A Data-Science-as-a-Service Model
Matthias Pohl, Sascha Bosse and Klaus Turowski
Magdeburg Research and Competence Cluster,
Faculty of Computer Science, University of Magdeburg, Magdeburg, Germany.
Data-Science-as-a-Service, Cloud Computing, Service Model, Data Analytics, Data Science.
The keen interest in data analytics as well as the highly complex and time-consuming implementation lead to
an increasing demand for services of this kind. Several approaches claim to provide data analytics functions
as a service, however they do not process data analysis at all and provide only an infrastructure, a platform or
a software service. This paper presents a Data-Science-as-a-Service model that covers all of the related tasks
in data analytics and, in contrast to former technical considerations, takes a problem-centric and technology-
independent approach. The described model enables customers to categorize terms in data analytics environ-
An increasing keen interest in data science and data
analytics exists as reported by a trend analysis of
the last 5 years (Google Trends). The data-intensive
world allures with generating revenue from analy-
zing information and data that are simply and quickly
available. Different approaches for knowledge dis-
covery (KDD) (Fayyad et al., 1996) or business-
related data mining (CRISP-DM) (Shearer, 2000)
are in use. However, proceeding is highly com-
plex, time-consuming and needs expertise in diffe-
rent disciplines, either computer sciences, mathema-
tics or a context-related specialization. The motiva-
tion within a company could arise from preventing
downtime of machines, getting insights about custo-
mer relationships or optimizing business processes.
If the required expertise for data analysis cannot be
provided internally, the tasks can be forwarded to
external consulting services. By using such servi-
ces it is possible to compensate the lack of exper-
tise, but it is very cost-intensive and still extremely
time-consuming. The paradigm of cloud computing
(Mell and Grance, 2011) establishes service concepts
that seem to be able to solve the remaining issues.
Among concepts like Infrastructure-as-a-Service
(IaaS), Platform-as-a-Service (PaaS) or Software-
as-a-Service (SaaS) that revolutionize computing
on several layers, service models like Analytics-
as-a-Service, Data-Analysis-as-a-Service, Business-
Analytics-as-a-Service or Big-Data-as-Service were
conceptualized. Whether these approaches are suit-
able recommendations, decision or application servi-
ces for non-expert customers or just analogies to stan-
dard concepts is not clarified. Furthermore, a custo-
mer is confronted to choose within a set of apparently
unclear buzzwords like data science, data mining, big
data analytics, etc.
Therefore we make a predefinition for the usage
of these terms and will diffuse a definition through
argumentation. The whole process that contains data
provision, data preparation, data analysis and data vi-
sualization is called data science in this paper. Data
analytics, business analytics and big data analytics are
often used synomously for data science, however they
could differ in context of use. Data mining is a key
term in most of related works and is used in varient
different ways. We will take this term as a byword for
data analysis.
This paper has twofold objectives. Firstly, it will
provide a full-service model that will be extracted
from existing approaches and will address data ana-
lytics services. Secondly, it will discuss the arising
service offers and data science process steps. The
subsumption of service models simplifies the asses-
sment of IT service offers for companies that plan to
get insight from their data. From a scientific view,
a guideline is drawn for a future usage of terms in
data analytics environment and a classification of past
work is a point of interest. The structure of the paper
is as follows. A knowledge base about related work
is presented in the second section. The review is built
up on a cross-reference search for data analytics ser-
vices and data mining process steps (Webster et al.,
Pohl, M., Bosse, S. and Turowski, K.
A Data-Science-as-a-Ser vice Model.
DOI: 10.5220/0006703104320439
In Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018), pages 432-439
ISBN: 978-989-758-295-0
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2002). The third section shows a Data-Science-as-
a-Service model that combines the different steps in
data mining as well as a cloud computing service mo-
del. Section four demonstrates examples of existing
services that are offered by known IT service provi-
ders. A discussion that argues aspects and challenges
from a technical and a scientific view as well as con-
cludes the paper.
There are numerous works that address data mining,
data analytics, data science or similar. First of all, we
want to have a look on frameworks that are related
to data mining, data analytics and data science. In
case of cloud computing, different services that pro-
vide data analytics are already developed. Next to
data analytics services we will consider data science
process steps and focus on it in further search. In
order to retrieve relevant works, we took search en-
gine (Science Direct, Scopus) and knowledge databa-
ses (DBLP, IEEE, ACM) into account.
