A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE OF THE KNOWLEDGE BASE IN INDUSTRY BY ESB, ARTIFICIAL INTELLIGENCE, AND BIG DATA SYSTEM INTEGRATION

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.5, September 2018
DOI : 10.5121/ijaia.2018.9503 27
A CASE STUDY OF INNOVATION OF AN
INFORMATION COMMUNICATION SYSTEM AND
UPGRADE OF THE KNOWLEDGE BASE IN
INDUSTRY BY ESB, ARTIFICIAL INTELLIGENCE,
AND BIG DATA SYSTEM INTEGRATION
Alessandro Massaro1,*
, Angelo Calicchio1
, Vincenzo Maritati1
, Angelo Galiano1
,
Vitangelo Birardi1
, Leonardo Pellicani1
, Maria Gutierrez Millan2
, Barbara Dalla
Tezza2
, Mauro Bianchi2
, Guido Vertua2
, Antonello Puggioni2
1
Dyrecta Lab, IT Research Laboratory, Via Vescovo Simplicio, 45, 70014 Conversano
(BA), Italy.
2
Performance in Lighting S.p.A., Viale del Lavoro 9/11 - 37030 Colognola ai Colli (VR),
Italy.
ABSTRACT
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication
system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is
gained by means of different tools that are able to provide data and information having different formats
and structures into an unique bus system connected to a Big Data. The initial part of the research is
focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the
proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz
Information Miner) Gradient Boosted Trees workflow processing data of the communication system which
travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the
new KB collected into a Cassandra big data system could be processed through the ESB by predictive
algorithms solving possible conflicts between hardware and software. The conflicts are due to the
integration of different database technologies and data structures. In order to check the outputs of the
Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been
tested. The test has been performed on a prototype network system modeling a part of the whole
communication system. The paper shows how to validate industrial research by following a complete
design and development of a whole communication system network improving business intelligence (BI).
KEYWORDS
Frascati Guideline, ESB, Data Mining, KNIME, Gradient Boosted Tree Algorithm, Big Data.
1. INTRODUCTION
Frascati manual [1] represents a useful guideline for research projects. This manual is adopted
in order to find real research topics in Research and Development R&D projects. Concerning to
the software research, the manual affirms that “the effort to resolve conflicts within hardware or
software based on the process of re-engineering a system or a network”. In this direction an
important issue to validate research is in the design and development of a communication
system allowing the information and data transfer. It is known that data coming from
technologies of different databases and tools are characterized by different structures and

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formats. For this reason the Enterprise Service Bus (ESB) [2]-[6] could solve many conflicts
between server machine and software tools such as Customer Relationship Management –
CRM-, Enterprise Resource Planning ERP-, Business Process Modeling (BPM), and Product
Lifecycle Management PLM. These tools can be tailored by means of different methodologies
such as [7]-[9]:
- The Linear model (Waterfall);
- Evolutionary development;
- Incremental Model (Waterfall in iteration);
- Spiral Model;
- Formal systems development;
- Agile Methods;
- Reuse-based development.
Starting from these approaches it is possible to formulate and to improve the new Knowledge
Base (KB) by using different databases technologies related to different tailored tools, and by
processing, by means of artificial intelligence algorithms [10]-[14], all data stored into a big
data system [15]. In particular Cassandra big data system is suitable for execution of data
mining workflows [6],[16]. Furthermore Frascati affirms that is scientific research [1] “the
creation of new or more efficient algorithms based on new techniques”, and that “The software
development component of such projects, however, may be classified as R&D if it leads to an
advance in the area of computer software. Such advances are generally incremental rather than
revolutionary. Therefore, an upgrade, addition or change to an existing program or system may
be classified as R&D if it embodies scientific and/or technological advances that result in an
increase in the stock of knowledge.”. Following in this direction the basic information industry
system (basic KB) has been upgraded by developing software tools having the main
specifications summarized in Fig. 1. The main functionalities of the software tools are also
described by the block diagram of Fig. 2 able to provide the following advantages:
• KB gain;
• Optimization of data flow management;
• New inputs for business intelligence B.I.;
• Quality improvements of the production activity;
• Data security improvements;.
The choice of basic software requirements represent the tailoring of a new information system
based on ESB/Bus communication suitable for Enterprise Application Integration (EAI) [2]-[4].
The bus systems together with big data system supports the overcoming of conflicts and
problems: as mentioned in [1]. It is a research of “the effort to resolve conflicts within hardware
or software based on the process of re-engineering a system or a network”. According to this
requirement, big data is considered as a massive information collector of different data
(structured, semi-structured, and unstructured data [17]), which can be interfaced with the ESB
in order to facilitate data migration between databases having different technologies and data
transfer into a communication system. If compared with traditional relational database systems,
big data is mostly indicated for the resolution of hardware inefficiencies linked to real time data
processing [18]. In addition, big data systems, through cloud data computing, can overcome
various problems related to memory and hardware calculation capacity [19]. According to
Italian directive of the MISE (Ministero dello Sviluppo Economico), the Frascati Manual
represents a reference for the evaluation of the industry research enabling tax incentives. For
this reason all the stages concerning a study case of applied research are discussed in the paper
thus validating the research activity developed within of the industry Pil Ligthing S.p.A.. In
order to furthermore validate the internal research, a classifier predictive algorithm has been

