Data Mining definition services in Cloud Computing with Linked Data

Data Mining definition services in Cloud
                                                         Computing with Linked Data
                                             Manuel Parra-Royon1 , Ghislain Atemezing2 , and J.M. Benı́tez1
                                                  Department of Computer Science and Artificial Intelligence, Universidad de
                                                          Granada, Spain. {manuelparra, j.m.benitez}
arXiv:1806.06826v2 [cs.DC] 24 Jun 2018

                                                           Mondeca, Paris, France.

                                                       In recent years Cloud Computing service providers have been adding
                                                  Data Mining (DM) services to their catalog. Several syntactic and seman-
                                                  tic proposals have been presented to address the problem of the definition
                                                  and description of services in Cloud Computing in a comprehensive way.
                                                  Considering that each provider defines its own service logic for DM, we
                                                  find that using semantic languages and following the linked data proposal
                                                  it is possible to design a specification for the exchange of data mining ser-
                                                  vices, achieving a high degree of interoperability. In this paper we propose
                                                  a schema for the complete definition of DM Cloud Computing services,
                                                  considering key aspects such as pricing, interfaces, experimentation work-
                                                  flow, among others. Our proposal leverages the power of Linked Data
                                                  for validating its usefulness with the definition of various DM services to
                                                  define a complete Cloud Computing service.

                                         1        Introduction
                                         Cloud Computing has been introduced into our daily lives in a completely trans-
                                         parent and frictionless way. The ease of Internet access and the exponential
                                         increase in the number of connected devices has made it even more popular.
                                         Adopting the phenomenon of Cloud Computing means a fundamental change
                                         in the way Information Technology services are explored, consumed, deployed or
                                         simply used. Cloud Computing is a model of providing services to companies,
                                         entities and users, following the utility model, such as energy or gas, among
                                         others. The objective is therefore a model of service provision where computer
                                         resources and computing power are contracted through the Internet of services
                                             Internet services are a great advantage for an organization, among them you
                                         can access it from anywhere and from various devices, also very cost-effective
                                         in terms of software, hardware and technical maintenance. But if there is one
                                         thing it stands out for, it is scalability, a fundamental feature of any business
                                             Major Cloud Computing providers are currently leveraging their vast com-
                                         puting infrastructure to offer a vast sort of services to enterprises, organizations
                                         and users. These services work within a Cloud Computing platform and are

thus services ready to be consumed by users. The definition of a service is
not only indicated by its functionalities, but also implies specific factors for
the consumption of the services by the users such as the methods of access to
the service or the interaction, Service Level Agreements (SLA), Service Level
Objective (SLO), pricing, authentication, catalogue of services and others.
    The effectivity of Cloud Computing would be greatly improved if there were
a general standar for services definition. The definition of Cloud Computing
services lacks a clear standardisation [17]. Each service provider offers a nar-
row definition of services, which is generally incompatible with other service
providers. For instance, where one provider has a SLA term, another provider
has another name and features for that term, although the two might be the
same. This makes it difficult to define services or service models independent
of the provider as well as to compare services through a service broker [33]. A
standardization of the definition of services would boost competitiveness, allow-
ing third parties to operate with these services in a totally transparent way,
skipping the individual details of the providers.
    For just a few years now, service providers have been adding specific services
for Data Mining. The increase in the volume of data generated by companies
and organizations is growing at an extremely high rate. According to Forbes1 ,
in 2020, the growth is expected to continue and data generation is predicted to
increase by up to 4,300%, all motivated by the large amount of data generated
by service users. By 2020, it is estimated that more than 25 billion devices
will be connected to the Internet, according to Gartner [26], and that they will
produce more than 44 billion GB of data annually. In this scenario, Cloud
Computing providers have successfully included data analysis services in their
catalogue of services, as a demand for massive processing of data.
    This massive data gathering, storage and processing is known as Big Data
[16], and not only takes into account the massive volume of data, but also
includes aspects of knowledge extraction, decision making or prediction, among
others. These techniques applied to large-scale and ever growing data require
a redesign of algorithms [42] and new hardware and software infrastructures
to deal with the analysis and processing of data, which would otherwise be
impossible to achieve.
    These services allow the application of Artificial Intelligence (AI) and Ma-
chine Learning (ML) techniques on a large variety of data and they offer an
extensive catalogue of techniques, algorithms and work-flows related to DM.
Services such as Amazon SageMaker 2 , or Microsoft Azure Machine Learning
Studio3 among others (Table 1), offer these algorithms as services within cloud
platforms. Following this line, other Cloud Computing platforms such as Algo-
rithmia 4 or Google Cloud ML5 among others, offer ML services at the highest
level, providing specific services for the detection of objects in photographs and
video, sentiment analysis, text mining or prediction for instance.
    There are several proposals for the definition of services. These proposals
cover an important variety of both syntactic and semantic languages in order


to achieve a correct definition and modelling of services. For the definition of
this type of DM services, there is no specific proposal, due to the complexity
of the services represented. Solutions based on the proposal offered by Linked
Data [7] can solve the problem of defining services from a perspective more
    Linked Data undertakes models and structures of the Semantic Web, a tech-
nology that aims to expose data on the web in a more reusable and interoperable
way with other applications, exploiting the relationships that exist between data,
both internal and external. The Linked Data proposal allows you to link data
and concept definitions from multiple domains through the use of the Semantic
Web [5] articulated with RDF [28] or Turtle [3] languages, among others. The
Linked Data proposal is based on four key aspects, such as: a) the use of URIs
to name things, real world objects and abstract concepts, b) the use of http
URIs, which makes it easier to find resources that are published, c) the query
of resources RDF with SparQL [41] for the discovery of relationships and enti-
ties, d) the inclusion of links to other URIs, which complement the definitions
with other external ones, thus maximizing the reuse and interlinking of existing
data. It is considered a richly interconnected network of resources, definitions
and relationships that can be processed programmatically by machines.
    The main objective of this work is the definition of DM and Machine Learn-
ing (ML) services for Cloud Computing using the Linked Data principles. The
definition of the service is not only focused on the service, but also allows the
definition and modelling of prices, authentication, SLA or catalogue. For the
modelling of the different components of the service, existing schemes and vocab-
ularies have been used which have been adapted to the problem of the definition
of DM services. In addition, new vocabularies have been created to cover spe-
cific elements of the domain of the problem that have not been available until
now. We present dmcc-schema, a proposal based on Linked Data to cover the
entire definition of DM/ML cloud services. The dmcc-schema proposal provides
a complete vocabulary for the exchange, consumption and negotiation of DM
services in Cloud Computing.
    The paper is structured as follows: In the next section we present the related
works in Cloud Computing services definition. Section 3 presents out proposal,
the dmcc-schema in detail, and depicts all components with its interactions. We
describe in section 4 a few cases for a real service, providing of a Random Forest
algorithm as service and including each additional aspects related to the service
definition in Cloud Computing, such as SLA or prices of the service, among
others. Finally, conclusions and future works are included in section 5.

