.

As one may note, two of the taxonomies in this

overview level are developed to define big data by

focusing on the characteristic “V”s, hence they are of

a complementary nature. The others position very

diverse in terms of the range and detail. However, the

division of big data itself into sub-categories is not

consistent and not transferable. It implies that there is

no agreement on a lucid big data overview, which

clusters big data into different sub-disciplines or

rather fields.

3.2.2 Technological – Data

Within big data literature, data are often being

described using their technical properties, such as

structure, format or attributes. Those information are

not covered within the taxonomies found, however

they partially refer to some of those aspects. A

taxonomy that deals with the data characteristics,

which are specific or relevant for big data has not

been detected in this search. According to the

taxonomies found, there is a measurement for data

size that leads to a classification. This approach was

introduced in (Fokoue 2015). In here, the concept of

a taxonomy uses a rather unique classification based

Classifying Big Data Taxonomies: A Systematic Literature Review

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on the ratio between the sample size of the data to be

dealt with and the size of the dimensions. Within the

described ratio and the sample size, six categories

emerge and are being discussed.

Within the contribution “Construing the big data

based on taxonomy, analytics and approaches”

(Pathak et al. 2018), a cross-functional view is

presented. By harnessing the idea of a taxonomy, an

approach dealing with different big data topics like

storage, analytics, state of the art and future trends in

a holistic way is proposed. It classifies data “based on

method of data collection, accessibility pattern,

source of data generation, and statistical approach”

(Pathak et al. 2018).

Another area, which was not ascertained and

might profit of a corresponding taxonomy is data

quality. Either referring to attributes that describe

data quality from a big data point of view, or possible

techniques, that respect or even increase data quality.

3.2.3 Technological – Data Acquisition

With the paper “Big Data Taxonomy” (Murthy et al.

2014) only one taxonomy was found that focuses on

the acquisition of the data itself. Along with a

classification of data structure, ahead of the taxonomy,

it provides a division of different industry domains and

subdomains in which (big) data is being generated.

The occurrence of only one particular

contribution within this subcategory may have

different reasons. Apart from the sole lack of

research, in terms of systematizations and

categorisations, also the general complexity of this

particular domain could be causative. In any case,

potential gaps that might be useful or possible to fill

are a clear overview of not only data sources, but also

the way they are collected and/or transferred. Further,

the data integration in terms of merging large-scale

data sets/databases together or integrating them into

an existing system landscape could be useful to

investigate further.

3.2.4 Technological – Data Storage

Further contributions have been found, which are

particularly focusing on the storing and management

of the data in different variations. Although there are

different approaches or variations in the main

emphasis, the academic assertion is very similar and

supplementary. In “Survey of Large-Scale Data

Management Systems for Big Data Applications”

(Wu et al. 2015) a three stepped procedure is

presented at which the management software used for

big data applications is explored. For each step a

separate taxonomy is developed. Firstly, the focus has

been set to the data point of view, describing physical

and conceptual levels. Secondly, a taxonomy of

system architectures is provided and different

approaches, as well as the related software solutions,

classified. In step three, the consistency model is the

point of attention. This last step deals with the

challenge of scaling down data management systems

without losing consistency. Again, different

approaches and software solutions are classified.

An entirely different approach that deals with the

existing storage possibilities was introduced by (Patgiri

2018). His taxonomy comprises four categories for

different existing storage possibilities. It focuses on

architecture, structure, implementation and usable

devices. Furthermore, NoSQL, which can be used for

the data storage, is regarded. To classify those

techniques and software, four paradigm classes are used.

A specific taxonomy that classifies different

database software products by the way data is being

stored is discussed in (Murthy et al. 2014).

Additionally to that, an overview table is presented,

providing detailed information regarding the

characteristics for each concept.

3.2.5 Technological – Data Analytics

There are various ways to prepare, process and

analyse data, yet there is no taxonomy found, that

synoptically creates an overview of existing methods

and sets those in relation to each other. Currently, the

existing classifications rather set the focus on specific

techniques or algorithms. In the already referred work

of (Patgiri 2018), one of the taxonomies provides

categories and sub-categories for the intention or

approach data is being analysed for. Another

presented taxonomy classifies machine-learning

algorithms into eleven different kinds.

Murthy et al. (2014) provides a brief overview of

the most used machine learning algorithms that are

used to analyse data. The algorithms are classified to

Supervised, Unsupervised and Semisupervised as

well as Re-enforcement.

