The Ontologically based Model for the Integration of the IoT and

Cloud ERP Services

Darko Andročec, Ruben Picek and Marko Mijač

Faculty of Organization and Informatics, University of Zagreb, Pavlinska 2, Varaždin, Croatia

Keywords: ERP, Cloud ERP, Internet of Things, Iot, Integration, Interoperability, Semantic Web.

Abstract: On-premise and Cloud ERP systems have become a backbone of almost all businesses. Another recent trend

currently in focus of both industry and academy is Internet of Things. The integration of Cloud ERP and the

Internet of Things (IoT) should be looked as a new shift in business effectiveness and will have a great

momentum in future. In this work, we propose the ontologically based model for the integration of the IoT

and Cloud ERP systems by using Semantic Web services. To semantically annotate things as services, we

plan to use recently published W3C’s SSN and SOSA ontologies. Furthermore, we plan to extend mentioned

ontologies to include classification and descriptions of Cloud ERP APIs. Our integration model proposes

usage of Semantic web services and AI planning technique to semi-automatically compose IoT and Cloud

ERP services.

1 INTRODUCTION

ERP systems have, from their very start, aimed at

being the IT backbone for business processes in

enterprises. To achieve that, they had to employ state

of the art information technology, and keep an eye on

the future trends and developments in industry. One

of the best examples of that is how ERP solutions

were heavily influenced by recent advances in cloud

computing technology. These advances resulted in

emergence of Cloud ERP systems, which implied

reshaping of technological, business and other aspects

of ERP systems. The software-as-a-service (SaaS)

model introduced changes from technological point

of view, making the ERP systems more flexible,

scalable and available from anywhere. It also changed

our view and use of ERP systems, previously as a

product, and nowadays as a service. This allowed the

shift to subscription business model, which

eliminated the need for up-front capital investments,

making the ERP solutions more accessible to

small and medium enterprises.

Another recent trend that is currently being in

focus of both industry and academy is Internet of

Things (IoT). The term originated in 1999 from

proposal of uniquely identifiable interoperable

connected objects with radio-frequency (RFID)

technology (Ashton, 2010). Of course, over time, the

concept included more and more evolving

technologies. Today, when we speak about IoT, we

speak about billions of “things” connected to a vast

network, which collect data by sensing their physical

environment, share this data with interested parties,

and intervene into concrete situations. Possibilities of

Internet of Things are so vast and diverse, that it is

hard to foresee all possible applications of the

technology. However, some notable examples

include smart homes, smart cities, transportation,

healthcare, agriculture, enterprises etc.

While it is still relatively novel concept for most

enterprises, IoT has a potential to again reshape ERP

systems, by making Cloud ERP more flexible and

intelligent. According to research by IDC (Rian van

Heur, 2015), 40% of data by 2020 will be machine-

generated, with 20 to 50 billion of connected devices

fuelling that growth. This will make Cloud ERP

systems more complex, but it will also enable a

unique point for adding value business.

The core characteristics of IoT and Cloud ERP

complement each other. On one hand IoT provides

interfaces to physical environment in which the

enterprise operates, thus being able to collect vast

amount of data. On the other hand Cloud ERP ensures

vast resources to storage, analyse and process this

data. IoT can provide Cloud ERP with real-time data

about the state of the performed business processes

and involved resources (people, equipment, tools,

Andro

cec, D., Picek, R. and Mija

c, M.

The Ontologically based Model for the Integration of the IoT and Cloud ERP Services.

DOI: 10.5220/0006763104810488

In Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018), pages 481-488

ISBN: 978-989-758-295-0

481

materials and products) in the real world enterprise

setting. Cloud ERP can use this data to help people

respond in a timely manner to possible malfunctions,

inefficiencies, safety and security risks, and other

issues at the operational level. Cloud ERP can also

use this data to support management activities, by

providing advanced analysis, statistics, visualization,

past trends, and predictions. These applications of IoT

technology in enterprises can be categorized as

follows (Lee and Lee, 2015): (1) monitoring and

control, (2) Big Data and business analytics, and (3)

information sharing and collaboration.