2    Related work
DM and ML is a very highly relevant topic nowadays. Those areas of knowledge
provides entities, organizations and individuals with tools for data analysis and
knowledge extraction at all stages. In addition, if we add to this the increase
in Cloud Computing and Edge Computing[43], these DM services are taking
a significant position in the catalogue offering by providers. The complexity
of DM/ML services requires a complete specification, that is not limited to
technical or conceptual considerations. The definition, therefore, must integrate
key aspects of the service into the Cloud Computing environment.

The definition of services in general has been approached from multiple
perspectives. At the syntactic level with the reference of Services-Oriented-
Architecture [38] (SoA) and XML [8] (Extended MarkUp Language) derived
languages such as WSDL [9], WADL [20], or SoAML [38], UDDI [4] (both with
UML [11]), it has been attempted to specify at the technical level the modelling
of any Internet service.
    The definition of services through semantic languages, enables to work with
the modeling of services in a more flexible mode. The flexibility of these lan-
guages allows to capture functional and non-functional features. OWL-S [37]
integrates key factors for these services such as service discovery, process model-
ing, and service interoperability details. Web services can be described by using
WSMO [12] as well. With WSMO it is possible to describe semantically the dis-
covery, invocation and logic aspects of the service. Schemes such as SA-WSDL
[30], which integrate both semantic and syntactic schemas, associate semantic
annotations to WSDL elements.
    Some of them, including SA-REST [27], WSMO-lite [31] or Minimal Service
Model [44] (MSM), are also part of the group of services for the exchange, au-
tomation and composition of services more centred on the final service. On the
other hand USDL [29] is a global approach to service modeling that seeks to em-
phasize business, entity, price, legal, composition, and technical considerations.
With Linked-USDL [40], this idea becomes concrete, in services integrated into
the Cloud Computing model. Linked-USDL assumes an important part of the
key aspects that a service must provide, extending its usefulness to the ability
to create cloud services from scratch. With Linked-USDL, the modelling of
entities, catalogue, SLA or interaction among them are considered.
    The proposals address the modelling and definition of services in a generic
mode, without dealing with the specific details of a DM/ML services in Cloud
Computing. For the treatment of these problems we typically use software
platforms such as Weka [21], Knime [6] or Orange [10] and frameworks and
libraries integrated into the most modern and efficient programming languages
such as C, Java, Python, R, Scala and others. These environments lack elements
for Cloud Computing services modelling due to its nature. The basic idea with
this type of modelling is that it allows the DM/ML service on Cloud Computing
to be defined. This makes it necessary that must contains semantic aspects for
the complete definition.
    There are several approaches to ontology-based definition languages for DM/ML
experimentation and work-flow. For example, Exposé [47] allows you to focus
your work on the experimentation work-flow [36]. With OntoDM [39] and using
BFO (Basic Formal Ontology), it is aimed to create a framework for data mining
experimentation and replication. In MEXcore [15] and MEXalgo [14] a complete
specification of the process of description of experimentation in DM problems is
made. In addition, together with MEXperf [15], which adds performance mea-
sures to the experimentation, the definition of this type of scheme is completed.
Another approach similar to MEX is ML-Schema [13], which attempts to estab-
lish a standard scheme for algorithms, data and experimentation. ML-Schema
consider terms like Task, Algorithm, Implementation, HyperParameter, Data,
Features or Model, for instance, as can be found in [1].
    The most recent proposals try to unify different schemes and vocabularies
previously developed following the Linked Data [5] guidelines. With Linked
Data, you can reuse vocabularies, schemes and concepts. This significantly

enriches the definition of schemas, allowing you to create the model definition
based on other existing schemas and vocabularies. Linked-USDL, MEX[core,algo,perf],
ML-Schema, OntoDM or Exposé among others, can be considered when creating
a work-flow for DM/ML service definition using Linked Data. These proposals
provide the definition of consistency in the main area of the service of DM to-
gether with the Linked Data properties, enabling the inclusion of other externals
schemas that complement the key aspects of a Cloud Computing service fully

3     dmcc-schema: Data Mining services with Linked
The Semantic Web applied to the definition of Cloud Computing services, allow
tasks such as discovery, negotiation, composition and invocation with a high
degree of automation. This automation, based on Linked Data is fundamental
in Cloud Computing because it allows services to be discovered and explored
for consumption by programmatic entities using the full potential of RDF and
SparQL. Linked Data [23] offers a growing body of reusable schemes and vo-
cabularies for the definition of Cloud Computing services of any kind. On this
basis, the LOV (Linked Data Open Vocabularies) [46] web platform is a good
example and its objective is to index schemas and vocabularies within Linked
Data, monitored by a group of experts.
    In this article we propose dmcc-schema, a scheme which has been designed to
address the problem of specifying and defining DM/ML services in Cloud Com-
puting. Not only it focus on solving the specific problem of modelling, with
the definition of experimentation and algorithms, but it also includes the main
aspects of a Cloud Computing service well defined. dmcc-schema can be con-
sidered as a Linked Data proposal for DM/ML services. Existing Linked Data
vocabularies have been integrated into dmcc-schema and new specific vocabular-
ies have been created ad-hoc to cover certain aspects that are not implemented
by other external schemes. Vocabularies have been re-used following Linked
Data recommendations, filling important parts such as the definition of exper-
iments and algorithms, as well as the interaction and authentication that were
already defined in other vocabularies. A service offered from a Cloud Computing
provider, must have the following aspects for its complete specification:

    • Interaction. Access points and interfaces for services consumption for
      users and entities.

    • Authentication. The service or services require authenticated access.
    • SLA/SLO. The services have service level agreements and more issues.
    • Prices. The services offered have a cost.