In “Data discretization: taxonomy and big data

challenge” (Ramírez-Gallego et al. 2016) a

taxonomy, resulting out of the findings of a literature

review, is described. This approach classifies the

most important discretization methods into two main

classes with various different subclasses.

The paper of (Zerdoumi et al. 2018) presents a

classification of graph processing platforms, that are

mostly specialized in large-scale data. It can be used

where general-purpose systems might have

performance issue. Furthermore, they also proposed a

pattern recognition taxonomy specified for big data

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use. Within the four suggested categories and their

sub-categories, the different approaches of pattern

recognition have been technically explained and

comprehensively analysed.

By examining the current technologies of

information retrieval, a comparative taxonomy is

created in (Haneef et al. 2018). This classifies methods

or systems according to types and parameters.

“Bio-Inspired Algorithms for Big Data Analytics:

A Survey, Taxonomy, and Open Challenges” (Gill

and Buyya 2019) — This taxonomy categorises the

algorithms in swarm-based, ecological and

evolutionary. Furthermore, it provides references for

each algorithm with date and an example of

application.

In the paper entitled “A Survey of Clustering

Algorithms for Big Data: Taxonomy and Empirical

Analysis” (Fahad et al. 2014), the authors present five

different clustering approaches and classify 24

algorithms accordingly.

Gani et al. (2016) classify and sub-classify

current indexing techniques and algorithms based on

their strategy. Moreover, big data characteristics

(“V”s) have been applied to the taxonomy and mark

especially big data related techniques.

Due to the fact, that most of the aforementioned

taxonomies, related the data analytics, are very

different, a comparison is very limited. However, the

missing synoptical taxonomy would be beneficial to

holistically sum up the existing information and to

investigate, if relations among them can be found and

also to find superordinate categories. Nonetheless,

there are two taxonomies about machine learning.

They both refer to existing algorithms. However, they

differ in the number of algorithms sorted as well in

categories named. This might be due to the time gap

between the publication dates or due to the nature of

each publication. Though there are already a number

of taxonomies in this field, there might be some

concepts missing in this list. For example data

cleansing to detect corruptions or false data when

acting on a large scale. Although the taxonomies

listed above contain techniques and algorithms that

are being used for prescriptive or predictive analyses,

it might be beneficial to develop a categorisation that

focuses this use of big data. Additionally,

mathematical approaches used to analyse big data are

to our knowledge, not yet classified.

3.2.6 Technological – Visualization

Visualization of data is not a very recent topic,

however with the perspective of big data, there is only

one classification mentioned. The already referenced

work by (Murthy et al. 2014) also classifies the most

common visualization software and algorithms based

on the way they process data.

Hence, it might be worth developing other

approaches to categorise visualization techniques that

highlight current development or focus on other

aspects like suitability for certain datasets or with

regards to current big data architecture concepts. In

addition, a very specific but arising topic in the

domain are self-service tools to visualize the data

exploration.

3.2.7 Technological – Platforms

Concrete taxonomies related to the category of

Platforms were found in four publications. In (Patgiri

2018), different technologies are broken down into

three classes and subsequently described.

In the taxonomy of Murthy et al. (2014), big data

architectures are divided in the two main categories,

Batch and Streaming, referring to the approaches the

systems are based on.

The taxonomy presented in “Big Data 2.0

Processing Systems: Taxonomy and Open

Challenges” (Bajaber et al. 2016) presents the state of

the art for big data platforms in order to understand

the recent developments. It points out four main

groups, namely General Purpose Systems, Big SQL

Systems, Big Graph Processing Systems and Big

Stream Processing Systems.

In (Mohamed et al. 2019) a holistic taxonomy is

presented. It originates out of the findings of a

literature review and shows four stages of big data,

starting from source and format of data to data

processing, analytics and visualization. For each

stage, it categorises techniques and software and

creates sub-categories if needed.

The two compute infrastructure taxonomies are

very alike and only differ in details, while the other

two approaches are disparate. In comparison, the last

taxonomies indicate that there is no clear line between

big data platforms and platforms or techniques used

for other purposes or applications, as the

categorisations are very different in platforms and

software listed.

3.2.8 Technological – Multidisciplinary

Some of the found out taxonomies are dealing with

multiple disciplines. Kumari et al. (2018) present an

interdisciplinary taxonomy that focuses on

multimedia big data, which is being processed to be

used for IoT applications. The taxonomy is split in big

data architecture layers and technical functions and

broken down into further detailed sub-taxonomies.

