Application of Data Stream Processing Technologies in Industry 4.0:

What is Missing?

Guenter Hesse

, Werner Sinzig, Christoph Matthies

and Matthias Uﬂacker

Hasso Plattner Institute, University of Potsdam, August-Bebel-Str. 88, 14482 Potsdam, Germany

Keywords:

Industry 4.0, Internet of Things, Data Stream Processing, Data Integration.

Abstract:

Industry 4.0 is becoming more and more important for manufacturers as the developments in the area of

Internet of Things advance. Another technology gaining more attention is data stream processing systems.

Although such streaming frameworks seem to be a natural ﬁt for Industry 4.0 scenarios, their application in

this context is still low. The contributions in this paper are threefold. Firstly, we present industry ﬁndings

that we derived from site inspections with a focus on Industry 4.0. Moreover, our view on Industry 4.0 and

important related aspects is elaborated. As a third contribution, we illustrate our opinion on why data stream

processing technologies could act as an enabler for Industry 4.0 and point out possible obstacles on this way.

1 INTRODUCTION

In the backlight of technological and economic devel-

opments, the term Industry 4.0 gained more and more

popularity. Technically, new Internet of Things (IoT)

technologies, such as sensors, are being created, sen-

sor accuracy increases and analytical IT systems are

being developed that allow querying huge amounts of

data within seconds, to name but a few. On the eco-

nomic side, a substantial price decrease for sensor IoT

equipment can be recognized. This trend is expected

to continue in the following years. To be more con-

crete, the price for an IoT node is expected to drop by

about 50% from 2015 to 2020 (McKinsey&Company,

2015). These developments fostered the increased

deployment of IoT technologies in companies, es-

pecially in the manufacturing sector, and thus, more

IoT data is available to companies (Weiner and Line,

2014). Monetarily expressed, the total global worth

of IoT technology is expected to reach USD 6.2 tril-

lion by 2025. One of the industry sectors investing

most on IoT is industrial manufacturing (Intel, 2014).

A related term in the context of manufacturing

that gained attention in the past years is Industry 4.0.

One reason for that is the potential that is seen in

it with respect to creating an added value for enter-

prises. A survey conducted by McKinsey in January

2016 amongst enterprises in the US, Germany, and

https://orcid.org/0000-0002-7634-3021

https://orcid.org/0000-0002-6612-5055

Japan with at least 50 employees highlights the signif-

icance of Industry 4.0. The study reveals, e.g., that the

majority of companies expect Industry 4.0 to increase

competitiveness (McKinsey&Company, 2016).

One of the identiﬁed key challenges is integrating

data from different sources to enable Industry 4.0 ap-

plications (McKinsey&Company, 2016). Especially

with the emerging signiﬁcance of IoT data, the fairly

old challenge of integrating disparate data sources

gets a new ﬂavor. Data Stream Processing Systems

(DSPSs) can be a technology suitable for tackling this

issue of data integration. Within this paper, a view on

Industry 4.0 as well as the potential of Data Stream

Processing technologies in that context is presented.

Following the introduction, industry insights re-

lated to Industry 4.0 observed through interviews and

site inspections are highlighted. In Section 3, we elab-

orate our view on Industry 4.0, i.e., our deﬁnition as

well as our view on data integration and IoT. After-

ward, Section 4 discusses DSPSs and their role in the

area of Industry 4.0, including challenges regarding

their application in Industry 4.0 settings. A section to

related work and a conclusion complete this paper.

2 OBSERVATIONS IN INDUSTRY

Beginning in 2015, we conducted interviews with

multiple enterprises with a focus on Industry 4.0 im-

plementation strategies and associated challenges and

304

Hesse, G., Sinzig, W., Matthies, C. and Uﬂacker, M.

Application of Data Stream Processing Technologies in Industry 4.0: What is Missing?.

DOI: 10.5220/0007950203040310

In Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019), pages 304-310

ISBN: 978-989-758-377-3

solutions. In this section, we describe and contrast the

Industry 4.0 efforts of two selected companies. Both

enterprises belong to the manufacturing sector and are

comparatively large, with more than 10,000 employ-

ees and revenue of more than e 1bn each.

