=Paper=
{{Paper
|id=Vol-2933/paper25
|storemode=property
|title=Prototyping Data Management Model and Tasks (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2933/paper25.pdf
|volume=Vol-2933
|authors=Kalinka Kaloyanova,Ioannis Patias,Boris Robev
}}
==Prototyping Data Management Model and Tasks (short paper)==
https://ceur-ws.org/Vol-2933/paper25.pdf
Prototyping Data Management Model and Tasks
Kalinka Kaloyanova1, Ioannis Patias1, Boris Robev1
1
Faculty of Mathematics and Informatics
University of Sofia St. Kliment Ohridski“
5 James Bourchier blvd., 1164, Sofia, Bulgaria
kkaloyanova@fmi.uni-sofia.bg
Abstract. When new products are introduced, the ability to deploy them quickly on
the market is the key to their success. In this paper, we discuss an approach for data
modeling used in the Laboratory Intelligent Urban Environment – IUE-Lab (Center
for Competence in Mechatronics and Clean Technologies). The main design goal
was that any prototyping procedure elaborated in IUE-Lab should not set up a new
database. Instead, a predefined data management model could be used as a data
repository for all prototypes. This model is based on a generic data presentation
adequate for both observational and real-time data for experiments, devices or
prototypes.
Keywords: Prototyping, Data Model, Data Repository, Data Management.
1 Introduction
Prototyping is widely accepted in different projects in the IT area. The prototyping
concept has been actively used in the Intelligent Urban Environment Laboratory
(IUE-Lab) to address the needs of the MIRACle project: Mechatronics,
Innovation, Robotics, Automation, Clean technologies – for the establishment and
development of a Center for Competence in Mechatronics and Clean Technologies.
IUE-Lab aims to develop three parallels, namely Intelligent Home Environment
(IHE), Intelligent Public Environment (IPE), and Intelligent Personal Assistant
(IPA). The main idea is to develop the required infrastructure, in terms both of
hardware and of software, able to support the observational and real-time data
collection, storage, and analysis. This is important for the development of the
laboratory and its activities (see fig.1), namely:
• iVille – mobile (incl. flight) autonomous hub of data and control signals
for the urban environment;
• iVac – a mobile autonomous hub of data and control signals for the home
environment;
• iÉcole – a mobile autonomous hub of data and control signals for a public
structured environment – for example in education and administrative
Copyright © 2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
services. Within the IUE-Lab, it will be prototyped for the purposes of the
educational environment, and
• iChien – an electronic guide for blind people, based on a smartphone and
integration into the IUE-Lab.
The process of prototype management covers several different tasks, includ-
ing database and reporting templates design, for both observational and real-time
data collection, storage, and analysis. The aim is to cover various application
fields from smart city, and e-government [1], smart home, smart school, even
more specific ones related to devices to support vulnerable groups, medical de-
vices and other services [8]. Essential features for our lab activities include the
following:
• prototyping end-users support: we aim in building autonomous manage-
ment of the users without having to work with our experts.
• multiple platforms support: able to work with various configurations of
hardware and operating system.
• generic analysis platform: able to support the data collection, storage, and
various types of analysis [12].
The present paper discusses the role of prototypes in product development
and presents the IUE-Lab prototyping observational and real-time model of data
management system. The prototyping database characteristics will be presented.
The proposed generic data model fits to the requirements for reusability.
2 Prototypes and product development
Prototypes are very important for the product development process [2]. They
affect the entrepreneurship and management of innovation. The companies that
wish to be counted in the innovators group invest with R&D expenses that often
get up to a double-digit percentage of their sales. Although the expenses get that
significant the product development either, do not get to the market or do not get
the expected sales.
The development of prototypes including complicated software and hard-
ware systems, embedded or embedding other subsystems, represents a significant
cost for the companies our days and it is important to be successful. We need
to look at prototype development as a business-oriented process, but also as an
engineering-oriented process, and try to identify the corresponding approaches
that will help us in the efficient application of those approaches [3].
Applying a business-oriented approach we evaluate all those components,
which are related to the development of the product in terms of market needs a
response. However, we also need to take into consideration the realization of the
product in the market, meaning we need to manage all of the logistics, timing,
and budget-related issues, as well as the whole project coordination itself. Those
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components apart in prototype development projects are also present in many
other kinds of product realization projects.