    • Entities. Services interact between entities offering or consuming services.
    • Catalogue. The provider has a catalogue of discoverable and consumable

Table 1: Cloud Computing Data Mining services analysed.
      Provider            Service name
      Google                   Cloud Machine Learning Engine
      Amazon                   Amazon SageMaker, Amazon Machine Learning
      IBM                      Watson Machine Learning, Data Science
      Microsoft Azure          Machine Learning Studio
      Algorithmia              Algorithms bundles

                                 Table 2: Vocabularies.
         Module                  Vocabularies
         SLA                     gr, schema, ccsla
         Pricing                 gr, schema, ccpricing, ccinstance, ccregion
         Authenticacion          waa
         Interaction             schema
         Data Mining             mls, ccdm
         Service Provider        gr, schema

    There is no standardization about what elements a service in Cloud Comput-
ing should have for its complete definition. According to NIST 6 , it must meet
aspects such as self-service (discovery), measurement (prices, SLA), among oth-
ers. For the development of dmcc-schema, a comprehensive study of the features
and services available on the DM/ML platforms on Cloud Computing has been
carried out. Table 1 contains the providers and the name of the DM/ML ser-
vice analysed; leading providers such as Google, Amazon, Microsoft Azure, IBM
and Algorithmia, for whom, the SLA and SLO, pricing for the different vari-
ants and conditions, the service catalogue (DM/ML algorithms), the methods
of interaction with the service, and authentication have been studied.
    Our scheme and vocabulary have been complemented adding external vocab-
ularies: the interaction with the service, using [19], authentication,
with Web Api Authentication (waa) [35], price design, with GoodRelations [24]
and experimentation and algorithms with DM/ML , using the dmcc-schema vo-
cabulary (mls). Table 2 shows the vocabularies reused in the mls schema to
define each module of the full service.
    The high level diagram of dmcc-schema entities can be seen in the Figure 1.
The detailed definition of each entity is developed in the following subsections.
    With dmcc-schema we have tried to emphasize the independence of the cloud
platform, that is, dmcc-schema does not define aspects of service deployment
or elements closer to implementation tools. dmcc-schema definition is at the
highest level and only addresses the definition and modelling aspects of the
service without going into the details of infrastructure deployment.

3.1     SLA: Software Level Agreement
The trading of Cloud Computing services sets up a series of contractual agree-
ments between the stakeholders involved in the services. Both the provider
  6 National   Institute of Standards and Technology,

ML Service                               ML Services
                                                    Provider                                 Catalog


                   Service Pricing      includes   ML Service     hasInteractionPoint       Interaction

                               hasAuthentication   hasFunction

                                                   ML Function                                 SLA

         Figure 1: Main classes, and relations for dmcc-schema(dmcc).

              Table 3: MUP term and compensations related.
         Monthly Uptime Percentage (MUP) Compensation
         [0.00%, 99.00%]                                                                25%
         [99.00%, 99.99%]                                                               10%

and the consumer of the service must agree on service terms. The service level
agreements define technical criteria, relating to availability, response time or
error recovery, among others, that the provider must fulfil to deliver the service
to the consumer. The SLO are specific measurable features of the SLA such
as availability, throughput, frequency, response time, or quality. In addition
with the SLO, it needs to contemplate actions when such agreements cannot be
achieved and a compensation is offered.
     The SLA studied for the DM/ML service environment are established by
terms and definitions of the agreement ([2], [32], [48] ). These terms may have
certain conditions associated with them which, in the case of violation, involve
a compensation for the guarantee service.
     In general terms, SLA for Cloud Computing services is given by a term of
the agreement that contains one or more definitions similar to Monthly Uptime
Percentage (MUP), which specifies the maximum available minutes less down-
time divided by maximum available minutes in a billing month. For this term,
a metric or interval is established over which a compensation is applied in the
case that the agreement term is not satisfied. In this context a scheme named
ccsla 7 has been created for the definition of all the components of an SLA. In
Table 3 an example with a pair of MUP intervals and compensation related is
     In Figure 2 the complete scheme of the SLA modelling that has been designed
is illustrated.

3.2    Pricing
Like any other utility-oriented service, Cloud Computing services are affected
by costs and pricing that vary with the price of the service offered. Following
the pay-as-you-go model, the costs of using the service are directly related to the
characteristics of the service and the use made of it. The pricing of services in
  7 Available   in

ccsla:Claim           ccsla:ServiceCredit       schema:structuredValue


                     ccsla:Term                                     ccsla:hasDefinitionValue
                                ccsla:hasCompensation                      ccsla:Definition

                        SLA                                          ccsla:Condition

                  ccsla:hasValidity                              ccsla:includesValue

                    time:interval                   gr:QuantitativeValue       gr:QualitativeValue

         Figure 2: SLA schema for Cloud Computing services (ccsla).

  Table 4: Price components for each Cloud Computing provider analysed.
        Provider / Service         Region Instances Storage
        Amazon SageMaker                             y                 y                      y
        Amazon Lambda                                y                 n                      n
        Azure Machine Learning                       y                 y                      y
        Azure Functions                              y                 n                      n
        IBM BlueMix                                  y                 y                      n
        Google Machine Learning                      y                 y                      n
        Algorithmia                                  n                 n                      n

Cloud Computing is a complex task. Not only there is a price plan for temporary
use of resources, but it is also affected by technical aspects, configuration or
location of the service. Table 4 collects some of the price modifying aspects of
the use of the DM/ML service. In the specific segment of data mining services
in Cloud Computing there are several elements to consider.
   The following components of the vocabulary stand out from the diagram of
the Figure 3:

   • ccpricing:PricingPlan Allows you to define the attributes of the price
     plan and their details. For example free, premium plan, or similar. It
     must match the attributes of MinPrice and MaxPrice.

   • gr:PriceSpecification It provides all the tools to define the prices of
     the services at the highest level of detail. Comes from GoodRelations (gr).
   • ccpricing:Compound Components of the price of the service. Services are
     charged according to values that are related to certain attributes of the
     service configuration. In compound you can add aspects such as type of
     instances, cost of the region, among others.

ccpricing:PricingPlan           dmcc:hasPricing             Service Pricing


                             ccpricing:MaxPrice ccpricing:hasComponentPrice
                    gr:PriceSpecification                                     ccpricing:Compound



                        Figure 3: Pricing schema with ccpricing.

   • ccinstances:Instance Provides the specific vocabulary for the definition
     of price attributes related to the instance or instances where the service is
   • ccregions:Region Allows you to use the regions management scheme
     offered by providers, so that this is an additional value to the costs of the
     service, as part of the price.