2.1 Company I

The ﬁrst company collects sensor and log data from

two sources, its machines used for manufacturing as

well as from its sold products used by its customers.

About 250 of the vended machines were conﬁgured

to collect and send sensor and log data to an exter-

nal central cloud storage service back in late 2015.

The data is sent as a batch every 23 hours and in-

cludes several state values, such as temperature and

position information. Overall, that results in about

800GB data on a monthly basis. Another external

company is responsible for data cleansing and some

basic calculations. The results are then used by Com-

pany I. As Company I is producing the machines, they

also developed the format of the log data that is col-

lected. Over time, this format changed with different

software releases, which introduces additional com-

plexity with respect to data integration.

Regarding the machines used for manufacturing,

ﬁve machines are conﬁgured to collect sensor data.

This data is recorded every 100ms and sent every hour

to the same cloud storage service. Each batch is about

20MB big with respect to size. It contains, e.g., infor-

mation about energy consumption and position data.

As of late 2015, none of the collected data has

ever been deleted. Moreover, the stored sensor data

had not found its way into an application at that point

in time. However, Company I expected growth in its

services area. As part of that, it could imagine of-

fering several services around its products for which

the collected data would be useful. Predictive main-

tenance or remote service and support scenarios are

an example of such services. Besides, the collected

data could reveal further insights about product usage

and behavior, which could help product development.

The internally captured data could be used for, e.g.,

predictive maintenance or quality improvements sce-

narios. The knowledge about production behaviors of

previously manufactured products can be combined

and gained learnings can be used to support product

development and production planning.

2.2 Company II

The second company has several measurement sta-

tions in its production line. At these stations, cer-

tain parts of the product-to-be are gauged. Result-

ing data are mostly coordinates, e.g., borehole posi-

tions. By doing so, possible inaccuracies added in

previous production steps are identiﬁed. If an inaccu-

racy exceeds a threshold, the corresponding product

is removed from the production line and the mistakes

are corrected if possible.

Furthermore, there is a central database storing all

warning or error messages that appear in the produc-

tion line. A higher ﬁve-digit number of messages oc-

curs on a single day on average, whereas this number

can go up to more than a million messages. Besides

the time stating when the deviation took place, the

point in time when it is remedied is stored next to fur-

ther values describing the event.

With respect to Industry 4.0 applications, the com-

pany was in the evaluation process, meaning thinking

about how the existing data could be used for such

scenarios. Back in 2015, the stored warnings and er-

rors had a documentary character rather than being

used in applications for, e.g., preventing future devi-

ations or optimizing processes. However, it was an

objective to leverage this data more in such kind of

programs. The measurement data was considered ﬁrst

for this kind of evaluations.

2.3 Industry Study Conclusions

Both presented and studied companies have in com-

mon the positive view on Industry 4.0, meaning they

see it as a chance rather than a threat, which ﬁts

the before-mentioned survey conducted by McKin-

sey (McKinsey&Company, 2015). However, neither

of the companies, which can both be considered as

leaders with respect to market share or revenue, have

been able to signiﬁcantly leverage the potential of In-

dustry 4.0. None of them is using data stream pro-

cessing technologies in this domain so far. To be more

concrete, IoT data is collected but no major new ap-

plications using this data or even combining it with

business data have been introduced. That might serve

as an example for technological leaders struggling to

implement new innovations. This situation is often

referred to as the innovator’s dilemma, which elab-

orates on the challenge for successful companies to

stay innovative (Christensen, 2013).

3 INDUSTRY 4.0

In this section, we elaborate on the Industry 4.0-

related topics of data integration and the Internet of

Things. Based on that, we present our view on the

term Industry 4.0 afterward.

Application of Data Stream Processing Technologies in Industry 4.0: What is Missing?

305

3.1 Data Integration

Being able to map data from different sources belong-

ing together is crucial to get holistic pictures of pro-

cesses, entities, and relationships. The more data can

be combined, the more complete and valuable is the

created view that is needed for fact-based assessments

and decisions within enterprises. Consequently, better

data integration and so more available data can lead to

greater insights and understanding, better decisions,

and thus, to a competitive advantage.

After giving a brief overview of the current sit-

uation in enterprises that we discovered through

conducted interviews, site inspections, and research

projects, our views on the terms horizontal and verti-

cal data integration are elaborated.

