A Multidimensional-Paradigm-Centered Architecture for

Cooperative Digital Ecosystems

Alfredo Cuzzocrea

and Luigi Canadè

iDEA Lab, University of Calabria, Rende, Italy

Keywords: Cooperative Digital Ecosystems, ICT, Open Source Technologies, Multidimensional Paradigms, Intelligent

Architectures.

Abstract: Cooperative Digital Ecosystems are an emerging class of information systems where the main goal is that of

supporting cooperation in human activities, driven by ICT technologies. Inspired by this main area, in this

paper we provide the anatomy and definitions of a multidimensional-paradigm-centered architecture for

supporting Cooperative Digital Ecosystems.

1 INTRODUCTION

Cooperative Digital Ecosystems (e.g., (Bakhtadze &

Suleykin, 2021; Li et al., 2012; Tsai et al., 2022)) are

an emerging class of information systems where the

main goal is that of supporting cooperation in human

activities, driven by ICT technologies. In this context,

this paper proposes a multidimensional-paradigm-

centered architecture for supporting Cooperative

Digital Ecosystems, called KnowExplo. KnowExplo

focuses on the definition of models, methodologies

and tools in the field of Open Source ICT

technologies that can be applicable to cooperative

digital ecosystems, such as data- and knowledge-

intensive environments (e.g., (Draheim et al., 2021;

Kuruppuarachchi et al., 2022; Riasanow et al.,

2021)). They play a supporting role in the production,

management and dissemination of knowledge

performed by Users, Operators and Decision Makers.

Recently, there has been a relevant interest about the

interaction of such class of systems with emerging big

data trends (e.g., (Cuzzocrea, 2009; Cuzzocrea &

Serafino, 2009; Cuzzocrea & Wang, 2007; Cuzzocrea

& Matrangolo, 2004; Cuzzocrea et al., 2003)), like

highlighted by recent studies (e.g., (Rrushi & Nelson,

2015; Schultes et al., 2022; Sheng et al., 2016)).

KnowExplo is characterized by an architecture

inspired by the multidimensional paradigm (e.g.,

(Gray et al., 1997)), as modern systems have a

https://orcid.org/0000-0002-7104-6415

https://orcid.org/0000-0002-6637-8837

multidimensional, multi-level and multi-resolution

nature. This paradigm is used by actors in an

interactive and cooperative way, followed by

multidimensional analysis of unconventional data

such as XML documents, Web pages, etc. The main

features of the KnowExplo platform concern with the

aspect of evolution of models and data schemas

which do not have a fixed and rigid structure but are

flexible on the basis of the evolution of interactions

among actors. The lifecycle of data- and function-

levels is characterized by an innovative and non-

conventional paradigm, and the level of interaction

and cooperation with processing models is

characterized a semi-structured and reconfigurable

paradigm. In this paper, we also describe the

implementation of the target architecture through a

component structure with different functionalities.

In order to describe the KnowExplo platform,

several components are introduced. First, the

Information/Knowledge Extraction component is

identified, in order to extract information and

knowledge from the primary data sources. Then, the

Ontology Modeling/Mapping component, which

creates appropriate mapping with the

multidimensional schemas of the global repository,

called MD Universal Schema (MDUS), is identified.

In particular, multidimensional modelling of the

MDUS schemas and their evolution using Data

Mining algorithms, such as clustering and frequent-

itemset-mining, over Constellations of Facts are

130

Cuzzocrea, A. and Canadè, L.

A Multidimensional-Paradigm-Centered Architecture for Cooperative Digital Ecosystems.

DOI: 10.5220/0011813800003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 130-137

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

introduced. Another goal of the actual research

proposal focuses the attention on the efficient

representation of multidimensional structures in

secondary memory, as well as querying and browsing

procedures to be implemented within the KnowExplo

platform.

With these principles in mind, one of the most

probing research challenges figured-out by the

KnowExplo platform consists in the issue of

effectively and efficiently presenting data,

information and objects from primary sources to

actors, being such data useful in cooperative and

decision-making processes. This will be also

discussed throughout the paper.