In order to achieve synergy between Cloud ERP

and IoT, there has to be a way of integrating these two

technologies. In this paper, we propose the model of

Cloud ERP and IoT integration, based on semantic

web services and AI planning technique for

composition. The rest of the paper proceeds as

follows. In section 2 the related work about

integration of Cloud ERP and IoT technology is

listed. Section 3 contains description of methodology

for model development. Description of the model

itself can be found in section 4. In the last section we

discuss the proposed model, and provide our

conclusions.

2 RELATED WORK

Cloud, ERP systems and IoT technologies are each

separate fields with large body of existing research.

However, their integration has a great potential in

providing benefits for each technology. Pairing of

Cloud computing and ERP systems has already

proved itself as a great move, which reshaped the

whole ERP market. Today, all major ERP vendors,

both large and small, offer their solutions in a form of

Software-as-a-Service (SaaS), i.e. Cloud ERP

solutions, which have numerous advantages over

traditional on-premises solutions. These advantages

include (Johansson et al., 2015): lower upfront costs,

lower TCO, availability, flexibility, integration with

other services, etc.

Integration of IoT into Cloud ERP systems looks

similarly promising. One of the main reasons for that

is the complementarity of these technologies. Authors

(Botta et al., 2014) investigated integration of Cloud

computing and IoT, and consider following

characteristics as complementary:

- Storage Resources – IoT produces a large

amount of non-structured or semi-structured data.

Cloud, on the other hand offers almost unlimited

capacity for storing that data. In big data terminology

we can say that IoT represent big data source and

Cloud represents platform for managing big data.

- Computational Resources– IoT devices have

no or very limited computational capabilities. This is

why collected data is transferred to Cloud which has

has required resources to process this data.

- Communication resources – Cloud has built-in

real-time solutions for connecting, tracking,

monitoring and controlling practically anything from

anywhere.

Above stated complementary characteristics are

perfectly valid also for Cloud ERP and IoT. If

anything, ERP components augments this

complementarity with being one of the software

systems most dependent on large amount of business

data. This can be nicely seen in following definition

of IoT in enterprise context (Haller et al., 2009): “A

world where physical objects are seamlessly

integrated into the information network, and where

the physical objects can become active participants in

business processes. Services are available to interact

with these ‘smart objects’ over the Internet, query

their state and any information associated with them,

taking into account security and privacy issues”.

Cloud acts as intermediate layer between the “things”

and the ERP system, where it hides all the complexity

and the functionalities necessary to implement latter.

According to (Boza et al., 2015), interoperability

of Cloud ERP and IoT can be seen as interaction

between ERP system and other, internal or external

systems. Same authors proposed two perspectives of

interoperability, the first one considering

technological aspects such as web services, SOA,

Cloud computing, IoT etc., and the second one

considering business aspects such as BPM, BPR;

virtual enterprises, references models etc. In our

paper, we focus on technological perspective. The

difficulties of legacy systems to exchange

information with each other within the company have

been overcome by the implementation of ERP

systems (Boza et al., 2015).

While the need for integration between Cloud

ERP with IoT and other systems in general is

apparent, interoperability remains a significant issue.

For example, issues with compatibility and

integration with other existing systems is recognized

as one of the major barriers in adopting Cloud ERP

systems (Picek et al., 2017). Different approaches for

mitigating that problem have been proposed in

literature. Most of them are based on Semantic Web,

for example, SOCRADES (de Souza et al., 2008) is

a middleware for business integration, focused on

integrating web service enabled devices with ERP

systems and other enterprise applications.

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482

Architecture for effective integration of the Internet

of Things in enterprise services has been proposed by

(Spiess et al., 2009). Meyer et al. (2013) identify and

integrate IoT devices as a type of resources in

business processes. Song et al. (2010) propose

application layer solution as a semantic middleware

for interoperability between IoT devices.