    For the modelling of this part, three vocabularies supplementing the defini-
tion of prices, have been developed which were not available. On the one side
ccinstances 8 , which contains everything necessary for the definition of instances
(CPU, number of CPU cores, model, RAM or HD, among others), on the other
side ccregions 9 , which provides the general modelling of the services regions and
location in Cloud Computing and the specific schema for the prices modelling
ccpricing 10 .

3.3    Authentication
Nowadays, security in computer systems and in particular in Cloud Computing
platforms is an aspect that must be taken very seriously when developing Cloud
Computing services and applications. In this way, a service that is reliable
and robust must also be secure against potential unauthorised access. In the
outer layer of security in Cloud Computing services consumption, authentication
that should be considered as a fundamental part of a Cloud Computing service
    Authentication of Cloud services today covers a wide range of possibilities. It
should be noted that for the vast majority of services, the most commonly used
option for managing user access to services are API Key and OAuth and other
mechanisms. waa:WebApiAuthentication has been included for authentication
modelling. This scheme allows you to model many of the authentication systems
available, not only through the major providers, but also for more discrete
environments that do not have OAuth access. In Figure 4, the model for the


               waa:requiresAuthentication           waa:hasAuthenticationMechanism

               waa:ServiceAuthentication             waa:AuthenticationMechanism



                          Figure 4: Authentication schema.

definition of authentication in the service is depicted, together with the details
of the authentication mechanisms.

3.4    Interaction
The interaction with DM/ML services is generally done through a RESTful
API. This API provides the basic functionalities of interaction with the service
consumer, who must be previously authenticated to use the services identified
in this way. For the interaction the Action entity of the vocabulary schema was
used. With this definition, the service entry points, methods and interaction
variables are fully specified for all services specified by the API.

3.5    Data Mining service
For the main part of the service, where the experimentation and execution of al-
gorithms is specified and modelled, parts of ML-Schema (mls) have been chosen.
MEXcore, OntoDM, DMOP or Exposé also provide an adequate abstraction to
model the service, but they are more complex and their vocabulary is more
extensive. ML-Schema has been designed to simplify the modelling of DM/ML
experiments and bring them into line with what is offered by Cloud Computing
providers. We have extended ML-Schema by adapting its model to a specific
one and inheriting all its features (Figure 5).
   The following vocabulary components are highlighted (ccdm is the name of
the schema used):

   • ccdm:MLFunction Sets the operation, function or algorithm to be exe-
     cuted. For example Random Forest or KNN.
   • ccdm:MLServiceOutput The output of the algorithm or the execution.
     Here the output of the experiment is modelled as Model or Model Evalu-
     ation or a DataSet.
   • ccdm:MLServiceInput The algorithm input, which corresponds to the set-
     ting of the algorithm implementation. Here you model the data entry of
     the experiment, such as the data set and values specific (ccdm:MLServiceInputParameters)
     to the algorithm being executed.

mls:Algorithm          mls:realizes        MLFunction                                                mls:Model

                mls:implements        mls:executes                                                     ccdm:MLServiceOutput        mls:ModelEvaluation


                        mls:Task                               mls:achieves                               mls:Data                  mls:Feature

           Figure 5: DM experimentation and algorithms execution.

    • mls:Model Contains information specific to the model that has been gen-
      erated from the run.

    • mls:ModelEvaluation Provides the performance measurements of the
      model’s evolution.
    • mls:Data They contain the information of complete tables or only at-
      tributes (table column), only instances (row), or only a single value.

    • mls:Task It is a part of the experiment that needs to be performed in the
      DM/ML process.

    The complete diagram of the dmcc-schema is available on the project web-
site11 , from which you can explore the relationships, entities, classes, and at-
tributes of each of the modules that comprise the proposal.

4     Use cases and test platform
For reasons of space we will not detail all the data or attributes in depth and will
only consider what is most important for the basic specification of the service
and his comprehension. All detailed information on each component, examples
and datasets are available on the project’s complementary information website.
    In this section we will illustrate how dmcc-schema can be used effectively
to define a specific DM service, as well as additional aspects related to Cloud
Computing such as SLA, interaction, pricing or authentication. To do this we
will create a service instance for a Random Forest [25] algorithm for classification
and K-means [22] for clustering. In Random Forest service implementation,
we will use the default function model and parametrization of this algorithm
in the R [45] language. We have chosen the parametrization format of the R
algorithms, due to the great growth that this programming language is currently
having for data science and DM/ML. For the instantiation of the service we will
use vocabularies such dcterms (dc:) [?], Good Relations (gr:) or
(s:). In these cases of use we will name dmcc-schema as dmcc namespace.
    The entry point for defining the service is the instantiation of the dmcc:ServiceProvider
entity, including its data, using the class dmcc:MLServiceProvider. We will
specify an example of DM service hosted on our Cloud Computing platform

1   _ : MLProvider a dmcc : MLServiceProvider ;
 2     rdfs : label " ML Provider " @en ;
 3     dc : description
 4         " DICITS ML SP " @en ;
 5     gr : name " DITICS ML Provider ";
 6     gr : legalName " U . of Granada ";
 7     gr : hasNAICS "541519";
 8     s : url < http :// www . dicits . ugr . es >;
 9     s : serviceLocation
10       [ a s : PostalAddress ;
11           s : addressCountry " ES ";
12           s : addressLocality " Granada ";
13       ] ;
14       s : contactPoint
15       [
16         a s : ContactPoint ;
17         s : contactType " Costumer Service ";
18         s : availableLanguage [
19             a s : Language ;
20             s : name " English ";];
21         s : email " ml@dicits . ugr . es ";
22       ];
23     dmcc : hasMLService
24                    _ : MLServiceDicitsRF ,
25                    _ : MLSe rvic eDic itsKM eans ;
26     dmcc : hasOfferCatalog
27                    _ : ML Se rv ic eDi ci ts Cat al og ;
28   .
                       Listing 1: New DM service definition.