29
applied. A big family of classifiers consists of decision trees and their ensemble descendants.
The basic principle of a decision tree is to split the original data set into two or more subsets at
each algorithm step, so as to better isolate the desired classes (classes labeled for the prediction)
[13]. Each step then produces a split on the data set and each split can be graphically
represented as a node of a tree whose branches define a rule path classifying the labeled classes
[13]. In the actual ensemble evolution of the decision tree, instead of working with only one
decision tree, it is possible to process data by different trees. The joint data processing of all the
decision trees support the decision process and makes the algorithm more robust in terms of
possible misclassifications. Implementations of ensemble decision trees are:
• Decision Tree
• Random Forest
• Gradient Boosted Trees
In the proposed research, Gradient Boosted Trees approach has been adopted, which is a
machine learning technique for regression and classification problems. It, generates a prediction
model in the form of an ensemble of prediction models.
The paper is structured as follows:
- design and development of KB system of the industry by discussing the main software
requirements necessary to improve the KB after a preliminary “As is” process mapping;
- design and development of the Big Data and artificial intelligence tools applied on the
upgraded systems providing new predictive outputs oriented on Business Intelligence
(B.I.).
2. UPGRADE OF THE INFORMATION SYSTEM AND OF KB FOLLOWING A
RESEARCH APPROACH
The first step of the applied research concerns the gain of the KB. In order to achieve this gain the
system has been tailored made into different modules as shown in of Fig. 1. Each one of the
modules provides information and datasets to process them for the strategic marketing and B.I
(Business Intelligence). All the implemented modules exchange and transfer data by the Local
Area Network (LAN) interfaced with a unique communication bus. A preliminary management
process design has been performed in order to plan the development activities. These modules
allow upgradation of the initial KB by means of information digitalization concerning all the
industry activities. We summarize below some of the important performed improvements related
to each implemented module enhanced in Fig. 1 using different colored blocks (ARIS Express
diagram).
2.1 Security Service Anti-malware
This research concerns the endpoint protection concept including virus identification engines, and
more advanced secure systems oriented on network and Email security [20],[21]. Today there are
different types of malware performing mass attacks with the aim of compromising the operation
systems (DDOS or Distributed Denial Of Service). Furthermore modern ransomware are viruses
able to “capture” through encryption, files on personal computers PCs and servers, thus
requesting a “ransom” (Cryptolocker or Cryptowall function). The only anti-virus on the PC is
not enough and today to ensure maximum protection, it is essential to use effective endpoint
protection software, equipped with technologies of analysis of known patterns, being the
automatic verification and real-time behavior of suspicious files insufficient. In addition,