Alexakos et al., (2016) present an approach to

integration between IoT and manufacturing processes

based on semantics. Zhuming Bi et al. (2014)

investigate the impact of IoT to modern

manufacturing in Enterprise Systems. Molano et al.

(2017) proposed a meta-model for integration of IoT,

Social networks, Cloud and Industry 4.0. The novelty

of our approach is usage of existing cloud ERP

application programming interfaces (APIs) and IoT

services that can be semantically annotated and semi-

automatically translated into AI planning method

returning plan how to compose the mentioned two

types of services. The main aim of our proposal is to

enable service-level interoperability among IoT

services and cloud ERP APIs.

3 METHOD

Semantic Web is often used in research papers and

research projects to tackle interoperability problems

among different systems, models, and frameworks,

e.g. integration of cloud computing services

(Androcec and Vrcek, 2016a) or integration of IoT

services (Androcec and Vrcek, 2016b). For this

reason and our prior works, we have also chosen

Semantic Web as a main method in our proposal of

the model for the integration of the Cloud ERP and

IoT services. The main idea of the Semantic Web is

to provide coherent data model that is a part of the

web infrastructure (Berners-Lee et al., 2001). One

data item can point to another using standard links.

The fundamental concepts of Semantic Web are

(Berners-Lee et al., 2001): the AAA slogan (anyone

can say anything about any topic), open world (it is

assumed that there is always more information than

known), and non-unique naming (the same entity can

have more names).

Semantic Web consists of a number of modelling

languages that are organized in layers. The basis of

Semantic Web is the Resource Description

Framework (RDF) used for representing information

about resources that can be identified by URIs (W3C,

2004). However, we have chosen the more expressive

Web Ontology Language (OWL 2), because it is

designed to represent rich and complex knowledge,

and is most often used in related

interoperability/integration papers. The main

elements of Web Ontology Language (OWL 2) are

classes, properties, individuals, and data values

(W3C, 2009). The most important tools when

working with OWL are ontology editors (we have

used the open-source tool Protégé) used

to create and edit ontologies, and reasoners (we have

used reasoner embedded to the Protégé tool) to infer

logical consequences.

OWL is mostly used to define ontologies that

describe a certain domain. The ontologies are often

used to tackle interoperability problems (Uschold and

Gruninger, 1996). The most cited definition of

ontology is: “An ontology is an explicit specification

of a conceptualization“(Gruber, 1993). The ontology

defines basic concepts and their relationships in a

specified domain of interest. Noy and McGuinnes

define ontology as “formal explicit description of

concepts in a domain of discourse” (Noy and

McGuinness, 2001), together with their properties

and restrictions. The ontologies are most often

developed to share common understanding, reuse,

separate, and analyse the existing domain knowledge,

and make domain assumptions explicit (Noy and

McGuinness, 2001). In the next sub-section we will

briefly describe the Semantic Sensor Network

Ontology (Compton et al., 2012) , that is mostly used

in the literature as a basis for IoT ontology

development.

3.1 SSN and SOSA Ontology

In October 2017, W3C published the new version of

their Semantic Sensor Network Ontology (W3C,

2017) that will be used as a basis for our ontology for

annotation of Cloud ERP and IoT services. “The

Semantic Sensor Network (SSN) ontology is an

ontology for describing sensors and their

observations, the involved procedures, the studied

features of interest, the samples used to do so, and the

observed properties, as well as actuators. SSN follows

a horizontal and vertical modularization architecture

by including a lightweight but self-contained core

ontology called SOSA (Sensor, Observation, Sample,

and Actuator) for its elementary classes and

properties” (W3C, 2017).