2.1 Data Science Frameworks
The data science process shall end up with know-
ledge. The key step in this nontrivial process is cal-
led data mining (Fayyad et al., 1996). Therefore data
has to be processed from selected sources and trans-
formed into a proper format. CRISP-DM (Shearer,
2000) describes a process cycle that is similar to Kno-
wledge Discovery in Databases (Fayyad et al., 1996),
but points out the business understanding that is ne-
cessary for initiating because data mining goals will
be defined on that basis. Respecting both frameworks
it is conspicuous that data mining is a name of a pro-
cess step in KDD and entitles the whole reference
model CRISP-DM. In (Kurgan and Musilek, 2006),
a review of data mining frameworks is given. There
exist various derivates of the mentioned frameworks
with relation to a specific field of application. In (Sun
et al., 2017), the authors add data analytics that also
contains data mining as a significant part to the dis-
cussion and observe it as an aggregation of data ana-
lysis, data mining, data warehouse, statistic modeling
and data visualization. Furthermore the authors aim
at identifying an ontological separation between data
analytics, business analytics, knowledge analytics and
big data analytics. In (Nalchigar and Yu, 2017), a
conceptual modeling framework for business analy-
tics is proposed and provides catalogues for business
questions, analytic algorithms and data preparation.
In this manner, a transmission between business re-
quirements and algorithmic implementation is esta-
blished. With the evolution of a data-intensive world
new technologies are needed for handling rapidly gro-
wing, variously shaped mass of data (Laney, 2001).
In (Maltby, 2011), the author reviews big data with
relation to analytic techniques and mentions that data
mining combines statistics and machine learning with
database management. Machine learning is not a new
thing (Michalski et al., 1983) and focuses on ”au-
tomatically learn to recognize complex patterns and
make intelligent decisions based on data” (Maltby,
2011). Data analysis, machine learning and data vi-
sualization are marked as the core disciplines of data
science that is a new paradigm in the field of big data
(Concolato and Chen, 2017). However, there is no
simple and unified data science framework (Marvasti
et al., 2015). Such frameworks are often deep into
specific context-related data and implemented in an
application environment (Brough et al., 2017; Loet-
sch and Ultsch, 2016). In (Cleveland, 2001), data ana-
lysis is described as an enlargement of statistics com-
bined with data computing and is called data science.
Obviously, there exists discordance about the terms
data science, (big) data analytics, data mining, etc.
However, it occurs that all definitions come up with
a similar structure. Data has to be selected, prepared
and analyzed to show up new information. However,
the needed expertise and the lack of uniformed pro-
cesses lead to the fact that data analytics is more an
art than science (Zorrilla and Garc
ıa-Saiz, 2013).
2.2 Data Science Services
The thought of automation without requiring human
interaction, the capability solving small and big data
problems, the availability as well as flexibility in com-
puting power and storage characterize the essentials
of cloud computing (Mell and Grance, 2011). Dif-
ferent concepts that offering machines (IaaS), opera-
ting systems (PaaS) and applications (SaaS) are alre-
ady defined and discussed (Dillon et al., 2010). There
exist approaches for hosting data analytics environ-
ments in a cloud. In (Xu et al., 2015), the authors aim
at making real-time data analytics available as a ser-
vice and deal with the challenges of creating service
interfaces, wrapping existing big data frameworks and
real-time processing of data. In (Zorrilla and Garc
Saiz, 2013), an approach is motivated with some ex-
emplary services (e.g. GoodData, IBM Smart Analy-
tics) and it is concluded that none of them wraps the
whole analytic process that is described in KDD or
CRISP-DM. The proposed framework contains servi-
ces for the steps of data preparation, data analysis and
data visualization and is layer structured from an en-
A Data-Science-as-a-Service Model
terprise perspective. The concept is implemented as
a SaaS, though its automation is not sufficiently dis-
cussed. In (Ribeiro et al., 2015), the authors provide a
Machine-Learning-as-a-Service approach and discuss
the low flexibility of implementing new algorithms on
existing platforms like PredictionIO or OpenCPU. In
(Xinhua et al., 2013), Big-Data-as-a-Service is defi-
ned with getting insight from big data, which charac-