3.1.1 Current Situation in Enterprises

Business processes are central artifacts that describe

an enterprise and the infrastructure they are embed-

ded in. Business systems represent such processes

digitally, e.g., in the form of data model entities like

a customer, customer order, product, production or-

der, or journal entries. For different companies, the

semantics of these entities can vary, which can ham-

per data integration exceeding company boundaries.

Within a single company, deﬁnitions should be clear.

However, that does not necessarily represent reality.

Besides business systems, sensors or IoT-related

technologies become a greater source of data that is

describing processes and infrastructure in an enter-

prise. This information is usually connected to busi-

ness systems, such as an Enterprise Resource Plan-

ning (ERP) system or alike, via a Machine Execution

System (MES) in the manufacturing sector. Addition-

ally, there might be more systems installed underlying

the MES responsible for managing the shop ﬂoor.

A typical IT landscape comprises many different

business systems. D

ohler, for instance, a company

with more than 6,000 employees from the food and

beverage industry, has more than ten business sys-

tems and supporting systems that need to be managed

and where ideally data can be exchanged amongst

each other. In addition to an ERP system, there

are, e.g., systems for customer relationship manage-

ment, extended warehouse management, and an en-

terprise portal (D

ohler, 2019; SAP, 2018). Often,

fragmented IT landscapes have been developed his-

torically and complexity increased through, e.g., ac-

quisitions. Simpliﬁcation is a challenge in companies,

e.g., due to the lack of knowledge about old systems

that might be still used.

But even if all business systems are from the same

vendor, entities can differ between systems. A cen-

tralization to a single ERP system is unlikely to hap-

pen for multiple reasons. Such arguments can be re-

lated to aspects like data security of sensitive data,

e.g., HR data shall be decoupled from the main ERP,

or the wish to be not dependent on a single software

vendor for economic or risk diversiﬁcation reasons.

Figure 1 visualizes a very simpliﬁed IT landscape

how it can be found at companies belonging to the

manufacturing sector. It distinguishes between differ-

ent system categories and highlights the areas of hor-

izontal and vertical integration, that are explained in

Section 3.1.2 and Section 3.1.3 respectively.

3.1.2 Horizontal Data Integration

We see horizontal data integration as a holistic view

of business processes, i.e., from the beginning to the

end. Technically, that means joining database tables

stored within business systems that are involved in

the business process execution as conceptually out-

lined in Figure 1. These links can be established, e.g.,

through foreign key dependencies. The greater the

number of tables that can be connected, the more de-

tailed and valuable the resulting view on a process. As

mentioned before, enterprises generally have multiple

business systems for the elaborated reasons, which in-

creases the effort for achieving a horizontal data inte-

gration. Compared to vertical integration, horizontal

integration is further developed having relatively ad-

vanced software solutions for achieving it.

3.1.3 Vertical Data Integration

Vertical data integration describes the connection of

technical data created by IoT technologies and busi-

ness systems, including the systems in between these

two layers as depicted in Figure 1. That means, two

different kinds of data have to be combined in contrast

to integrating only business data as in horizontal data

integration. These distinct data characteristics intro-

duce new challenges.

While business data is well-structured and with a

comparatively high degree of correctness, sensor data

can be relatively unstructured and error-prone. Con-

trary to the close business process reference of busi-

ness data, sensors have a strong time and location ref-

erence. Moreover, both volume and creation speed

of IoT data is generally higher, which impacts, e.g.,

the performance requirements on IT systems handling

this kind of data (Hesse et al., 2017b).

Moreover, it is a challenge to map entities in busi-

ness processes, such as a product that is being pro-

duced, to the corresponding IoT data that has been

measured while exactly this product has been pro-

duced at the corresponding workplace. In contrast to

DATA 2019 - 8th International Conference on Data Science, Technology and Applications

306

Figure 1: Conceptual Overview of Data Integration in the Context of Industry 4.0.

integrating relatively homogeneous data among busi-

ness systems, foreign keys cannot simply be used. In-

stead, a time-based approach is often applied, which

can potentially introduce errors due to imprecise time

measurements. However, the progress of vertical data

integration we experienced in site inspections, e.g., in

the form of being able to map sensor measurements

created at production machines to the corresponding

products that were being produced, is not as advanced

as the horizontal integration. Nevertheless, vertical

integration as in the previously described scenario is

desired since it can help to get further insights about

processes and thus, support to create an added value.