30
maximum protection is achieved when the endpoint protection integrates and collaborates
actively with the devices included into the security perimeter of the network. The technologies
adopted in the prototype system are able to provide maximum protection to clients, as they are
equipped with different systems of analysis and inspection based on an Anti-malware system
(called MaSS AM) suitable for remote management of endpoint security.
2.2 Firewall
Concerning firewall topic, the research has been focused on a new controller component
integrated with the Information Technology (IT) architecture. The software component checks the
data source using a firewall (named Barracuda) thus controlling the data flow and limiting actions
in case anomalous situations. The integration of this new software component is part of the
business integration concept, which allows to add new functionalities to the system, and to
connect existing modules with the new ones. Firewall improves data traffic blocking for
suspicious addresses. This according to the theory of Frascati [1], involves functionality in terms
of security data properly designed for the company.
2.3 Web marketing
One of the frequent errors in the development of an internet site is to adopt a one-dimensional
perspective. Some researchers in [22] suggest for this purpose to apply a particular model called
"bar model". By this model a website is seen as:
• a set of contents, messages, interactions and possible transactions;
• a set of technical tools that make the contents accessible and feasible;
• an internet page which is implemented and updated by a group of users;
• an Iteration tools with visitors;
• a group of users who access and use it.
For a good design, the following logical phases of the production process can be executed:
• Phase 1: requirements definition;
• Phase 2: project start-up;
• Phase 3: web design;
• Phase 4: visual design;
• Phase 5: development;
• Phase 6: drafting of contents;
• Phase 7: publication.
where all the phases are analyzed in details in order to apply marketing strategies.
Concerning phase 1 the requirements are classified as follows:
• Content requirements: all the messages that the customers intend to communicate through the
site;
• Structure requirements: these requirements explain how the contents are structured within the
site;
• Content access requirements: these requirements show the way in which the user can access to
web page contents;
• Navigation requirements: connections between the various contents that may be relevant to the
visitor, in order to achieve the set objectives.
• Presentation requirements: presentation of contents, visual impact to be achieved, etc. (Web
Design).

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• Requirements for user operations (indications about operations, transactions and functionalities
for the user);
• Requirements for system operations;
• Requirements related to the management of the site and its maintenance.
All the user feedbacks and sales results are used for the optimization of the web page by means of
a data mining algorithms able to enhance clusters of products most sold and to predict demand
marketing (time series forecasting prediction [23]-[24]).
2.4 Finance module
The Finance tool (called DocFinance) introduces an innovative business organization model that
must be adopted to allocate efficiently resource and to optimize treasury operations thus
supporting the relationships with the banks. This module is integrated into ERP SAP system thus
allowing to use all the information in the ESB and Big Data informatics infrastructure.
2.5 Analytics and indoor/outdoor communication system
By means of Waterfall approach, a module useful to improve Knowledge Discovery, analytics,
and indoor/outdoor communications has been developed. This module integrates Business
Process Re-engineering (BPR-) and Business Process Management (BPM) facilities embedded in
the ERP system. The Big Data analytics will provide important results mainly in the following
topics:
• Logistics (reception, warehouse management, inventory control, transport planning,
distribution management);
• Operations including maintenance and testing activities applied from the raw materials to
the final products;
• Marketing and sales (marketing channel selection, promotion activities, price and space
optimization);
• Services (customer support, installations, technology transfer).
2.6 ERP development
This module is related to a specific development of the ERP about management of accounting
processes and management of commissions. These functions have been implemented after an
accurate process analysis which enabled the reengineering process of the industry. The software
has been developed by applying Waterfall and Agile methodologies. The implementation of this
module allows the automation of different business processes thus optimizing the business
intelligence (BI).
2.7 ERP basic platform
This module is related to the development of the following basic ERP SAP modules:
• Financial Accountig (FI);
• Controlling (CO);
• Sales and Distribution (SD);
• Material Management (MM);
• Production Planning (PP);
• Business Planning and Consolidation (BPC);
• Quality Management (QM);

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• Asset Accounting (AA) ;
• Treasury (TR);
• Human Resource (HR);
• Warehouse Management (WM).
The ERP data represents the primary KB which will be transferred in the unique communication
system.
2.7 Augmented Reality –AR- Building Information Model –BIM-
This module allows to adopt augmented reality-AR- for visual marketing. The AR is adopted for
Building Information Modeling [25], specifically for virtual immersion in different environments
involving different typologies of lights. The new AR objects are allocated in libraries stored in the
a database of the information system, and are tailored by analyzing the behavior of the user
during the AR visualization. AR is an enabling technology of Industry 4.0.
Endpoint Security
Firewall Security
Web marketing
Finance
Analytics
Indoor/outdoor
Comunication
ERP Developers
ERP
Augmented Reality
(BIM)
Figure 1. Aris Express Layout: Proposed upgraded Information System of Performance in Lighting S.p.A.
3. GAIN OF KB AND TESTING PROTOTYPE BASIC NETWORK
The gain of KB is achieved by integrating all the tools described in section 2 into an unique
information system as sketched in Fig. 2. The diagram block of Fig. 2 summarizes the
requirements of Fig. 1 and highlights project goals. The initial KB is upgraded by tools able to
provide new structured and unstructured data and information useful for:
• Gain of knowledge related to different industry process in different area (administrative,
sales, production, etc.);