terizes this as big data analytics. In (Ardagna et al.,
2016), the authors describe the Big-Data-Analytics-
as-a-Service paradigm as suitable of companies do
not have enough data scientists. Along to service re-
quirements (data preparation, data analysis, data vi-
sualization), various challenges (data quality, diver-
sity, security, privacy) that come up with handling vast
data are examined by the authors. In (Grossman et al.,
2016) the variations of data science services (infra-
structure, platform, software, support) and a colloca-
tion of these is mentioned. The authors also describe
requirements like API-based access, data portability,
data peering and pay-for-compute, however automa-
tion in processing is not addressed. In (Medvedev
et al., 2017), an approach for setting up data mining
processes in a cloud environment is proposed and al-
lows a user to model a data mining process within the
steps of data uploading, data pre-processing, data mi-
ning and presentation. Several Analytics-as-a-Service
providers are compared in (Naous et al., 2017) with
respect to core features of data sources, data proces-
sing & preparation, analysis, visualizations and even
platform and infrastructure propositions. In conclu-
sion all provided services have a self-service charac-
2.3 Data Science Process Steps Services
After terms and existing data science services have
been discussed, we want to focus on the steps that
represent the parts of the data science process. Com-
panies are often interested in analyzing internal and
external business-related data. Within the scope of di-
gitalization, Internet-of-Things (IoT) applications are
in widespread use. In conjunction with that, a lot of
data services are formed that could have their origin
in data crawling concepts. In (Haase et al., 2011), an
information workbench for linked data applications is
provided, and a structured crawling system to gather
linked data over the web with standards like RDF is
proposed in (Isele et al., 2010). However such ser-
vices are known and described as Data-as-a-service
(Delen and Demirkan, 2013). In (Terzo et al., 2013),
Data-as-a-Service is interpreted as a storing and pro-
cessing service and the authors proposed a layer ar-
chitecture of an IaaS. In (Seibold and Kemper, 2012),
different types of Database-as-a-Service (shared ma-
chine, shared processes, shared tables) are described
and can appear as SaaS, PaaS and IaaS, which de-
pends on the complexity of the delivered systems. In
(Curino et al., 2011), the authors point out some pro-
perties (efficient multi-tenancy, elastic scalability, and
database privacy) that have to be realized which is
confirmed in (Agrawal et al., 2009). With respect to
data storing, data integration is similarly important.
In (Riedemann and Timm, 2003), the authors draw
the ”vision to achieve automated just-in-time integra-
tion”, and in (Bergamaschi et al., 2008) the idea co-
mes up that ”data integration systems is on producing
a comprehensive global schema successfully integra-
ting data from heterogeneous data sources”. In (Co-
hen and Richman, 2002), it is mentioned that data ma-
tching and clustering algorithms can create a solution
for these problems. At this point an intersection with
data preparation is obvious. ”Data preparation is an
important and critical step for complex data analysis”
(Yu et al., 2006). A handbook for data preparation is
provided in (Pyle, 1999) and describes the access of
data, data discovery and data modeling. It also focu-
ses on data mining and determines that ”the process
of data science is smooth and backward adjustment is
possible”. In (Yu et al., 2006), the authors mention
problems in data preparation like incomplete data,
noisy data, inconsistent data, selecting relevant data,
reducing data and resolving data conflicts, however,
they notices some solution approaches. In (Narman
et al., 2009), a model-based method for detecting
data accuracy problems is proposed. The straighte-
ned data has to be organized, so data modeling is
a further sub-process. In (Duggan and Yao, 2015),
the authors describe ”an approach [for] automating
most of this work, building data models from speci-
fications of a data collection system”.In (Song et al.,
2015), the authors address problems like combinato-
rial complexity, scattered modeling rules, semantic
mismatch, inexperience of novice designers, incom-
plete knowledge of designers and multiple solutions
in data modeling and give some solution techniques
that are categorized in linguistics based (e.g. NLP),
pattern-based, case-based, ontology-based and multi-
techniques-based. At this point, one can see a rela-
tion to machine learning techniques that can also be