SOSA extends the original scope of the SSN

ontology to include classes and properties for

actuators and sampling (see Figure 1.). Given the

increased interest to use Semantic Web technology on

individual things (sensors, actuators, and platforms),

SOSA is lightweight and does not use the more

complex language elements of the SSN (W3C, 2017).

SOSA aims at broadening the target audience (web

The Ontologically based Model for the Integration of the IoT and Cloud ERP Services

483

developers) and application areas that can make use

of Semantic Web ontologies (W3C, 2017). The new

SSN introduces additional classes and relations on top

of SOSA to model the capabilities of sensors and

actuators and the compositionality of systems (W3C,

2017).

Figure 1: Actuator perspective of the SOSA (W3C, 2017).

3.2 Semantic Web Services

All of the main Cloud ERP vendors expose some of

the services of their solutions as application

programming interfaces (APIs) in form of the SOAP

or RESTfull web services. For example, Microsoft

Dynamics NAV provides SOAP and OData web

services. An example that lists the operations of

SOAP web service to work with customer object is

depicted at the Figure 2. Also, the functionalities of

the sensors and actuators are mostly expressed in the

form of the web services, either individually (per

Web thing) or through IoT middleware or brokers,

e.g. Global Sensor Network (Aberer et al., 2006).

Current web services provide only syntactical

descriptions, so web service integration must be done

manually. Semantic web services are the integration

of Semantic Web and service-oriented architecture

implemented in the form of web services. Semantic

web services are aimed at an automated solution to

the following problems: description, publishing,

discovery, mediation, monitoring and composition of

services. To implement Semantic Web service, new

languages are used: OWL-S (Semantic Markup for

Web Services), Service Modeling Ontology

(WSMO), or lightweight approaches such as WSMO-

Lite, SAWSDL, MicroWSMO, hRESTS, and SA-

REST.

Figure 2: Sample of the MS NAV 2016 SOAP API.

3.3 AI Planning Method

AI planning is one of the most promising approaches

to solve a problem of automated Semantic Web

service composition. Sirin et al. proved the semantic

correspondences between the SHOP2 planner and

OWL-S, and they showed how one can use SHOP2

planner to compose web services (Sirin et al., 2004,

p. 2). Hierarchical Task Network (HTN planning) is

the AI planning technique that is most widely used for

practical applications (Goyal, 2010). For this reason,

we have used the HTN planning in our model to

compose ERP and IoT services. RESTfull and SOAP

web services can be translated into planning axioms

that can be used to semi-automatically compose

services relevant to stated problem or desired steps

defined in the planning problem file. The

implementation details are shown in the Section 4.1.

4 MODEL FOR THE IoT AND

CLOUD ERP SERVICES

INTEGRATION

Semantic Web is the dominant method and technique

to integrate different systems, so we have chosen it in

our work to propose model of integration of Cloud

ERP APIs and things as a service. To compose the

semantically annotated web services, we have chosen

AI planning method.

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484

Figure 3: IoT and cloud ERP integration model.

4.1 Integration Model

Our model is described in Figure 3, and the methods

and tools for each layer are described in the Table 1.

The main aim of our model is to enable integration

and interoperation of IoT services and application

programming interfaces (APIs) defined by Cloud

ERP providers. Things (sensors, actuators and

complex things) and their functionalities are exposed

as IoT services (SOAP or RESTful services)

individually or through IoT (often service oriented

and cloud based) middleware such as Global Sensor

Network (Aberer et al., 2006), openIoT (Soldatos et

al., 2015), Hydra (Eisenhauer et al., 2009), Xively etc.

All main Cloud ERP providers offer APIs in form of

SOAP or RESTful services which enable integration

of third-party application, systems or data with Cloud

ERPs. In our model, services are semantically

annotated using Semantic Web services standard.

After that, Semantic Web services can be semi

automatically composed. For this purpose, we use AI

planning technique. The similar approach was used

for similar purposes in the existing literature: for

example, to integrate cloud services of different cloud

providers (Androcec et al., 2015), and to enable

interoperability of different IoT services (Androcec

and Vrcek, 2016b). The main advantage of our

proposed model is that it enables integration of the

IoT services with the chosen Cloud ERP solution or

multiple Cloud ERP solutions.