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• Optimization of the management of the new data flow by a communication bus;
• Improvement of data security;
• Improvement of product quality and product business.
These information can be collected into a big data and processed by an artificial intelligence
engine able to deliver advanced outputs.
Knowledge Base Gain, Management and Data Flow Control/Optimisation, Inputs for Business
Intelligence, Data Security, Quality Improvement
MASS AM
Firewall
Barracuda
Web Data
Web
marketing
DocFinance
SAP
Metel Analytics
FI CO SD MM PP
BPCQM AA TR HR WM
ENTERPRISE RESOURCE AND PLANNING (ERP)
Accounting
Optimisation
Agent
Commissions
Knowledge Base (KB)
Gain
Big Data / Artificial Intelligence
Advanced Outputs
BIM
ESB/Bus
Figure 2. Proposed Knowledge Base Gain in Performance in Lighting S.p.A.
An example of a network prototype oriented on the implementation of the scheme of Fig. 2 is
reported in Fig. 3, where the data flow is represented by the following three phases:
1) A Phyton script executed by a graphical user interface (GUI of the KNIME tool)
activates a query for a data access (data stored into a MySQL database associated with
a particular software tool);
2) The data are transferred into the big data system where it is combined with other ones;
3) The data collected in the big data system are processed by the KNIME workflow
modeling artificial intelligence algorithm.
All the three phases are characterized by the data transfer occurring in ESB. The KNIME
workflow is divided into the following main blocks, which will be described in details in
section 3.2:

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a) data preparation in local repository: the phase 1), 2) and 3) decribed above allows to
prepare data into the local repository;
b) data pre-processing: some attributes are selected and filter to be processed;
c) data processing; data are processed by a training model used to learn the algorithm and
by a testing data processing used to obtain predicted values.
We observe that the prototype network has been implemented by connecting a standard MySQL
database. Authors checked the same connectivity of ESB with Sybase ASE database technology
(ERP SAP database).
We observe that the data migration from a DB to a big data system can be also managed by the
ESB by Application Programming Interfaces (API) executed as web services (see scheme of
Fig. 4).
ESB/Bus
Phyton
Source Node
Cassandra
Big Data
DB
testing
KNIME Workflow
Data pre-
processing
Data
processing
Output
(1) (2) (3)
DB Tool 1DB Tool n
Figure 3. Testing prototype network enabling gain of KB by KNIME data mining workflow connected
to a Cassandra Big Data system. The database DB testing refers to a database having the same
technology (MySQL) of a developed tool of Fig. 1 and Fig. 2.

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ESB/Bus
Cassandra
Big Data
DB
User
Web
Service
API
1
2
Figure 4. Web service enabling data migration from a DB to a Big Data.
3.1 ESB implementation
In order to test the prototype network of Fig. 3 the WSO2 Data Service Server module (WSO2
DSS) has been implemented [26]. The main function of the prototype ESB is to connect different
databases having different technologies with the Cassandra big data systems, to manage data
transfer into the local information system, and to generate data sources which will be imported in
the local repository to be processed [6]. This module is also suitable for Service Oriented
Architecture (SOA) for the creation of multisource data systems, and for hosting data services. It
supports secure and managed data, data service transactions, and data transformation using an
agile development approach according with tools specifications and data security. The WSO2
graphical interface facilitates the multiple sources management and the formulation of structured
queries useful to select different data from different databases. In Fig. 5 is illustrated a screenshot
of the implemented WSO2 DSS ESB. The ESB has been tested for database management
involving all the tools indicated in Fig. 1 and Fig. 2 and Cassandra big data system (WSO2
includes a Cassandra database connector). In Fig. 6 is illustrated a screenshot of WSO2 indicating
Sybase ASE connectors thus confirming the compatibility of the ESB with the ERP.