used for outlier detection (Hawkins et al., 2002; Pru-
engkarn et al., 2016) or data matching (Rong et al.,
2012). The overall modeling concept could be na-
med as data warehousing. A transformation approach
from operational schemes to data warehouse is pre-
sented in (Dori et al., 2008). Such a data structure
changes with time and arising amount of data, so a
framework for real time data warehousing is suitable
CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science
(Farooq and Sarwar, 2010). With the usage of ma-
chine learning techniques, one can see a connection to
data analysis tasks. The main aim is to automate the
task of selecting algorithms that analyze the data. In
ıa-Saiz and Zorrilla, 2017), a framework is pre-
sented that includes a meta-learning approach. With
model-based, data contextual, information theoreti-
cal, complexity and statistical meta features a system
learns the way of appropriate algorithm selection for
a common problem. In (Luo, 2016), the author pro-
vides a literature review on automatic algorithm se-
lection for machine learning and categorizes different
approaches. It also exposes that the parameter and fe-
ature selection is an important part. In (Langley et al.,
1994), an approach with a heuristic search while in
(Hall, 2000) a correlation-based feature selection is
described. A different approach is introduced in (Es-
pinosa et al., 2013), in fact a taxonomy that provides a
recommendation about using data mining methods for
non-expert data miners. After these steps, a suitable
visualization or presentation of the results is neces-
sary. In (Matsushita et al., 2004), the authors focus on
an automated visualization of user required informa-
tion via processing data frames. In (Andrienko and
Andrienko, 1999), a data characterization scheme for
automated data visualization is provided. The cycle
between data analysis and data visualization is picked
up in (Wagner, 2015) where a process of reconfigu-
ring data analysis with ideas coming from visualiza-
tions is introduced. The essential challenges are con-
cluded in (Xu et al., 2015). The creation of service
interfaces that are suitable for integrating and proces-
sing distinct data sources in combination with analy-
zing frameworks and that are also utilizable for diffe-
rent applications is paired with the ability of real-time
Considering prior works in the field of data science
and correlated disciplines, a Data-Science-as-a-
Service model will be deduced in this section. The
majority of the existing approaches provide IaaS,
PaaS or SaaS models that enable users to conduct data
science. However, for serving data science there has
to be a service level above the known cloud compu-
ting models (Mell and Grance, 2011) that covers the
whole data science process. The earlier frameworks
and theoretical data mining approaches overlap in the
core steps of accessing data, arranging data, analyzing
data and presenting results. The automatization of the
process steps leads to separated services that can be
used as standalone ones. Therefore, we will describe
some entry and exit points of the sub-services to show
the feasibility of service separation. An entry point
is referred to a condition for initiating a sub-process.
An exit point is defined as a point at which results are
3.1 Data-as-a-Service
There have to exist data (Fayyad et al., 1996; Shearer,
2000) for a data science process. Either data is uplo-
aded or collected by a user (Naous et al., 2017; Med-
vedev et al., 2017) or integrated from data services
(Delen and Demirkan, 2013) in an adequate storage
system. The aim of this step called Data-as-a-Service
is providing a base of data that ideally cover all related
information. A first entry point of this service model
is a chunk of data or an information request that could
be represented by keywords. On basis of the extracted
meta data or keywords related data could be provided
via data services. Hence, an exit point is the provision
of required data or an index-linked data pool that is,
for instance, stored in a traditional database or a dis-
tributed file system. It is shown that data services can
be connected even with other data sources and sup-
plied on appropriate infrastructure (Vu et al., 2012;
Isele et al., 2010; Bergamaschi et al., 2008).
3.2 Data-Preparation-as-a-Service
The process step that follows after data provision is
data preparation. It is termed as data selection, data
preprocessing, data transformation (Fayyad et al.,
1996), directly data preparation (Ardagna et al., 2016;
Naous et al., 2017; Shearer, 2000), data modeling or
generally data warehousing. However, all of these
terms are covered by Data-Preparation-as-a-Service.