To semantically annotate web services, SAWSDL

will be used in this work. It enables the usage of the

semantic annotation by specifying references to

semantic models such as SSN and SOSA ontologies

mentioned before in this work. The concept from the

semantic models can be referenced from WSDL or

XML schema. A model reference can be used with

every WSDL element, but its meaning is defined in

SAWSDL only for interface, operation, fault,

xs:element, xs:complexType, xs:simpleType and

xs:attribute (W3C, 2007). The same annotation on a

WSDL operation or fault gives semantic information

about the annotated operation or fault, and it provides

a classification of the interface on a WSDL interface.

The support for data mediation in SAWSDL is

provided by using the 'liftingSchemaMapping' and

‘loweringSchemaMapping’ attributes on web service

message input and output elements to create

mappings with the ontology concept with which input

or output is associated with (Nagarajan et al., 2007).

Table 1: Methods and techniques of the proposed model.

Layer

Proposed methods, tools

or techniques

Physical (sensors,

actuators, and complex

things)

Native interfaces of things

(serial ports, WiFi, cloud,

etc.). Optional usage of

IoT middleware (e.g.

GSN, openIoT).

Web service layer (IoT

services and APIs of

Cloud ERP providers)

SOAP and RESTfull

services

Semantic Web service

layer

SAWSDL, XSLT

Semi-automatic

composition of Cloud

ERP APIs and IoT

services

AI planning technique

using JSHOP2 tool

Web operations and their inputs/outputs will be

semantically annotated, and SAWSDL and XSLT

will be used to define service type mappings, similar

to the work (Androcec et al., 2015) where semantic

annotation were used to annotate APIs of different

cloud providers. Data mediation will be ontology

based. We use the new version of the mentioned

W3C’s ontologies: SSN and SOSA to annotate thing

as service. We also plan to upgrade the mentioned

ontologies to include Cloud ERP APIs

functionalities, inputs and outputs to enable

interoperability between Cloud ERP APIs and

semantically annotated things as a service.

SAWSDL provides its lifting and lowering

schema mapping features to map XML elements to

the ontology and back. Use of cross-Cloud ERP and

IoT services concepts for data types in the ontology

simplifies mappings, and enables the creation of new

mappings and possible transformations, when new

Cloud ERP offer or new IoT service is used, or when

specific API is changed. This is a more flexible

approach than direct mapping and transformation

approach used in web service composition languages

like BPEL. The most critical part of this approach is

the requirement for user/administrator to create valid

and meaningful mappings and transformations.

To compose the semantically annotated web

services, we have chosen the AI planning technique.

Concretely, we have used JSHOP2 tool (Ilghami,

2006, p. 2). JSHOP2 is a Java version of Simple

Hierarchical Ordered Planner (SHOP). It is used to

The Ontologically based Model for the Integration of the IoT and Cloud ERP Services

485

generate sequential plans. It is based on ordered task

decomposition where tasks are planned in the same

order as later in execution (Ilghami, 2006, p. 2). The

objective of JSHOP2 and other HTN planners is to

accomplish a set of tasks where each task can be

decomposed, until primitive tasks (Ilghami and Nau,

2003) are reached. The inputs of JSHOP2 are a

planning domain and a planning problem. In

JSHOP2, primitive tasks are called operators whose

name must begin with an exclamation mark. The

body of an operator consists of precondition (must be

satisfied to execute the action), delete list (set of

properties that will be removed), and add list (set of

properties that will be added) (Ilghami, 2006, p. 2).

Solving a planning problem in JSHOP2 is done in

three steps: the domain description file is compiled

into Java code, the problem descriptions are

converted into Java class, and the second Java class

should be executed to initiate the planning process

and inspect the planning results.