3.2 Internet of Things

Internet of Things is a term often used in the context

of Industry 4.0 that has versatile meanings. Originally

emerged out of the area of radio frequency identiﬁ-

cation (RFID), where it described connected physi-

cal objects using this technology, the term IoT be-

came broader over recent years. It is not limited to

RFID technology anymore, but also comprises, e.g.,

things connected via sensors or machine-to-machine

communication. Additionally, applications leverag-

ing these technologies are referred to as IoT (Ashton

et al., 2009; Miorandi et al., 2012).

We see IoT as network-connected physical ob-

jects, whereas it does not matter which exact technol-

ogy is used for establishing a connection. Moreover,

IoT is an enabler for Industry 4.0 as it is driving verti-

cal data integration and thus, paving the way for new

business applications. Through making machines or

physical objects in general digitally accessible, new

data can be analyzed, new insights be gained and a

more holistic view of processes can be created. This

increased level of live information can lead to a com-

petitive advantage for enterprises.

3.3 Our View on Industry 4.0

We see Industry 4.0 as a term describing an advanced

way of manufacturing enabled and driven by techno-

logical progress in various areas.

These areas can be categorized into two groups,

developments with respect to IoT technologies and re-

garding IT systems. While the advances related to IoT

enable to gain new, higher volumes and more precise

measurements, the IT system development progresses

allow to analyze high volumes of data with reasonable

response times nowadays. Moreover, high volumes of

data created with a high velocity can also be handled

with the help of modern DSPSs.

These achievements lead to new opportunities in

manufacturing. New data is being generated in high

volume and velocity, which can now also be analyzed

in a reasonable amount of time with state-of-the-art

IT systems. This natural ﬁt of two technological de-

velopments generates opportunities. Making use of

both advances in combination with full data integra-

tion, i.e., horizontally as well as vertically, raises the

level of detail and completeness enterprises can have

on their processes and entities. This information gain

• leads to the enablement of better data-driven deci-

sions,

• facilitates new insights into processes or entities,

• creates the opportunity for new business applica-

tions, and

• allows for rethinking the way of manufacturing.

Speciﬁcally, holistic data integration enables a

ﬂexible and more customizable production, i.e., mov-

ing from a nowadays commonly existing batch-wise

production to piece-wise production while not sacri-

ﬁcing economic performance. Although we have not

observed a batch size of one as an explicitly formu-

lated objective in our side inspections, it was consid-

Application of Data Stream Processing Technologies in Industry 4.0: What is Missing?

307

ered as a desirable situation. Generally, we got the

impressions that there are greater challenges related

to IT compared to those related to the engineering as-

pect of IoT.

4 DATA STREAM PROCESSING

SYSTEMS

In this section, our view on the potential role of

DSPSs in the context of Industry 4.0 is presented.

Moreover, the related challenges that need to be tack-

led are highlighted.

4.1 A Possible Role in Industry 4.0

Although data stream processing systems is not a new

technology, it gained more attraction in the past cou-

ple of years (Hesse and Lorenz, 2015). Reasons for

that are technological advances, e.g., with respect to

distributed systems on the one hand, and on the other

hand the grown need for such systems due to the in-

creased data masses that are being created through de-

velopments like IoT for instance.

We think that stream processing technologies have

the potential to play a central role in the context of In-

dustry 4.0. A reason for that is its suitability regarding

the data characteristics of processed data, which ﬁt

the overall purpose behind DSPSs. Instead of issuing

a query that is executed once and returns a single re-

sult set as in a database management system (DBMS),

DSPSs execute queries on a continuous basis. Simi-

larly, IoT data is often generated on a continuous ba-

sis, which is contrary to traditional business data.

Altering requirements, e.g., due to growing data

volumes introduced by added machines or advanced

IoT technologies, can be handled as modern DSPSs

are typically scalable distributed systems. As another

consequence, high elasticity is enabled, i.e., nodes can

be added or removed from the cluster as the work-

load increases or decreases. This ﬂexibility is ad-

vantageous from an economic perspective. Especially

manufacturers that do not produce during certain pe-

riods, generally speaking companies with large IoT

workload variations, can beneﬁt.