Next to the previous service step a user-provided data
pool could also be an entry point for this service, ho-
wever, one has to consider that at least infrastructure
(IaaS) is needed. An exit point is a fully organized
set of data that is suitable for data analysis. In case of
unstructured or semi-structured data the data prepara-
tion service step is necessary before starting the data
analysis service. Following (Duggan and Yao, 2015;
Song et al., 2015; Yu et al., 2006; Narman et al., 2009)
it is possible to arrange data automatically after clean-
3.3 Data-Analysis-as-a-Service
The key step in the majority of the related approaches
is called data mining (Fayyad et al., 1996; Medvedev
et al., 2017; Shearer, 2000; Zorrilla and Garc
A Data-Science-as-a-Service Model
2013; Sun et al., 2017). Data analysis is also used for
indication (Xinhua et al., 2013; Naous et al., 2017;
Sun et al., 2017). The term data analytics that we in-
troduced as a synonym for data science is seldomly
mentioned (Ardagna et al., 2016), just like (machine)
learning and predicting (Ribeiro et al., 2015). Howe-
ver, all the approaches use the referred terms in sense
of Data-Analysis-as-a-Service. The concept of data
mining transposes the idea of creating data by pre-
dictive or prescriptive analysis. Nevertheless, in ge-
neral it is a derivation of data analysis. In a stand-
alone service consumption an entry point could be
a well-structured data set that will be used as input
data for analysis procedures and algorithms. An eva-
luated output (e.g. a data frame) is a possible exit
point. The automatic selection of algorithms (Luo,
2016) and a self-learning application system (Garc
Saiz and Zorrilla, 2017) enable such a service.
3.4 Data-Visualization-as-a-Service
The last service step adresses data visualization which
is a term that has been chosen by most researchers
(Ardagna et al., 2016; Xinhua et al., 2013; Naous
et al., 2017; Sun et al., 2017; Zorrilla and Garc
2013). However, an interpretation of results (Fayyad
et al., 1996), a deployment (Shearer, 2000) or a struc-
tured output for a customers product (Xu et al., 2015)
is mentioned. An entry point could be structured data
that should only be visualized. However, a service for
distributing analysis results is also conceivable that
could be seen as the exit point. Approaches by (Mat-
sushita et al., 2004) and (Andrienko and Andrienko,
1999) can facilitate a visualization service.
Figure 1: Data-Science-as-a-Service model.
3.5 Data-Science-as-a-Service
The Data-Science-as-a-Service model (Fig. 1) com-
bines the explained services and realizes the whole
data science process. Data-Science-as-a-Service is
the ordered sequence of Data-as-a-Service, Data-
Preparation-as-a-Service, Data-Analysis-as-a-Service
and Data-Visualization-as-a-Service. The entry
points of Data-Science-as-a-Service are equal to the
ones of Data-as-a-Service. Exit points could be de-
rived from Data-Visualization-as-Service. Conside-
ring the model a combination that does not contain all
of the sub-processes is also conceivable. The entry
and exit points of the sub-services enable the usage
of sub-sequences. For instance, one could getting
started with a information requirement at Data-as-
a-Service and quit with a well-structured dataset at
Furthermore, the idea of backward adjustment
(Pyle, 1999; Wagner, 2015) is included. In case a
service result would not fulfill the customer require-
ments a service step could be re-processed or a previ-
ous service step could be invoked. For instance, if the
output result of the data analysis service is not suita-
ble or applicable then the step could be repeated. At
this point the service system gets the possibility to le-
arn and can forward the demand of advancement if it
is not successful. Otherwise the system rolls back to
the underlying Data-Preparation-as-a-Service to rear-
range the data (e.g. new metrics) to gain better results
in the next step. This functionality is only useable in
case of requesting more than one service.
The essential characteristics of cloud computing
(Mell and Grance, 2011) are necessary for provi-
ding Data-Science-as-a-Service. Although the data-
base services or data integration services are part of
Data-as-a-Service these services have to be supported
by infrastructure, platform and software to process all
of the service steps. At this point we do not want to
categorize the underlying services. Nevertheless, re-
source elasticity, service measurement and broad net-
work access is required to offer Data-Science-as-a-
Data analytics could be used as a similar expres-
sion for data science. Normally the initiation of a
data science process is forced by a user or business
requirements that expect some kind of knowledge or
insight from data. Thus, the terms business analy-
tics, Insight-as-a-Service or Knowledge-as-a-Service
(Terzo et al., 2013) are used. In case of big data pro-
blems, there exist different approaches about so cal-
led Big-Data-Analytics-as-a-Service or Big-Data-as-
a-Service. However, all of these concepts are covered
by the presented Data-Science-as-a-Service concept.
CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science
In the previous section we presented a Data-Science-
as-a-Service model. In (Naous et al., 2017), it is
shown that all of the data science cloud computing
offers (e.g. Google Cloud Platform, IBM Bluemix,
SAP Cloud Platform, Amazon Quicksight or Good-
Data Platform) are some kind of self-service via soft-
ware or platform. The GoodData and SAP Cloud
platform cover the most service steps, from data pro-
vision up to visualization. However, if one has a
look on the current product line of Google Services
all data science process steps are covered (e.g. Big-
Query, CloudStorage, DataFlow, TensorFlow, Pre-
diction API, Charts and Firebase). Nevertheless none
of the listed providers offers a completely data science
service that proceeds (nearly) automatically.
Observing Google’s image recognition tools we
find a demonstrating example for Data-Science-as-
a-Service. For instance, Google Goggles combines
the whole sequence of the Data-Science-as-a-Service
model. The starting point of the service is a taken
picture of a user. Google provide databases with
website links and images as Data-as-a-Service. The
image of a user has to be transformed in a suita-
ble format for further processing (Data-Preparation-
as-a-Service). Google’s machine learning algorithms
will detect patterns of interest by means of, for in-
stance, trained neural networks (Data-Analysis-as-
Servce). Afterwards a presentation of the detected
image areas and referred website links is given (Data-
Presentation-as-a-Service). Furthermore, the service
can learn from a rating of the result regarding if a user
is satisfied or not. In the unsatisfied case enriching
the databases or implementing new algorithms could
be fitting solutions. New computation methods may
assume reprepared input data. Although, new data
definitely involve a repreparation. Hence, we observe
the formerly mentioned backward adjustment.
There is a many-faceted choice of products that range
from IaaS over PaaS to SaaS. Providing a platform or
a software that allows to conduct data science is not
Data-Science-as-a-Service, however such an offer is
not possible without. Data-Science-as-a-Service is a
symbiosis of infrastructure, platform, software and
the processing of data science tasks. This can be done
automatically or semi-automatically, e.g. with opti-
ons of user interactions for launching further service
steps or re-processing. Otherwise there would
not be an added value for a customer in compari-
son to common services. Considering (Vargo and
Lusch, 2004), value only results from the beneficial
application of data science services where the infra-
structure is a transmitter. Furthermore, the essential
characteristics of cloud computing like on-demand
self-service, broad network access, resource pooling,
rapid elasticity and measured service are given. The
same refers to the deployment models. Considering
the service measurement one could think about new
business models that specify a typical pay-per-use
model. Among a pay-per-compute, a pay-per-insight
model is potentially conceivable. Measurements
could also differ on service levels. Ultimately, it is
an existing future task, because it drops the question
how to measure insight in that case. We have shown
that a data science service system is feasible with
the connection of several service steps. However
we describe an overall service model and not the
orchestration of services in detail. The connection
between the service steps has to be observed in
future. There exist different frameworks (e.g. RDF)
or markup languages (e.g. PMML) that can be used
to determine utility. Challenges will arise to achieve
smooth transitions, even though when a service
step is skipped by a customer or the functionality
of backward adjustment is applied. Therefore it is
not even called service layers, because the usage as
stand-alone services is also possible. ”Information
systems that provide such capabilities are often called
business intelligence tools nowadays” (Delen and
Demirkan, 2013). Indeed one can see parallels to
business intelligence (BI). Comparable concepts are
given in the theory of decision support systems (DSS)
that could be included in future research. Especially
BI tools focus on a technical layer structure for col-
lecting business data, re-arrangement and reporting.
However, there also exists an alternate definition
that sees analytics as a subset of BI (Davenport
et al., 2001). From a point of service a data/business
understanding is not necessary. The customer is
forced to input its information demand and to check
the knowledge outcome on its suggestions. If an
automated data science service system is able to
govern requirements and in-depth knowledge one
might call it Artificial Intelligence. Associating
decision support decision model presentation could
be included in Data-Presentation-as-a-Service in
future. With the given service models that orientate
towards the key steps of data mining it is possible to
characterize terms that occurs in the analytics service
field. One has only to decide if a term is related to
a sub-process or the whole system. Provisioning
A Data-Science-as-a-Service Model
an ontology in the field of data analytics will be a
prospective aim.
In this paper, a service model framework for data
science was created. It enables business and scientific
customers to classify offers of common data science
services and to substantiate their expectations. The
results are furthermore useful as a template for crea-
ting data science services.
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