5 DISCUSSION AND

CONCLUSIONS

The possible application of the proposed model for

IoT services and Cloud ERP APIs can be done by

choosing a business processes in one ERP module

(e.g. maintaining and services) and connecting

resources (e.g. equipment) with IoT devices (e.g.,

temperature or movement sensors) that will collect

real-time data. Based on these data, through ERP

system we can try to accelerate and optimize

everyday activities and proactively increase business

effectiveness and efficiency. This will be added

functionality of ERP system achieved through custom

forms (e.g. page in Microsoft NAV). Business rules

will be triggered on some values and workflows.

Using tools for business intelligence, we can analyse

the various performance indicators, create business

reports or new segments of monitoring for selected

business processes through the ERP system. For

example, we can use IoT service that returns

temperature from the sensor attached to a specific

machine in a specific production hall. If company

uses e.g. Microsoft Dynamics NAV 2016 ERP

system, we can find in its documentation that it

provides SOAP web operation void Create(ref Entity

entity) that creates a single record. We can

semantically annotate the mentioned two services and

use our proposed method to store temperature data in

ERP database that can be further used for some

analysis or for a new action request in the ERP

system.

IoT can bring positive impact on the companies’

performance by increasing operational efficiency and

by reducing operating costs. The connected things

allow cost reduction, e.g. if extraordinary

maintenance (detection of abnormal parameters by

integrated sensors) and malfunctions of the machines

are reported immediately and integrated with used

Cloud ERP solutions. The IoT is also able to improve

the inventory management.

The main contribution of our work is a cloud-IoT

integration state-of-the-art and the proposal of the

model for the integration of IoT services and Cloud

ERP APIs at the service level. Our model uses

Semantic Web technologies, ontologies (SSN, SOSA,

and the Cloud ERP API ontology), SAWSDL to

define Semantic Web services, and AI planning

technique to semi automatically compose defined IoT

services and Cloud ERP APIs. Many ERP vendors

provide a way to integrate IoT data with their

systems, but big disadvantage is that they provide

proprietary tools and methods applicable only for

their solution. What if the customer needs or wants to

switch Cloud ERP solution? Our approach is more

general and flexible because it does not rely on

proprietary technology or specific Cloud ERP

vendors.

As a future work, we plan to implement the

proposed model and develop proof-of-concept

software to integrate and use sensors data in different

Cloud ERPs. For this purpose, we plan to design

various related experiments. We also plan to develop

the ontology for classification and descriptions of

Cloud ERP APIs. Integration of IoT and business

process management suite’s (BPMS) services seems

an interesting future research subject, as sensors

or/and actuators can accept roles in the workflow. IoT

covers a huge range of devices which produce useful

information for organizations, so we believe that

integration of IoT services and Cloud ERP systems is

important research and professional topic.

REFERENCES

Aberer, K., Hauswirth, M., Salehi, A., 2006. The Global

Sensor Networks middleware for efficient and flexible

deployment and interconnection of sensor networks

(Technical Report No. LSIR-REPOR T-2006-006).

Ecole Polytechnique Federale de Lausanne, Lausanne.

Alexakos, C., Anagnostopoulos, C., Kalogeras, A.P., 2016.

Integrating IoT to manufacturing processes utilizing

semantics. IEEE, pp. 154–159. https://doi.org/10.1109/

INDIN.2016.7819150.

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

486

Androcec, D., Vrcek, N., 2016a. Ontologies for Platform as

Service APIs Interoperability. Cybern. Inf. Technol. 16.

https://doi.org/10.1515/cait-2016-0065.

Androcec, D., Vrcek, N., 2016b. Thing as a Service

Interoperability: Review and Framework Proposal. In

IEEE 4th International Conference on Future Internet

of Things and Cloud (FiCloud). IEEE, pp. 309–316.

https://doi.org/10.1109/FiCloud.2016.51.