In addition to these contributions, several cloud-

computing-based verticalizations of the proposed

platform are described, namely: Taxation, Justice,

and Social Networks. Finally, the development

environment is presented through a collection of

programmable APIs and implementation of case

studies.

2 THE KnowExplo PLATFORM

The general objective of the KnowExplo platform can

be summarized in the definition of a set of models,

methodologies and tools (with relative

implementation and experimentation in the form of

case studies and significant verticalizations) that are

positioned in the context of support of Open Source

ICT technologies to digital ecosystems that are

strongly characterized by a high degree of interaction

and cooperation among the actors involved in the

stages of production, management and dissemination

of knowledge, in the dual form of data and processes.

Due to these specific characteristics, the digital

ecosystems of interest by the platform are classified

as data- and knowledge-intensive environments.

Actors of these digital ecosystems can be

classified into three large groups: Users, Operators,

Decision Makers. Users request and perform

services/functions available in the reference digital

ecosystem; Operators act as an intermediary between

the Users and the services/functions available in the

reference digital ecosystem; Decision Makers carry

out decision-support processes aimed at

modifying/restructuring the services and functions

available on the basis of the general progress of the

digital ecosystem, and the achievement of a pre-fixed

set of business objectives.

As previously mentioned, in such a scenario,

Open Source ICT technologies play an essential role

by offering, in fact, an indispensable support to all the

activities of production, management and

dissemination of knowledge carried out by the actors

of the digital ecosystem in a marked way, also

including interaction and cooperation. Significant

verticalizations of this general scenario are the

following: (i) tax management, where citizens and

governance (government companies, regions, etc.)

cooperate in the management and payment of taxes;

(ii) justice, where the actors of civil justice (judges,

clerks, etc.) cooperate in the management and

conduct of civil justice cases and trials; (iii) social

networks, where citizens and territorial authorities

(municipalities, mountain communities, etc.)

cooperate in managing life and processes of the

community.

In order to achieve the described objectives, the

KnowExplo platform provides the definition of an

advanced Open Source platform that is able to

embody both the paradigms of production,

management and dissemination of knowledge of a

markedly interactive and cooperative type, and

support paradigms for the representation and

processing of data and processes of data- and

knowledge-intensive environments. These two

different aspects play significant roles within the

KnowExplo platform, and are also characterized by a

relevant inter-relationship, as will be described

below.

In order to adequately support the application and

functional requirements required by data- and

knowledge-intensive environments, the architecture

of the KnowExplo platform, shown in Figure 1, is

strongly characterized by representation, query,

processing and delivery models for data, information

and objects of the platform’s multidimensional

primary sources. This peculiarity is dictated by the

fact that, in modern systems and applications (such as

those described by the mentioned verticalizations),

data and processes are increasingly characterized by

a multidimensional, multi-level and multi-resolution

nature, from which it follows the need for modelling,

analyzing and processing data, information and

objects that populate these systems and applications

according to multidimensional abstractions. These

multidimensional models and abstractions

implemented in the data/function layer of the

platform are used by the platform actors as part of

their interactive and cooperative knowledge

production, management and dissemination

processes, in the form of access, query, processing

A Multidimensional-Paradigm-Centered Architecture for Cooperative Digital Ecosystems

131

Figure 1: KnowExplo platform reference architecture.

and delivery of data, information and objects.

Possible applications that derive from the integration

of these functionalities of the data/functions level in

the interaction/cooperation level are in a great

number, and they range from the multidimensional

analysis of unconventional data sources (RDF

networks, XML documents, data from social

networks, etc.) to classification and multidimensional

analysis of document corpus (Web pages, etc.). These

functions range from the effective management of

complex objects coming from intermediate

computations of Data Mining processes/algorithms

(graphs, trees, etc.) to the use and multidimensional

navigation of image databases, and many others.

Figure 1 shows the reference architecture of the

KnowExplo platform.