In an engineering-oriented approach, we need to focus on the creation of the
physical product. This includes the establishment of the processes for effective
development supported with the use of appropriate methods and tools.
Finally, the successful method is the one that can incorporate both the busi-
ness-oriented and the engineering-oriented approaches.
Fig. 1. IUE-Lab activities under the MIRACle project.
For the project purposes, we need to bring together people with different
backgrounds. On the one side, the ones with the business orientation defining the
requirements, and on the other the engineers, which are requested to transform
those requirements into product prototypes.
The successful combination and the implementation of this method is the
tool for the successful prototype development and product delivery efforts. This
method helps us avoid projects time overdue, or projects over gone in the budget.
This method secures successful products on time to the market.
Trying to outline the importance of prototyping in the frame of the IUE-Lab
design process we may also study the effects on both the process and the people
involved in it.
First, we can compare engineering and the positive impact on management.
The idea is that we can use agile prototyping, or re-use previous prototypes, and
build in a scalable manner. So, by creating some tangible prototypes, usually at
low-cost, the developers become rapidly able to get end-user evaluations. This
helps evaluate the product but also helps to understand any existing trends re-
garding user acceptance. The earliest stage in the development process, you have
the end-user feedback, the better you can react and get the benefit of this feed-
back. In other words, such fast prototypes are beneficial to the product design,
and to the people involved in it. The designers gain majorly that:
• early critics from end-users are an opportunity for faster and successful
release to the market;
• the progress of product’s development step-by-step, and
• the feeling of creativity.
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Second, we can compare the effect of working in a real–physical environ-
ment versus working in a virtual environment on the people involved in product
development. Even if we divide into two groups the people involved, meaning
non-designers, non-engineering, and non-informatics, and designers, engineers,
or informatics specialists, we can see the differences when we have real envi-
ronments instead of virtual environments. All involved people get better results
when they have direct access to physical prototyping and testing environment,
with a real, or at least real-like testing platform.
In other words, the availability, and access to real physical prototyping and
testing environments are important for the faster and better product development.
However, it is also important for the people involved, because support direct
communication with the end users, systematic product development progress fol-
low up, and creativity.
In the case of more specialized quality management systems (QMS) like ISO
13485, for quality management in medical devices [5,6,7] the provided processes
standardization clarifies the difference between some of the terms that will be
used here: monitoring, measuring, analyzing, and evaluating a process [9] (see
fig. 2):
• monitoring – of process performance through inspection or observation
and by keeping records of those observations;
• measurement – of quality and quantity;
• analysis – of trends and tendencies;
• evaluation – against criteria, able to confirm the performance or confor-
mity of the process and the output;
• improving – based on the analysis and evaluation.
Applying the plan, do, check, act discipline (PDCA cycle) is an efficient way
of implementing the improvement of processes. The aim is to develop records
of accomplishment for all process performance monitoring activities, the results
captured in terms of quality and quality, the examined trends and tendencies,
and finally the comparison results of those, against pre-defined expected process
outcomes. Those records can be used to secure the systems predictability, accu-
racy, and reliability. All those are prerequisites for sensitive systems, like medical
devices.
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Fig. 2. QMS processes based on ISO 13485.
3 Prototyping database and generic data model
In order to design a generic data model, we need to define how the mapping of the
different prototyping variables, defined on the reporting templates, will be done
into the database tables.
The most convenient for our case model is the Entity–Attribute–Value model
(EAV) [13], also known as Object–attribute–value model, vertical database mod-
el, and open schema. The model is used in similar applications for clinical trials
[14]. The difference is that it enables encoding in a more efficient way regarding
space. Entities and the respective number of attributes describing them, like prop-
erties, or parameters are many, and differ to each next experiment, device, or pro-
totype. At the same time, the amount of times that those concrete attributes will
be used is small. By using an EAV data model, we will have attribute-value pairs
as facts describing the entities, and each row will store one so described fact [15].