    The code above shows the specific details of the service provider, such as legal
aspects, location or contact information. The service provider dmcc:MLServiceProvider
has the property dmcc:hasMLService. With this property two services such as
 :MLServiceDicitsRF and :MLServiceDicitsKMeans (listing 1 line 24-25) are
    Listing 2 shows the instantiation of each of the entities for :MLServiceDicitsRF.
For the service definition of the :MLServiceDicitsKMeans entities it is done in
the similar manner, but using the corresponding properties that differ from the
 :MLServiceDicitsRF, that is, different point of interaction, different pricing
plan, among others, whenever applicable.
      :MLServiceDicitsRF allows defining all the service entities such as SLA
(listing 2 line 8), algorithm (listing 2 line 10), prices (listing 2 line 15), interaction
points (listing 2 line 6) and authentication (listing 2 line 12).
 1   _ : MLServiceDicitsRF a dmcc : MLService ;
 2     rdfs : label
 3          " ML Service dicits . ugr . es " @en ;
 4     dc : description
 5          " DICITS ML Service " @en ;
 6     dmcc : hasInteractionPoint
 7          _ : MLServiceInteraction ;
 8     dmcc : hasServiceCommitment
 9          _ : MLServiceSLA ;

10       dmcc : hasFunction
11           _ : MLServiceFunction ;
12       dmcc : hasAuthentication
13           _ : MLServiceAuth ;
14       dmcc : hasPricingPlan
15         _ : MLServicePricing ;
16   .
                     Listing 2: Components of the DM service

    The interaction with the service (3) is performed using the dmcc:Interaction
class that includes the property dmcc:hasEntryPoint that allows to define an
Action on a resource or object. In this case we use the vocabulary
to set the method of access to the Random Forest service, for which we specify
that the service will be consumed through a RESTful API, with HTTP POST ac-
cess method, the URL template, for instance
(listing 3, line 13), and its parameters (listing 3, from line 14).
 1   _ : MLServi ceInteraction
 2             a dmcc : Interaction ;
 3     rdfs : label " Service Interaction " @en ;
 4     dc : description
 5              " Service EntryPoint " @en ;
 6     dmcc : hasEntryPoint [ a s : Action ;
 7     s : target [
 8       a s : EntryPoint ;
 9       s : httpMethod " POST ";
10       s : contentType
11         "x - www - form - urlencoded ";
12       s : urlTemplate
13         " http :// dicits . ugr . es / ml / rf /;";
14       s : formula - input [
15         a s: PropertyValueSpecification ;
16         s : valueRequired true ;
17         s : valueName " formula "; ];
18       s : dataset - input [
19         a s: PropertyValueSpecification ;
20         s : valueRequired true ;
21         s : valueName " data ";];
22       ...
                            Listing 3: Service interaction.

    The selected service requires authentication and for modelling, an API KEY
authentication has been chosen. The instantiation of service authentication
is defined requesting authentication with waa:requiresAuthentication and
setting up the authentication mechanism waa:hasAuthenticationMechanism
to waa:Direct and as credentials an API KEY is also used as shown in listing
 1   _ : MLServiceAuth
 2           a dmcc : Serv iceA uthe ntica tion ;
 3     rdfs : label
 4        " Service Authentication " @en ;
 5     dc : description " Service Auth " @en ;
 6     waa : re qui re sA ut hen ti ca tio n waa : All ;

7       waa : h a s A u t h e n t i c a t i o n M e c h a n i s m
 8          [ a waa : Direct ;
 9             waa : hasInputCredentials
10           [ a waa : APIkey ;
11             waa : isGroundedIn " key ";
12           ];
13         waa : w ay Of S en d in gI n fo r ma ti o n
14               waa : ViaURI ;
15       ]
16   .
                                   Listing 4: Service authentication.

    For the definition of the SLA, in our example service we have taken the
some providers as Amazon or Azure. The following terms and agreements have
been established, a license term called Month Uptime Percentage (MUP) that
corresponds to the subtract 100 from the percentage of minutes in the month in
which any of the services were unavailable. For the cases where this occurs SLA
define two ranges: less than 99.00 is compensated by 30 credits (in service us-
age) and the range of 99.00 to 99.99 is compensated with 10 credits. To model
the SLA we use ccsla:SLA together with the property ccsla:cointainsTerm
 :SLATermMUP A; in which we define the specific term in listing 5.
 1   _ : SLATermMUP_A a ccsla : Term ;
 2     rdfs : label " SLA MUP " @en ;
 3     dc : description
 4        " SLA Term : Monthly Uptime %" @en ;
 5     dc : comment " Calculated by
 6        subtracting from 100% the
 7        percentage of minutes
 8        during ..." @en ;
 9     ccsla : hasCompensation
10        _ : SLACompensationMUP_A ,
11        _ : SLACompensationMUP_B ;
12     ccsla : includeDefs
13        _ : SLADefinition_A ,
14        _ : SLADefinition_B ;
15   .
                              Listing 5: SLA terms and compensation.

   The definition of the terms and ranges :SLADefinition A, :SLADefinition B,
and the associated compensation :SLACompensationMUP A, :SLACompensationMUP B
are indicated in line 10 and 11 in listing 5; To define the range of the term is
used in :SLADefinition A, as shown in listing 6.
 1   _ : SLADefinition_A a ccsla : Definition ;
 2     ccsla : hasDefinitionValue [
 3     a s : structuredValue ;
 4     s : value [
 5       a s : QuantitativeValue ;
 6       s : maxValue 99.99;
 7       s : minValue 99.00;
 8       s : unitText " Percentaje ";
 9       ];
10     ];

11     .
                                  Listing 6: SLA term definition.

    The scheme for SLA (ccsla) and other examples with the SLA for Amazon
and Microsoft Azure can be accessed from the website 12 .
    For the definition of the economic cost of the service we have considered two
variants for the example. The first is for free service use, limited to 250 hours
of execution of algorithms within an instance (Virtual Machine) with a CPU
model Intel i7, 64 GB of RAM and one region. The second pricing model where
you charge what you consume for the service in USD/h., for an instance and
one region.
    In listing 7 we define the two pricing plans (free :PricingPlanFree and ba-
sic :PricingPlanRegular, lines 7-8) and in listing 8 we specify the :ComponentsPricePlanFree
plan, where the prices components and modifiers are set. The price modelling
is done with our proposal for the definition of prices ccpricing.
 1     _ : MLServicePricing a
 2                 ccpricing : ServicePricing ;
 3       rdfs : label " Service Pricing " @en ;
 4       dc : description
 5           " Service Pricing " @en ;
 6       ccpricing : hasPricing
 7         _ : PricingPlanFree ,
 8         _ : PricingPlanRegular ;
 9     .
                    Listing 7: Service pricing (free plan and regular plan).