Scalability can be reached by using a message bro-

ker between the sources of streaming data and the

DSPS. That is a common approach seen in many ar-

chitectures, both in industry and science (Hesse et al.,

2017b). A schematic overview of a possible architec-

ture is visualized in Figure 2.

IoT devices, such as manufacturing equipment,

can send their measurements to a message broker,

Business

IoT Device

DSPS

Business

System

Message

Broker

Business

MES

Figure 2: Example Industry 4.0 IT Architecture in Funda-

mental Modeling Concepts (FMC).

from which a DSPS can consume the data. Stream-

ing applications that require more than IoT data, i.e.,

programs that need vertical integration, can also be

realized using DSPSs. Corresponding data can be

consumed via established interfaces, such as JDBC,

and enrich the IoT data. If a horizontal data integra-

tion can be achieved in the business system layer. A

holistic view on entities or processes can then be cre-

ated in the DSPS where all data is brought together.

Additionally, data from MES systems or alike can be

integrated as depicted in Figure 2. That makes data

stream processing technologies a suitable framework

for developing Industry 4.0 applications whose use

case does not have further requirements that can not

be satisﬁed in this setting.

Summarizing, since DSPSs are capable of han-

dling the high volume and high-velocity IoT data as

mentioned previously, they can act as an enabler for

vertical integration and thus, for Industry 4.0 scenar-

ios. Data can be analyzed on the ﬂy without the need

of storing high volumes of data in advance, which has

a positive impact economically as well as on the per-

formance side. An imaginable pre-aggregation that

would lower these effects is not needed. Moreover,

aggregation comes at the cost of data loss and thus,

sacriﬁces accuracy.

4.2 Challenges

Certain challenges exist that could hinder an estab-

lishment of data stream processing technologies in the

context of Industry 4.0 on a broader scale.

One reason is the existing lack of a broadly ac-

cepted abstraction layer for formulating queries or

DATA 2019 - 8th International Conference on Data Science, Technology and Applications

308

developing applications, such as SQL for DBMSs.

Similarly, Stonebreaker, C¸ etintemel and Zdonik men-

tioned the need for DSPSs to support a high-level

stream processing SQL as one of the eight require-

ments of real-time stream processing they deﬁned

in (Stonebraker et al., 2005). The lack of such an es-

tablished abstraction layer introduces multiple chal-

lenges. It reduces ﬂexibility for enterprises as af-

ter choosing a certain system, the boundaries to ex-

actly this system are comparatively tight. Switching

to another framework, e.g., due to altered system re-

quirements or changed performance ratios amongst

the group of existing systems, is more complex and

thus, costlier for companies. Streaming applications

need to be developed using native system APIs, which

results in high porting effort if a system is supposed

to be exchanged compared to the effort needed for

switching a DBMS. The resulting potentially needed

SQL adaptions are relatively small since the same

abstraction layer, namely SQL, is also used in the

new system in typical scenarios. There are multiple

system-speciﬁc SQL dialects developed for stream

processing frameworks, but none of them gained

broader acceptance. However, there is the open-

source project Apache Beam aiming to close this gap.

It is not a domain-speciﬁc language like SQL, but

a software development kit that allows writing pro-

grams, which can be executed on any of the supported

stream processing engines. The impact of using this

abstraction layer on selected state-of-the-art DSPSs

with respect to performance is analyzed in (Hesse

et al., 2019).

Furthermore, identifying the most suitable system

might be a challenge for enterprises. Although the

growing number of DSPSs that have been developed

in recent years is generally a good thing, the more

choice the harder to make a decision for choosing a

system. Typically, performance benchmarks are used

for this task. Similarly to the previously described cir-

cumstances regarding the abstraction layers, the situ-

ation for DBMSs is more sophisticated. While there

are many well-known and often used benchmarks for

databases, such as TPC-C, TPC-H, or TPC-DS, the

area of DSPS benchmarks is signiﬁcantly less devel-

oped. Linear Road is probably the best-known bench-

mark for stream processing architectures (Arasu et al.,

2004). However, it does not reﬂect typical Indus-

try 4.0 scenarios in contrast to a benchmark currently

under development and proposed by (Hesse et al.,

2017a), which could close the gap of not having a

suitable benchmark for comparing different DSPSs

for use in the Industry 4.0 domain.