In the EAV data model, we have data recorded as three columns:
• entity: describing the item, or in our case the experiment, device, or pro-
totype;
• attribute: describing entity properties or parameters. The tables for the
definitions of the attributes usually contain at least: attribute ID, attribute
name, attribute description, data type, and
• value: of the concrete attribute.
In our concrete case, the EAV design will represent every single entity – ex-
periment, device or prototype, and its data as multi-rows in a single table struc-
ture. The following elements can be combined to form our data model (see fig. 3):
• form: the templates used to collect data, which map data for a set of as-
signed in it measurements for the concrete experiment, device or proto-
type;
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• version: different versions for each form, the various filled with observa-
tional and/or real-time data copies of the used forms;
• data field: the concrete variable on a form;
• variables: the concrete ones for which experiment, device or prototype
data are collected;
• variable category: the category of variables, for which experiment, device
or prototype data may be collected;
• place-keeper: the associated with a particular variable place-keeper in the
form of standard elements like input field, combo box, etc;,
• unit: representing the distinct variables;
• unit category: with which the unit variables are associated;
• prototype: each experiment, device, or prototype is related to a respective
collection of documents;
• experiment: a concrete record for an experiment, device, or prototype,
associating it with a set of related documents;
• document: the forms become documents when stored;
• value: one data field.
Fig. 3. Prototyping generic data model.
Finally yet importantly, regarding the EAV model, it is worth noting that
Resource Description Framework (RDF) is being employed as the basis of Se-
mantic-Web-related ontology work. RDF, intended to be a general method of
representing information, is a form of EAV, as an RDF triple comprises an ob-
ject, a property, and a value, even given their limitations [10]. In our case, this
is important also for further development of the semantic representation of the
prototyping results in terms of ontologies [4, 11].
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4 IUE-Lab prototyping data management system model
The following three data management tasks (see fig. 4) are usually performed in
the course of experiment, device, or prototype testing: (1) reporting templates
design, (2) observational and real-time data collection, and storage, and (3) data
analysis. Those three cover the above-mentioned QMS activities (processes) and
results (products). In the following sections, we will discuss how each of these
steps is supported by the modeled system.
Fig. 4. IUE-Lab prototyping data management tasks.
4.1 Reporting templates design
The purpose of the reporting templates design component is to support the process
of new templates construction. End users should be able to create their templates
and directly relate them to the database. The required functionality concerns:
• component reusability, using a repository with elaborated components
such as different variables,
• design automation, by using existing templates and variables,
• observational and real-time data incorporation in templates, by adding
standardized elements, and
• remote accessibility, all templates should be stored and accessed remotely.
4.2 Data collection and storage
Either Prototyping observational and real-time data are recorded by filling in
reporting form or with real-time data flow stored directly in the respective pre-
defined elements. In both cases, data is stored in the database. The data collection
process could be customized in different ways. For example, by using concrete
templates automatically loaded and displayed for concrete users, or using real-
time data that is automatically stored in predefined elements.
4.3 Data analysis
The analysis component should be open to different statistics, analytics, and other
research methods. For the analysis component, the system should use different
formats, like the ones used to store data.
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6 Conclusions
In this paper, the concept prototype and the role of prototypes in product development
were discussed. It was reported the IUE-Lab prototyping observational and real-
time data management system model, and the characteristics of the prototyping
database and the underlying generic data model. The proposed data management
model serves the needs of MIRACle project: Mechatronics, Innovation, Robotics,
Automation, Clean technologies – Establishment and development of a Center
for Competence in Mechatronics and Clean Technologies, and more specifically
for the Laboratory Intelligent Urban Environment. The design goal that the
prototyping procedure should not have to design and set up a new database
for each new experiment, device, or prototype was met. The described generic
data model is suitable for the storage of any experiment, device, or prototype
observational and real-time data the IUE-Lab is able to support the prototyping
procedure, in order to ensure the ability to support the race for new products in
the market.
6 Acknowledgment
This paper is prepared with the support of MIRACle: Mechatronics, Innova-
tion, Robotics, Automation, Clean technologies – Establishment and develop-
ment of a Center for Competence in Mechatronics and Clean Technologies –
Laboratory Intelligent Urban Environment, funded by the Operational Program
Science and Education for smart growth 2014-2020, Project BG 05M2OP001-
1.002-0011.
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