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Figure 5. Screenshot of WSO2 DSS ESB implementation.
Figure 6. Screenshot of WSO2 DSS ESB implementation: Sybase ASE connectors (SAP ERP
connectors).
3.2 Cassandra Big Data and KNIME testing
Apache Cassandra on a Windows machine has been installed in order to execute testing. The only
prerequisites are [27]:
• Windows 7 or Windows 2008 server;
• Java;
• Either the Firefox or Chrome Web browser for DataStax OpsCenter;
• Visual C++ 2008 runtime (Windows 7 and Windows 2008 Server R2 has it already
installed).
In Cassandra database, data available in the testing dataset of [28] has been loaded, which is a
dataset suitable for machine learning testing .
In Fig. 7 is shown an example indicating the following attributes:

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• VoiceNo: Invoice number (Nominal type): 6-digit integral number uniquely assigned
to each transaction;
• StockCode: Product (item) code (Nominal type): 5-digit integral number uniquely
assigned to each distinct product;
• Description: Product (item) name (Nominal type).
• Quantity: The quantities of each product (item) per transaction. Numeric.
• InvoiceDate: Invoice Date and time (Numeric type) indicating the day and time when
each transaction has been executed;
• UnitPrice: Unit price (Numeric type) indicating product price per unit in sterling units.
• CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned
to each customer.
• Country: Country name. Nominal, the name of the country where each customer
resides.
The used experimental dataset is described in [28],[29]: this is a dataset which contains all the
transactions of a store occurring between 01/12/2010 and 09/12/2011 A dataset of about 50000
records (more precisely 45909 records) is extracted from the whole original dataset.
Figure 7. First records of the experimental dataset [28].
In Fig. 8 is illustrated the designed KNIME workflow able to predict sales validating the
prototype of Fig. 3. This algorithm is composed by 12 blocks named nodes. These node are:
• Node 1 (Puthon Source). This block allows to load in local repository the experimental
dataset by means of the following Puthon script:
from cassandra.cluster import Cluster
from pandas import DataFrame
cluster= Cluster()
session=cluster.connect()
query="""SELECT * FROM SAP.getVendite;"""
rows=session.execute(query)
data=[]
for row in rows:
data.append(row)
output_table=DataFrame(data);
• Node 2 (Column Filter able to filter code, date, unit price and quantity);
• Node 3 (Rule-based Row Filter used to clean not congruous fields);
• Node 4 (String Manipulation block extracting from string Date only values in day /
month / year format);
• Node 5 (String To Number-PMML- block enabling conversion from string to an
integer number of quantity and price);
• Node 6 (Math Formula –Multi Column- block allowing multiplication of quantity by
price);
• Node 7 (Column Filter block allowing elimination columns duplicated);

38
• Node 8 (Partitioning block splitting the experimental dataset into 80% of training
dataset and 20% of testing dataset by taking data from top of the list);
• Node 9 (Domain Calculator block applied to define the attributes for the training
model);
• Node 10 (Gradient Boosted Trees Learner –Regression-): this block represents the
learning of the model and embeds the algorithm implementing very shallow
regression trees and a special form of boosting to build an ensemble of trees; the
implementation follows the algorithm described in [30] (Gradient Boosted Trees
algorithm produces competitive, highly robust, interpretable procedures for both
regression and classification. This algorithm is suitable to solve “predicting
learning”[30]);
• Node 11 (Gradient Boosted Trees Predictor -Regression-): this block represents the
testing of the model and applies regression from the Gradient Boosted Trees model;
the output is the sales prediction;
• Node 12 (Numeric Score): this node computes certain statistics between the a numeric
column's values and predicted values (an error of about ± 5 receipts is estimated);
• Node 13 (Excel Writer –XLS- block which saves outputs into an excel file).
Data Source
Preprocessing
Training/Testing
Node 2 Node 3 Node 4 Node 5 Node 6 Node 7 Node 8
Node 9 Node 10
Node 11
Node 12
Node 13
Figure 8. KNIME prediction algorithm data flow. In the figure are divided the main functions of the
predictive workflow
In Fig. 9 is shown the comparison between real values and predicted ones of daily number of
receipts by taking into account a specified day for each month of the year. The prediction is
referred to the same period of the next year. A good convergence is found thus proving a
truthful prediction. Figure 7 is obtained by means of plot facilities of the exported excel file
(node 13). A part of the whole output in table format is illustrated in Fig. 10.