Another challenge we recognized in site inspec-

tions is the identiﬁcation of Industry 4.0 scenarios that

possibly create an added value. Although this situa-

tion is not directly linked to DSPSs, thoughts about

Industry 4.0 and technologies that can be used barely

include stream processing frameworks and their ca-

pabilities based on our industry experiences. This

lack of awareness of streaming technologies results

in not considering it for new application scenarios.

Moreover, when taken into account, there are often

reservations, such as that there is no or only little

knowledge about these technology amongst the em-

ployees. Another fear is that modern DSPSs are very

complex systems, which are hard to maintain and dif-

ﬁcult to use for application development. However,

these points could be eliminated automatically in near

future if development efforts and improvements of

DSPSs stay as high as they are at the moment.

5 RELATED WORK

A recent work developed a framework called Produc-

tion Assessment 4.0, which aims to support enter-

prises developing Industry 4.0 use cases. For doing

so, they made use of the design thinking approach.

After elaborating on the framework and its processes,

a section about its evaluation is presented. Production

Assessment 4.0 was evaluated in several consulting

projects with enterprises. However, no details about,

e.g., their data characteristics or their state of Industry

4.0 adoption progress are given (Bauer et al., 2018).

With respect to Industry 4.0, there are many ex-

isting deﬁnitions and views published. An overview

of selected perceptions of Industry 4.0 is presented

in (Mrugalska and Wyrwicka, 2017). Moreover, it

also states that there is no generally accepted deﬁni-

tion for the term Industry 4.0.

The Association of German Engineers (VDI) pub-

lished an architecture reference model for Industry

4.0 named RAMI 4.0 (Hankel and Rexroth, 2015).

It comprises three different dimensions, namely hier-

archy levels in factories, the product life cycle value

stream, and an architecture dimension containing sev-

eral layers from physical things up to the organization

and business processes.

Another work presents design principles for In-

dustry 4.0 that are derived through text analysis and

literature studies (Hermann et al., 2016). Thereby, it

is aimed to help both, the scientiﬁc community and

practitioners with this result. In total, four design

principles were identiﬁed, namely technical assis-

tance, interconnection, decentralized decisions, and

information transparency.

With regard to challenges, there is the previ-

ously mentioned work by Stonebreaker, C¸ etintemel

Application of Data Stream Processing Technologies in Industry 4.0: What is Missing?

309

and Zdonik that deﬁnes eight requirements for real-

time stream processing. Particularly, these are the

need (1) to keep the data moving, (2) for a stream-

ing SQL language as highlighted before, (3) for the

ability to handle stream imperfections, (4) to generate

predictable outcomes, and (5) to integrate stored as

well as streaming data, which ﬁts to the Industry 4.0

scenarios where business (stored) and IoT (streaming)

data are integrated. Furthermore, the requirement (6)

to ensure data safety and availability, (7) to automati-

cally scale and partition programs, and (8) to process

and respond immediately are highlighted.

6 CONCLUSION

The present paper pictures a point of view on Indus-

try 4.0 and on data stream processing systems in its

context. Thereby, contributions are threefold. First,

we present insights about current situations and opin-

ions at two selected companies with respect to Indus-

try 4.0. This includes information about data char-

acteristics and Industry 4.0 applications. All ﬁndings

were derived from site inspections and alike.

Secondly, a viewpoint on Industry 4.0 as well as

on further important and closely related aspects is

given. Among others, it ensures a common under-

standing needed for the third contribution.

This third part is about data stream processing sys-

tems. Particularly, it is about why and how this tech-

nology could become an enabler for Industry 4.0. A

possible architecture for Industry 4.0 scenarios is pro-

posed and obstacles hindering DSPSs from being ap-

plied more in this context are pointed out.

REFERENCES

Arasu, A., Cherniack, M., Galvez, E. F., Maier, D., Maskey,

A., Ryvkina, E., Stonebraker, M., and Tibbetts, R.