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273

Along the taxonomy, further literature is referenced

and technical aspects are explained and compared.

Another multidisciplinary approach was

introduced in (Xu et al. 2017). This taxonomy

research refers to fault diagnosis in industrial big data

such as IoT or cloud computing, and combines it with

fault diagnostic methods that were commonly applied

in the time before big data became relevant. In this

way, the taxonomy is classified in three categories

with further sub-categories. Traditional methods are

separated from the ones based on industrial big data.

Shah et al. (2019) dealt as well with the big data

analytics and IoT. In particular, they defined a

taxonomy for disaster management processes,

comprising seven classes and their properties

respectively the utilized techniques.

The existing taxonomies are very detailed and

provide a specific point of view for their area of

application. However, some important areas might be

missing. For example, a taxonomy that refers to

business intelligence and the integration of big data in

the already existing architectures. Also big data used

for recommender systems or the development of

hardware particularly used for big data purposes

might be possible task for future research on

taxonomies or classifications.

3.2.9 Technological – Security and Privacy

Techniques

Apart from general concepts which are related to the

used analysis methods, tools, technologies and

application areas, also security and privacy

techniques were highlighted in some of the found out

taxonomies. In the paper entitled “State-of-the-art Big

Data Security Taxonomies” (Srinivasan and

Padmanaban 2018), a taxonomy is introduced that

focuses on Hadoop and Hadoop related systems.

Apart from listing the security essentials and levels, it

describes three categories in which the security

challenges and vulnerabilities have been sorted in.

Siddiqa et al. (2016) propose four different

categories for security in big data. For each of them,

two problems or issues are formulated as well as

possible solutions or approaches. Along with the

taxonomy, further details for this classification have

been given.

Within the very comprehensive work presented

by (Murthy et al. 2014) again relevant information

have been found, for this particular area. In here, most

common challenges regarding the technical aspects of

security and privacy within the big data architectures

and management are provided, grouped in main

categories and briefly described.

All three existing approaches regarding the

Security and Privacy Techniques show different ways

of dealing with this topic. While the taxonomy about

security challenges focuses on Hadoop respectively

Hadoop related systems only, the other two constitute

an overall approach, but with a very different

outcome. However, the last two mentioned

taxonomies are not developed within a publication

that focuses on security or privacy. Therefore, further

research could be instructive.

3.2.10 Characteristics and Requirements

All taxonomies listed in Characteristics and

Requirements focus on different views or applications

of big data and are hardly comparable. They reveal

specific information and provide conclusive details.

Sometimes, specific information are not only given

for a certain area or purpose, but also for a

multidisciplinary context.

Regarding the Business context, three

contributions have been found, that present promising

taxonomies. The paper “IDC's Worldwide Big Data

and Analytics Software Taxonomy” (Vesset et al.

2017) shows the big data and analytics software

market by creating three segments from the

conceptual architecture perspective. Although it

describes big data itself as a subset throughout all

three market segments, the taxonomy is more

comprehensive by including big data analytics

software and traditional software for data

management, analysis and visualization.

In (Hartmann et al. 2016), a taxonomy is proposed

that describes how companies gain value or monetize

data by focusing on six dimensions, which are

subsequently split into sub-clusters. Furthermore, the

taxonomy has been practically used by statistically

analysing a sample set of 100 start-up firms and

applying clusters on this sample according to the

taxonomy.

In the approach presented by (Linda and To

2014), big data characteristics from an organizational

point of view have been examined. It describes the

data sources and data structures that are important for

big data management.

Security and privacy Aspects were of major

interest in the contribution of (Custers and Uršič

2016). In a continuous text, which focuses on the

taxonomy developed for privacy reasons in big data,

a classification, taking in the view of a data controller

and data subject, has been worked out. It formulates

and defines different types of data reuse.

Characteristics and Requirements regarding the Data

Analysis were described in the paper “Analytical

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Competences in Big Data Era: Taxonomy”

(Hristozov et al. 2018). This taxonomy describes

competences required for data analysis in the big data

context. It formulates requirements and

characteristics of skills that are essential for

successfully analysing big data. Some of the found

out contributions were also Interdisciplinary. Miller

(2013) deals in his taxonomy with the risks that are

associated with the utilization of big data in cloud

computing.