    For each price plan we take into account the variables and features that
affect the price. These are: region, instance type and other components for the
 :ComponentsPricePlanFree (listing 8).
 1     _ : C om p on e nt sP r ic e Pl an F re e
 2            a ccpricing : Compound ;
 3       ccpricing : hasRegion _ : RegionFree ;
 4       ccpricing : hasInstance _ : InstanceFree ;
 5       ccpricing : MaxCompound _ : MaxUsageFree ;
 6     .
                                  Listing 8: Pricing components.

    For example, to define features of the type of instance used in the free plan,
we use ccinstance:Instance; and a few attributes like RAM or CPU as seen
in listing 9. More examples for the schema ccinstances, including a small dataset
of Amazon instances 13 (types T1 and M5 ) are available in the web site of the
complementary information of the paper.
 1     _ : InstanceFree
 2           a ccinstances : Instance ;
 3          ccinstances : hasRAM [
 4            a ccinstances : ram ;
 5               s : value "64"
 6               s : unitCode " E34 ";
     12 ccsla:
     13 ccinstances:

7       ] ;
 8       ccinstances : hasCPU [
 9         a ccinstances : cpu ;
10           ccinstances : cpu_model
11             " Intel i7 ";
12       ] ;
13   .
                        Listing 9: Pricing and instances.

   For example, in order to define the :MaxUsageFree, we need to determine
the free access plan to the service and the limitation of compute hours to 250.
For this purpose we use gr:PriceSpecification and gr:Offering classes as
shown in listing 10.
 1   _ : MaxUsageFree a
 2        gr : PriceSpecification ,
 3        gr : Offering ;
 4     gr : max 0.00;
 5     gr : priceCurrency " USD ";
 6     gr : includesObject [
 7       a gr : TypeAndQualityNode ;
 8       gr : amountOfThisGood "250";
 9       gr : hasUnitOfMeasurement " HRS ";
10     ];
11   .
                Listing 10: Price specification for the free plan.

    For the regular price plan :PricingPlanRegular defined in listing 7 line
8, we use a similar price modelling, but including another type of instance and
the same region as shown in listing 11. In this case, the regular price plan offers
consumption of instance type :instance02 at a cost of $0.0464 per hour in the
 :RegionNVirginia region.
 1   _ : PricingPlanRegular a ccpricing : Compound ;
 2         rdfs : label " Price Components ";
 3         rdfs : comment
 4          " List of components and limits ";
 5         ccpricing : hasRegion
 6          _ : RegionNVirginia ;
 7         ccpricing : hasInstances
 8          _ : instance02 ;
 9         ccpricing : withMaxCompound [
10          a gr : Offering ;
11              gr : ha sPri ceSp ecifi cati on [
12                 a gr : PriceSpecification ;
13                    gr : hasCurrency
14                      " USD "ˆˆ xsd : string ;
15                    gr : hasCurrencyValue
16                      "0.0464 USD "ˆˆ xsd : float ;
17                   ];
18                 gr : includeObject [
19                    a gr : TypeAndQuantityNode ;
20                    gr : amountOfThisGood
21                        "1"ˆˆ xsd : integer ;

22                             gr : hasUnitOfMeasurement
23                                " HRS "ˆˆ xsd : string ;
24                             ];
25                        ];
26                    .
                                Listing 11: Region and instance pricing.

    Additional examples for the ccpricing schema, a dataset of Amazon Sage-
Maker 14 pricing plans, are available.
    Finally, we define the service algorithm using ccdm:MLFunction for the def-
inition of a Random Forest function, where we specify the input parameters
(dataset and hyper-parameters), the output of the algorithm, among others
(see listing 12).
 1     _ : Rand omFo rest_ Func tion
 2         a ccdm : MLFunction ;
 3          ccdm : hasInputParameters
 4             _ : RF_InputParameters ;
 5          mls : hasInput
 6           _ : RF_Input ;
 7          mls : hasOutput
 8           _ : RF_Output .
                      Listing 12: Operations for the algorithm/function.

    The input and output data of the algorithms must be included in the defi-
nition of the data mining operation to be performed. The input of data, which
can be parameters :RF InputParameters or datasets :RF Input.
    Input parameters of the algorithm dmc:MLServiceInputParameters and the
parameter list :parameter 01, [...] is shown in listing 13.
 1     _ : RF_InputParameters
 2         a ccdm : M L S e rv i c e In p u t Pa r a m et e r s ;
 3           ccdm : Parameters
 4                _ : parameter_01 ,
 5                [...]
 6           dc : description
 7                  " Input Parameters " ;
 8           dc : title " Input "
 9           .
                                Listing 13: Input parameters definition.

    Definition of ccdm:hasInputParameters :RF InputParameters allows you
to specify the general input parameters of the algorithm. For example for Ran-
dom Forest dc:title "ntrees" (number of trees generated), as well as whether
ccdm:mandatory "false" is mandatory and its default value, if it exists. List-
ing 5 shows the definition of one of the parameters parameter 01. The other
algorithm parameters are defined in the same way.
 1     _ : parameter_01
 2         a ccdm : M LS e rv i ce In p ut P ar a me te r ;
 3           ccdm : defaultvalue "100" ;
 4           ccdm : mandatory " false " ;
     14 ccpricing:

5           dc : description
 6              " Number of trees " ;
 7           dc : title " ntrees " .
                    Listing 14: Example of parameter and features.

    An mls:Model model and an evaluation of the mls:ModelEvaluation model
have been considered for specifying the results of the Random Forest service
execution in mls:hasOutput :RF Output. Model evaluation is the specific re-
sults if the algorithm returns a value or set of values. When the service al-
gorithm is preprocessing the result is a dataset. For the model you have to
define for example whether the results are PMML [18] for example :RF Model
a dmc:PMML Model as shown in listing 15.
 1     _ : KMeans_Model
 2         a ccdm : PMML_Model ;
 3           ccdm : storagebucket
 4              < dicits :// models / > ;
 5           dc : description
 6              " PMML model " ;
 7           dc : title " PMML Model " .
             Listing 15: PMML Model and storage of the service output.