(2004). Linear Road: A Stream Data Management

Benchmark. In (e)Proc. International Conference on

Very Large Data Bases, pages 480–491.

Ashton, K. et al. (2009). That ’Internet of Things’ Thing.

RFID journal, 22(7):97–114.

Bauer, W., Pokorni, B., and Findeisen, S. (2018). Produc-

tion Assessment 4.0 – Methods for the Development

and Evaluation of Industry 4.0 Use Cases. In Inter-

national Conference on Applied Human Factors and

Ergonomics, pages 501–510. Springer.

Christensen, C. M. (2013). THE INNOVATOR’S DILEMMA

- WHEN NEW TECHNOLOGIES CAUSE GREAT

FIRMS TO FAIL//. Harvard Business Review Press.

ohler (2019). About D

ohler — Who we are. https://www.

doehler.com/en/about-doehler/who-we-are.html. Ac-

cessed: 2019-04-03.

Hankel, M. and Rexroth, B. (2015). Industrie 4.0: The Ref-

erence Architectural Model Industrie 4.0 (RAMI 4.0).

ZVEI, 2:2.

Hermann, M., Pentek, T., and Otto, B. (2016). Design Prin-

ciples for Industrie 4.0 Scenarios. In Hawaii Interna-

tional Conference on System Sciences, HICSS, pages

3928–3937.

Hesse, G. and Lorenz, M. (2015). Conceptual Survey on

Data Stream Processing Systems. In IEEE Interna-

tional Conference on Parallel and Distributed Sys-

tems, ICPADS 2015, pages 797–802.

Hesse, G., Matthies, C., Glass, K., Huegle, J., and Uﬂacker,

M. (2019). Quantitative Impact Evaluation of an Ab-

straction Layer for Data Stream Processing Systems.

In International Conference on Distributed Comput-

ing Systems, ICDCS.

Hesse, G., Matthies, C., Reissaus, B., and Uﬂacker, M.

(2017a). A New Application Benchmark for Data

Stream Processing Architectures in an Enterprise

Context: Doctoral Symposium. In ACM International

Conference on Distributed and Event-based Systems

(DEBS), pages 359–362.

Hesse, G., Reissaus, B., Matthies, C., Lorenz, M., Kraus,

M., and Uﬂacker, M. (2017b). Senska - Towards an

Enterprise Streaming Benchmark. In TPC Technology

Conference, TPCTC, pages 25–40.

Intel (2014). A GUIDE TO THE INTERNET OF

THINGS. https://www.intel.com/content/www/us/

en/internet-of-things/infographics/guide-to-iot.html.

Accessed: 2019-04-03.

McKinsey&Company (2015). Industry 4.0 - How

to navigate digitization of the manufacturing sec-

tor. http://www.forschungsnetzwerk.at/downloadpub/

mck\ industry\ 40\ report.pdf. Accessed: 2019-04-

02.

McKinsey&Company (2016). Industry 4.0 after the ini-

tial hype - Where manufacturers are ﬁnding value and

how they can best capture it.

Miorandi, D., Sicari, S., Pellegrini, F. D., and Chlamtac, I.

(2012). Internet of things: Vision, applications and

research challenges. Ad Hoc Networks, 10(7):1497–

1516.

Mrugalska, B. and Wyrwicka, M. K. (2017). Towards Lean

Production in Industry 4.0. Procedia Engineering,

182:466 – 473.

SAP (2018). Cloud Experts Tell It Like It Is - D

ohler’s

Best Practices on Setting Up a Hybrid IT Land-

scape. https://d.dam.sap.com/m/xmxUAO/Cloud

Experts Doehler Interview Brochure-V03.pdf. Ac-

cessed: 2019-04-03.

Stonebraker, M., C¸ etintemel, U., and Zdonik, S. B. (2005).

The 8 Requirements of Real-Time Stream Processing.

SIGMOD Record, 34(4):42–47.

Weiner, S. and Line, D. (2014). Manufacturing and the data

conundrum - Too much? Too little? Or just right?

https://www.eiuperspectives.economist.com/sites/

default/ﬁles/Manufacturing Data Conundrum Jul14.

pdf. Accessed: 2019-04-02.

DATA 2019 - 8th International Conference on Data Science, Technology and Applications

310