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Real values
Predicted values
Date (month/day/year)
Numberofreceipts
Figure 9. KNIME: Gradient Boosted Trees KNIME outputs indicating the comparison between real
values and predicted ones of number of daily receipts. The predicted values are to consider for the 2012
year.
Figure 11. KNIME table: output of the Gradient Boosted Trees Predictor node (node 11) indicating
quantity prediction.
About 11 seconds For the execution of the workflow of Fig. 6 by using a personal computer
having the following characteristics: Asus Intel Core i5 -7200U, 2.5GHz- 2,71 GHz, 8 GB Ram,
OS Windows 10, 64 bit. For the data prediction have been adopted 100 as numnber of levels, a
learning rate of 0.5, and 10 as three depth. We observe that the number of models is the number
of decision trees to learn, that the limit number of levels (three depth) is the number of tree levels
to be learned, and that the learning rate influences how much influence a single model has on the

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ensemble result (usually a value of 0.1 is a good starting point but the best learning rate also
depends on the number of models).
The same algorithm of Fig. 8 can be applied to other dataset of the upgraded communication
systems by predicting sales, financial trends, costs, AR contents impacts, revenues, visitors
number of the industry web page, social trend and other factors introduced in the new KB.
However databases of software could have some missing values due to data storage failures or to
conflicts. This problem can be overcomed by the “missing value handling” procedure related the
data- pre processing where the missing value handling can be:
• XGBoost (default): the learner will calculate which direction is best suited for missing
values, by sending the missing values in each direction of a split; the direction that
provides the best result (largest gain) is then used as default direction for missing values;
this method works with both, binary and multiway splits.
• Surrogate: this method estimates for each split alternative splits that best approximate the
best split [31]; this method can only be used with binary nominal splits.
4. CONCLUSION
The goal of the paper is to show results of a case of a study regarding research and development
applied in industry. Following “Frascati” guidelines the research has been focused on the design
and development of an ESB managing data contained into different databases and information
sources. The data source is structured by tailoring different software tools enabling the gain of the
knowledge base. The software tools have been structured by reengineering data flow process of
Pil Ligthting S.P.A. information infrastructure, and by adding new functions suitable for B.I..
These tools have been implemented by following research methodologies such as Waterfall and
Agile approaches. After the implementation and the testing of the properly designed tools has
been developed a WSO2 ESB network able to solve different conflicts between hardware and
software generated by the transfer of unstructured data and by the use of different database
technologies. The ESB allows to connect different data source technologies by managing data
transfer and collecting them into a Cassandra big data system. Big data allows to collect during
the time massive data performing analytics and advanced statistical analyses about production
trend, sales, costs etc.. Finally in the work has been developed a test a prototype communication
system proving the connectivity of WSO2 ESB with Cassandra and with data mining KNIME
algorithms. The test proved that all data stored into a data system of an application tool can be
transferred and managed by the ESB, and, consecutively, processed by a data mining predictive
algorithm. The prototype is tested by applying Gradient Boosted Trees predictive algorithm
generating sales prediction outputs with good performances. Gradient boosted algorithms are
defined by scientific community as innovative algorithms thus proving that the knowledge base is
“gained by new algorithms”. The gain of knowledge base is improved by all the communication
system of the case of study composed by software tools, ESB, Big Data and Gradient Boosted
Trees algorithms. Other innovations of the research can be found in the flowchart able to manage
data transfer and data preprocessing/data processing of the whole workflow of the KNIME
predictive algorithm. The correct execution of the KNIME algorithm proves that the research is
well matched according to the industry requirements. This work can be considered as a first
example case study demonstrated as a specified case study of Frascati research and development
(R&D) theories. The proposed work can be a reference for the researchers working in industry
projects.

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ACKNOWLEDGEMENTS
The work has been developed in the frameworks of the Italian projects: “Process, Design,
Management and Marketing in Lighting Production ‘De.Li.Pro’ ”. The authors would like to
thank the following researchers and collaborators: B. Boussahel, V. Custodero, M. Le Grottaglie,
A. Leogrande, F. Tarulli, and L. Patruno.
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Corresponding Author
Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.

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