    The second service offered by our provider, a K-means algorithm (listing 1,
line 25), requires a definition :MLServiceDicitsKMeans, following the same
pattern as for Random Forest, but in this case you have to generate a set of
different input parameter to perform the K-means service. Listing 16 contains
an example of two input parameters for the algorithm.
 1     _ : parameter_01
 2             a ccdm : M LS e rv i ce In p ut P ar am e te r ;
 3                 ccdm : defaultvalue "" ;
 4           ccdm : mandatory " true " ;
 5           dc : description
 6             " numeric matrix of data " ;
 7           dc : title " x "
 8                 .
 9     _ : parameter_02
10             a ccdm : M LS e rv i ce In p ut P ar am e te r ;
11                 dmc : defaultvalue "3" ;
12           ccdm : mandatory " true " ;
13           dc : description
14           " either the number of clusters ." ;
15           dc : title " centres "
16                 .
17     ...
            Listing 16: Example of a parameter and features for K-Means.

    Other services implemented on dmcc-schema as examples, such as Naive-
Bayes, LinearRegression, SVM or Optics, are available in the supplementary
website of the paper 15 . In addition, to validate the service created, several
queries have been made in SparQL, in which, price information, SLA and inter-
action on different service configurations has been extracted.

5    Conclusion
In this paper, we have presented the dmcc-schema, a scheme and vocabulary
to model the definition of DM and ML services in Cloud Computing. It has
been built on the Semantic Web using an ontology language and following the
Linked Dataproposal. In this proposal, we have encouraged the reuse of external
vocabularies and schemes, which enrich our scheme and avoid the redefinition
of concepts on Cloud Computing.
    The use of dmcc-schema for service definition enables the design of inter-
operable and lightweight DM services. In addition, it not only focuses on the
definition of experiments or algorithms, but also considers fundamental elements
for consumption and discovery of services in Cloud Computing platforms. As
mentioned above, it covers all aspects necessary for the portability of the defi-
nition of services, such as pricing or SLA among others.
    A key element of dmcc-schema is that it allows you to model a Cloud Com-
puting DM provider service, supporting the specific properties and characteris-
tics of the service offered. Our scheme has been designed taking into account the
different types of concept definition that are present in the the most well-known
Cloud Computing providers.
    In the development of the use cases, where a pair of DM/ML services have
been defined. The usefulness of dmcc-schema is confirmed, allowing all key
aspects of these specific services to be defined and modelled. Definition of
services in dmcc-schema consider key aspects of the service in a simple way and
with the appropriate level of detail.
    Service modelling with dmcc-schema can be used for cloud brokerage ser-
vices. To use cloud broker for DM/ML, the design of dmcc-schema allows you
to group concepts and terms with different definitions across the different Cloud
Computing providers and has the possibility of integrating them into a single
scheme. This allows you to abstract each provider’s unique service and work
with a service definition that models the differences between them. It thus
enables comparison of services of DM/ML in Cloud Computing.
    As future work, dmcc-schema will be further enhanced to integrate more
current Cloud Computing services such as serverless DM/ML related functions
and algorithm bundles such us text mining or image classification. It will also be
integrated into an DM/ML cloud platform under development, offering DM/ML
services using the dmcc-schema service definitions. This platform will allow you
to work with the DM/ML service definitions and build the full service from

M. Parra-Royon holds a ”Excelencia” scholarship from the Regional Government
of Andalucia (Spain). This work was supported by the Research Projects P12-
TIC-2958 and TIN2016-81113-R (Ministry of Economy, Industry and Compet-
itiveness - Government of Spain).

 [1] Mlschema: machine learning and data mining ontologies.   http:
     Accessed: 2018-05-20.
 [2] Bhagyashree Ambulkar and Vaishali Borkar. Data mining in cloud com-
     puting. In MPGI National Multi Conference, volume 2012, 2012.
 [3] David Beckett. Turtle-terse rdf triple language. http://www. ilrt. bris. ac.
     uk/discovery/2004/01/turtle/, 2008.
 [4] Tom Bellwood, Luc Clément, David Ehnebuske, Andrew Hately, Maryann
     Hondo, Yin Leng Husband, Karsten Januszewski, Sam Lee, Barbara Mc-
     Kee, Joel Munter, et al. Uddi version 3.0. Published specification, Oasis,
     5:16–18, 2002.
 [5] Tim Berners-Lee, James Hendler, Ora Lassila, et al. The semantic web.
     Scientific american, 284(5):28–37, 2001.

 [6] Michael R. Berthold, Nicolas Cebron, Fabian Dill, Thomas R. Gabriel, To-
     bias Kötter, Thorsten Meinl, Peter Ohl, Christoph Sieb, Kilian Thiel, and
     Bernd Wiswedel. KNIME: The Konstanz Information Miner. In Studies in
     Classification, Data Analysis, and Knowledge Organization (GfKL 2007).
     Springer, 2007.

 [7] Christian Bizer, Tom Heath, and Tim Berners-Lee. Linked data-the story
     so far. International journal on semantic web and information systems,
     5(3):1–22, 2009.
 [8] Tim Bray, Jean Paoli, C Michael Sperberg-McQueen, Eve Maler, and
     François Yergeau. Extensible markup language (xml). World Wide Web
     Journal, 2(4):27–66, 1997.
 [9] Erik Christensen, Francisco Curbera, Greg Meredith, and Sanjiva Weer-
     awarana. Web Service Definition Language (WSDL). Technical report,
     World Wide Web Consortium, March 2001.
[10] Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar,
     Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže
     Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka
     Žitnik, and Blaž Zupan. Orange: Data mining toolbox in python. Journal
     of Machine Learning Research, 14:2349–2353, 2013.
[11] Alan Dennis, Barbara Haley Wixom, and David Tegarden. Systems analysis
     and design: An object-oriented approach with UML. John wiley & sons,
[12] John Domingue, Dumitru Roman, and Michael Stollberg. Web service
     modeling ontology (wsmo)-an ontology for semantic web services, 2005.

[13] Diego Esteves, Agnieszka Lawrynowicz, Panče Panov, Larisa Soldatova,
     Tommaso Soru, and Joaquin Vanschoren. ML Schema Core Specification.
     Technical report, World Wide Web Consortium, October 2016.