Androcec, D., Vrcek, N., Kungas, P., 2015. Service-Level

Interoperability Issues of Platform as a Service, In IEEE

World Congress on Services (SERVICES 2015). IEEE,

New York City, pp. 349–356. https://doi.org/10.1109/

SERVICES.2015.60.

Ashton, K., 2010. The “Internet of Things” Thing. RFID J.

Berners-Lee, T., Hendler, J., Lassila, O., 2001. The

Semantic Web. Sci. Am. 284, 29–37.

Boza, A., Cuenca, L., Poler, R., Michaelides, Z., 2015. The

interoperability force in the ERP field. Enterp. Inf. Syst.

9, 257–278. https://doi.org/10.1080/17517575.2013.

866697.

Botta, A., de Donato, W., Persico, V., Pescapé, A., 2014.

On the Integration of Cloud Computing and Internet of

Things, In 2014 International Conference on Future

Internet of Things and Cloud, Barcelona, pp. 23-30.

Compton, M., Barnaghi, P., Bermudez, L., García-Castro,

R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M.,

Henson, C., Herzog, A., Huang, V., Janowicz, K.,

Kelsey, W.D., Le Phuoc, D., Lefort, L., Leggieri, M.,

Neuhaus, H., Nikolov, A., Page, K., Passant, A., Sheth,

A., Taylor, K., 2012. The SSN ontology of the W3C

semantic sensor network incubator group. Web Semant.

Sci. Serv. Agents World Wide Web 17, 25–32.

https://doi.org/10.1016/j.websem.2012.05.003.

de Souza, L.M.S., Spiess, P., Guinard, D., Köhler, M.,

Karnouskos, S., Savio, D., 2008. SOCRADES: A Web

Service Based Shop Floor Integration Infrastructure. In:

Floerkemeier, C., Langheinrich, M., Fleisch, E.,

Mattern, F., Sarma, S.E. (Eds.), The Internet of Things.

Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 50–

67. https://doi.org/10.1007/978-3-540-78731-0_4.

Eisenhauer, M., Rosengren, P., Antolin, P., 2009. A

Development Platform for Integrating Wireless

Devices and Sensors into Ambient Intelligence

Systems. In: SECON Workshops ’09. 6th Annual IEEE

Communications Society Conference on Sensor, Mesh

and Ad Hoc Communications and Networks

Workshops. IEEE, Rome, Italy, pp. 1–3.

https://doi.org/10.1109/SAHCNW.2009.5172913.

Goyal, A., 2010. Real-Time Planning Using HTN in Stealth

Game.

Gruber, T.R., 1993. A translation approach to portable

ontology specifications. Knowl. Acquis. 5, 199–220.

https://doi.org/10.1006/knac.1993.1008.

Haller, S., Karnouskos, S., Schroth, C., 2009. The Internet

of Things in an Enterprise Context. In: Domingue, J.,

Fensel, D., Traverso, P. (Eds.), Future Internet – FIS

2008. Springer Berlin Heidelberg, Berlin, Heidelberg,

pp. 14–28. https://doi.org/10.1007/978-3-642-00985-

32.

Ilghami, O., 2006. Documentation for JSHOP2 [WWW

Document]. URL http://sourceforge.net/projects/shop/files/

JSHOP2/ (accessed 7.5.13).

Ilghami, O., Nau, D.S., 2003. A General Approach to

Synthesize Problem-Specific Planners (Technical

Report No. CS-TR-4597). University of Maryland,

College Park, Maryland.

Johansson, B., Alajbegovic, A., Alexopoulo, V.,

Desalermos, A., 2015. Cloud ERP Adoption

Opportunities and Concerns: The Role of

Organizational Size. IEEE, pp. 4211–4219. https://doi.

org/10.1109/HICSS.2015.504.

Lee, I., Lee, K., 2015. The Internet of Things (IoT):

Applications, investments, and challenges for

enterprises. Bus. Horiz. 58, 431–440.

https://doi.org/10.1016/j.bushor.2015.03.008.