Miloslavskaya et al. (2016) classify

characteristics of information system threats,

vulnerabilities, incidents as well as attacks against

security operation centres in a big data context. The

resulting taxonomy also defines relevant parameter

and gives further descriptions.

In (Krieger and Drews 2018), a taxonomy is

presented that focuses on big data with auditing

purposes such as accounting and fraud detection. It

defines dimensions throughout auditing, data

management and analytics and presents their

characteristics.

There are several other opportunities for research

that apply to this category. To name a few, business

intelligence and business decision making could be

potential topics. In addition, an overall classification

of application fields could advance research in big

data.

4 FINDINGS

With regards to the research question raised at the

beginning of this paper, the taxonomies resulting

from this literature search have been thematically and

hierarchically clustered, which provides a straight

forward outline of the current body of literature. It

already reveals the nature of each taxonomy as well

as areas with less devised taxonomies. Within each

subject area, the presentation of each taxonomy

shows its main topic as well as further observed

characteristics. It provides a more detailed overview

and has led to the previously described observations.

It is not always possible or expedient to compare

taxonomies that are sorted in the same area. However,

in some cases it reveals a lack of consensus while

others seem rather confirmatory. A significant

discrepancy seems to exist when dealing with the

actual big data techniques and platforms, which

indicates, that there is no distinct border among them

or for technologies used in big data and other

disciplines. The same observation is made for

dividing big data into sub-disciplines or sub-

categories on a basic level, or when dealing with

security and privacy terms. On the contrary, data

storage taxonomies are highly consistent and big data

taxonomies dealing with the typical “V”

characteristics are complementary. In terms of

taxonomy research carried out, there are areas of big

data with a higher coverage than others. When

looking at data visualization, data acquisition or the

characteristics and requirements of the data analysis,

those topics seem to be less extensively analysed than

other areas like for example, data storage, data

analytics and big data platforms. Nevertheless, nearly

in all areas, potential gaps for creating additional

taxonomies have been detected. Additionally, besides

the already mentioned specific topics, there was not a

single taxonomy found in the course of the conducted

literature research that deals with ways or methods of

testing big data solutions or systems. Equally, the

numerous potential causes for failures or quality

reduction in big data analysis (Staegemann et al.

2019b) were not part of the obtained considerations.

This emphasizes once more, that, while big data in

general is popular, the quality assurance is neglected

(Staegemann et al. 2019a). Furthermore, regarding

Characteristics and Requirements, there are some

other topics that might be beneficial. One example is

the training for human resources working with big

data or analysing big data platforms, taking into

account current legal conditions or recent changes.

Another one concerns the different possibilities for

the visualization of the results of an analysis. While

(Murthy et al. 2014) proposed an according

taxonomy, it focusses on graphical depictions. Hence,

other possible options, such as texts, tables or audio-

based representations, are not regarded. Relevant for

both categories, Technological as well as

Characteristics and Requirements might be to

investigate the current state of running big data

solutions cost-effective and with fewer resources.

This topic is important from both, an economical, but

also an ecological point of view and comprises

approaches like the optimisation of algorithms, the

choice of used hardware or considerations regarding

server consolidation and virtual machines placement,

with the latter already being covered by (Nahhas et al.

2019). However, since the consolidation is not

directly big data related, it is not part of the literature

review’s obtained results, despite being somewhat

relevant in the grand scheme.

5 CONCLUSIONS

In the presented research, a structured literature

review was conducted and thereupon used to find and

Classifying Big Data Taxonomies: A Systematic Literature Review

275

evaluate existing big data taxonomies, answering

SRQ1. Following the proposition of (Vom Brocke et

al. 2009), the search and review process is thoroughly

described to enable subsequent scientists to retrace

the results and build their own research upon them.

Each taxonomy has been described and, according to

SRQ2, categorised. Subsequently, the taxonomies

have been compared with the other ones from the

same category, providing the answer to SRQ3.

Furthermore, corresponding to SRQ4, potential

research gaps have been identified. While this list of

determined research gaps does not claim to be

exhaustive, it constitutes a starting point for readers

with expertise or experience in big data to identify

promising research topics. The formal creation of a

meta taxonomy for big data however, might be the

next step, expanding on the present work and

providing even more clarity. In this course, it could

also be beneficial to expand the scope of the regarded

literature, allowing to incorporate also taxonomies

that are relevant, but not directly aimed at big data.

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