[14] Diego Esteves, Diego Moussallem, Ciro Baron Neto, Jens Lehmann, Maria
     Cláudia Reis Cavalcanti, and Julio Cesar Duarte. Interoperable machine
     learning metadata using mex. In International Semantic Web Conference
     (Posters & Demos), 2015.
[15] Diego Esteves, Diego Moussallem, Ciro Baron Neto, Tommaso Soru, Ri-
     cardo Usbeck, Markus Ackermann, and Jens Lehmann. Mex vocabulary: a
     lightweight interchange format for machine learning experiments. In Pro-
     ceedings of the 11th International Conference on Semantic Systems, pages
     169–176. ACM, 2015.
[16] Salvador Garcı́a, Sergio Ramı́rez-Gallego, Julián Luengo, José Manuel
     Benı́tez, and Francisco Herrera. Big data preprocessing: methods and
     prospects. Big Data Analytics, 1(1):9, 2016.
[17] Souad Ghazouani and Yahya Slimani. A survey on cloud service description.
     Journal of Network and Computer Applications, 91:61–74, 2017.
[18] Alex Guazzelli, Michael Zeller, Wen-Ching Lin, Graham Williams, et al.
     Pmml: An open standard for sharing models. The R Journal, 1(1):60–65,
[19] R. V. Guha, Dan Brickley, and Steve MacBeth. Evolution of
     structured data on the web. Queue, 13(9):10:10–10:37, November 2015.
[20] Marc J Hadley. Web application description language (wadl). 23, 2006.
[21] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter
     Reutemann, and Ian H. Witten. The weka data mining software: An
     update. SIGKDD Explor. Newsl., 11(1):10–18, November 2009.
[22] John A Hartigan and Manchek A Wong. Algorithm as 136: A k-means
     clustering algorithm. Journal of the Royal Statistical Society. Series C
     (Applied Statistics), 28(1):100–108, 1979.
[23] Tom Heath and Christian Bizer. Linked data: Evolving the web into a
     global data space. Synthesis lectures on the semantic web: theory and
     technology, 1(1):1–136, 2011.
[24] Martin Hepp. Goodrelations: An ontology for describing products and
     services offers on the web. Knowledge Engineering: Practice and Patterns,
     pages 329–346, 2008.
[25] Tin Kam Ho. The random subspace method for constructing decision
     forests. IEEE transactions on pattern analysis and machine intelligence,
     20(8):832–844, 1998.
[26] Gartner Inc. Gartner says 6.4 billion connected ’things’ will be in use in
     2016. Technical report, 2016.
[27] Matthias Klusch. Service discovery. In Encyclopedia of Social Network
     Analysis and Mining, pages 1707–1717. Springer, 2014.
[28] Graham Klyne and Jeremy J Carroll. Resource description framework (rdf):
     Concepts and abstract syntax. 2006.

[29] Srividya Kona, Ajay Bansal, Luke Simon, Ajay Mallya, Gopal Gupta, and
     Thomas D Hite. Usdl: A service-semantics description language for auto-
     matic service discovery and composition1. International Journal of Web
     Services Research, 6(1):20, 2009.
[30] Jacek Kopeckỳ, Tomas Vitvar, Carine Bournez, and Joel Farrell. Sawsdl:
     Semantic annotations for wsdl and xml schema. IEEE Internet Computing,
     11(6), 2007.

[31] Jacek Kopeckỳ, Tomas Vitvar, Dieter Fensel, and Karthik Gomadam.
     hrests & microwsmo. STI International, Tech. Rep., 2009.
[32] Xiaoyong Li and Junping Du. Adaptive and attribute-based trust model
     for service-level agreement guarantee in cloud computing. IET Information
     Security, 7(1):39–50, 2013.

[33] Dan Lin, Anna C Squicciarini, Venkata N Dondapati, and Smitha Sun-
     dareswaran. A cloud brokerage architecture for efficient cloud service se-
     lection. IEEE Transactions on Services Computing, 2016.
[34] Ling Liu. Services computing: from cloud services, mobile services to inter-
     net of services. IEEE Transactions on Services Computing, 9(5):661–663,
[35] Maria Maleshkova, Carlos Pedrinaci, John Domingue, Guillermo Alvaro,
     and Ivan Martinez. Using semantics for automating the authentication of
     web apis. The Semantic Web–ISWC 2010, pages 534–549, 2010.

[36] Fabrizio Marozzo, Domenico Talia, and Paolo Trunfio. A workflow man-
     agement system for scalable data mining on clouds. IEEE Transactions on
     Services Computing, 2016.
[37] David Martin, Mark Burstein, Jerry Hobbs, Ora Lassila, Drew McDermott,
     Sheila McIlraith, Srini Narayanan, Massimo Paolucci, Bijan Parsia, Terry
     Payne, et al. Owl-s: Semantic markup for web services. W3C member
     submission, 22:2007–04, 2004.
[38] Eric Newcomer and Greg Lomow. Understanding SOA with Web services.
     Addison-Wesley, 2005.
[39] Pance Panov, Sašo Džeroski, and Larisa Soldatova. Ontodm: An ontol-
     ogy of data mining. In Data Mining Workshops, 2008. ICDMW’08. IEEE
     International Conference on, pages 752–760. IEEE, 2008.
[40] Carlos Pedrinaci, Jorge Cardoso, and Torsten Leidig. Linked usdl: a vocab-
     ulary for web-scale service trading. In European Semantic Web Conference,
     pages 68–82. Springer, 2014.

[41] Eric Prud, Andy Seaborne, et al. Sparql query language for rdf. 2006.
[42] Sergio Ramı́rez-Gallego, Alberto Fernández, Salvador Garcı́a, Min Chen,
     and Francisco Herrera. Big data: Tutorial and guidelines on information
     and process fusion for analytics algorithms with mapreduce. Information
     Fusion, 42:51–61, 2018.

[43] Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. Edge com-
     puting: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–
     646, 2016.

[44] Mohsen Taheriyan, Craig A Knoblock, Pedro Szekely, and José Luis Am-
     bite. Rapidly integrating services into the linked data cloud. In Interna-
     tional Semantic Web Conference, pages 559–574. Springer, 2012.
[45] R Core Team et al. R: A language and environment for statistical comput-
     ing. 2013.
[46] Pierre-Yves Vandenbussche, Ghislain A Atemezing, Marı́a Poveda-Villalón,
     and Bernard Vatant. Linked open vocabularies (lov): a gateway to reusable
     semantic vocabularies on the web. Semantic Web, 8(3):437–452, 2017.
[47] Joaquin Vanschoren and Larisa Soldatova. Exposé: An ontology for data
     mining experiments. In International workshop on third generation data
     mining: Towards service-oriented knowledge discovery (SoKD-2010), pages
     31–46, 2010.
[48] Zibin Zheng, Jieming Zhu, and Michael R Lyu. Service-generated big data
     and big data-as-a-service: an overview. In Big Data (BigData Congress),
     2013 IEEE International Congress on, pages 403–410. IEEE, 2013.

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