Meyer, S., Ruppen, A., Magerkurth, C., 2013. Internet of

Things-Aware Process Modeling: Integrating IoT

Devices as Business Process Resources. In: Salinesi, C.,

Norrie, M.C., Pastor, Ó. (Eds.), Advanced Information

Systems Engineering. Springer Berlin Heidelberg,

Berlin, Heidelberg, pp. 84–98. https://doi.org/10.1007/

978-3-642-38709-8_6.

Molano, J.I.R., Lovelle, J.M.C., Montenegro, C.E.,

Granados, J.J.R., Crespo, R.G., 2017. Metamodel for

integration of Internet of Things, Social Networks, the

Cloud and Industry 4.0. J. Ambient Intell. Humaniz.

Comput. https://doi.org/10.1007/s12652-017-0469-5.

Nagarajan, M., Verma, K., Sheth, A.P., Miller, J.A., 2007.

Ontology Driven Data Mediation in Web Services. Int.

J. Web Serv. Res. 4, 104–126. https://doi.org/

10.4018/jwsr.2007100105.

Noy, N.F., McGuinness, D.L., 2001. Ontology

Development 101: A Guide to Creating Your First

Ontology.

Picek, R., Mijač, M., Andročec, D., 2017. Acceptance of

Cloud ERP Systems in Croatian Companies: Analysis

of Key Drivers and Barriers. In 20th International

Scientific Conference on Economic and Social

Development (Book of Proceedings).

Rian van Heur, 2015. Internet of things could make ERP

flexible, intelligent and real-time. Computerweekly.

Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D., 2004.

HTN planning for Web Service composition using

SHOP2. Web Semant. Sci. Serv. Agents World Wide

Web 1, 377–396. https://doi.org/10.1016/j.websem.

2004.06.005.

Soldatos, J., Kefalakis, N., Hauswirth, M., Serrano, M.,

Calbimonte, J.-P., Riahi, M., Aberer, K., Jayaraman,

P.P., Zaslavsky, A., Žarko, I.P., Skorin-Kapov, L.,

Herzog, R., 2015. OpenIoT: Open Source Internet-of-

Things in the Cloud. In: Podnar Žarko, I., Pripužić, K.,

Serrano, M. (Eds.), Interoperability and Open-Source

Solutions for the Internet of Things. Springer

International Publishing, Cham, pp. 13–25.

Song, Z., Cardenas, A.A., Masuoka, R., 2010. Semantic

middleware for the Internet of Things. IEEE, pp. 1–8.

https://doi.org/10.1109/IOT.2010.5678448.

Spiess, P., Karnouskos, S., Guinard, D., Savio, D., Baecker,

O., Souza, L.M.S. de, Trifa, V., 2009. SOA-Based

Integration of the Internet of Things in

The Ontologically based Model for the Integration of the IoT and Cloud ERP Services

487

Enterprise Services. IEEE, pp. 968–975.

https://doi.org/10.1109/ICWS.2009.98.

Uschold, M., Gruninger, M., 1996. Ontologies: principles,

methods and applications. Knowl. Eng. Rev. 11, 93.

https://doi.org/10.1017/S0269888900007797.

W3C, 2017. Semantic Sensor Network Ontology (W3C

Recommendation No. OGC 16-079).

W3C, 2009. OWL 2 Web Ontology Language Primer

(W3C Recommendation).

W3C, 2007. Semantic Annotations for WSDL and XML

Schema (W3C Recommendation). W3C.

W3C, 2004. RDF Primer (W3C Recommendation).

Zhuming Bi, Li Da Xu, Chengen Wang, 2014. Internet of

Things for Enterprise Systems of Modern

Manufacturing. IEEE Trans. Ind. Inform. 10, 1537–

1546. https://doi.org/10.1109/TII.2014.2300338.

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