Models, methodologies and tools for supporting

digital ecosystems with a high degree of interaction

and cooperation that are intended to be defined and

tested within the KnowExplo platform are

characterized by two fundamental aspects:

• Evolution: this aspect refers to the fact that

models and patterns of data and processes

through which the actors of the platform interact

do not adhere to a fixed and rigid structure over

time, but rather they are flexible and can vary

their structure in an adaptive way, based on the

evolution of the interactions of actors in the

interaction/cooperation level of the platform.

• Rich Life Cycle at the Data-Level/Function

Frontier – Interaction/Cooperation Level: this

aspect refers to the fact that, in the context of

their interactive and cooperative knowledge

production, management and dissemination

processes, actors of the platform give birth to a

rich lifecycle at the frontier between the

data/function layer (which is internal to the core

layer of the platform - see Figure 1) and the

interaction/cooperation level (which is external

to the core layer of the platform - see Figure 1).

This requires that, on one hand, data/functions

layer of the platform must support functionalities

of access, querying, processing and delivery of

data, information and objects of the platform

primary sources in an innovative and

unconventional manner. On the other hand, the

interaction/cooperation level of the platform

must support semi-structured and reconfigurable

processing models, according to innovative

paradigms that enhance the degree of interaction

and cooperation between the platform.

These two fundamental aspects define, in fact, the

main features of the KnowExplo platform

architecture.

3 KnowExplo ARCHITECTURE

DETAILS

As shown in Figure 1, the reference architecture of

the KnowExplo platform is characterized by a

complex component structure, such that each

component adheres to a well-established logic and

implements a particular and well-separated

functionality. To facilitate the description and

understanding of the KnowExplo platform, an

approach that maps several implementation

objectives (ORs) of the KnowExplo framework on

this platform is adopted, based on the isolation of the

different layers/levels/components that characterize

the same platform. Figure 2 shows this mapping.

As it can be argued by Figure 2, every OR is

mapped onto a specific set of (software) components

of the target KnowExplo platform. This nice amenity

allows us to achieve a pertinent separation during the

design phase as well as a solid software maintenance

at run time.

A description of the various ORs of the

framework is provided next, as referred to the

KnowExplo platform architecture (see Figure 2).

3.1 OR1 – Cooperation with

Information Producers

OR1 focuses attention on the following two main

activities/components. The first component, called

Information/Knowledge Extraction, deals with the

task of implementing information and knowledge

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Figure 2: Mapping of the framework ORs on the reference

architecture of the KnowExplo platform.

extraction functions from the primary data sources

that populate the KnowExplo platform. It is useful to

highlight that these sources can be classified into

three major classes: (i) conventional sources, such as,

for example, relational DBs, RDF bases, XML

documents, etc.; (ii) multimedia sources, such as, for

example, documents, images, etc.; (iii) complex

objects, such as, for example, trees, graphs, time

series, etc. deriving from intermediate computations

of Data Mining processes/algorithms. The second

component, called Ontology Modeling/Mapping, is

dedicated to the creation of Ontologies for modelling

the knowledge inherent to the primary data sources in

the form of networks of concepts and relationships,

and creating the appropriate mappings towards the

multidimensional schemas of the global repository

MDUS. Furthermore, to ensure effectiveness and

efficiency, the ontologies so-constructed are stored

and made persistent in a specific layer of the

KnowExplo platform.

In the KnowExplo proposal, the aggregation of

these multidimensional schemas in the MDUS

pursues an innovative interpretation according to

which such schemas that are located in the core layer

of the KnowExplo platform architecture define the

data universe of next generation applications and

systems that will be based on this platform. The idea

behind this approach consists in integrating data,

information and objects of primary sources in a global

and universal schema that offers the advantage of

being able to use the well-known multidimensional

abstractions on the sources themselves. As

highlighted above, these multidimensional

abstractions and the numerous supported applications

are then used by the KnowExplo platform actors in the

interaction/cooperation layer of the platform. Overall,

this contributes to improve the representation and

mining capabilities of the whole platform, in an

effective and pragmatical manner.

3.2 OR2 – Development of the

Methodology

OR2 is dedicated to two central aspects within the

KnowExplo platform. The first aspect concerns with

the multidimensional modelling of the MDUS

schemas, which is the primary objective of the MD

Schema Modeling/Evolution component. The second

aspect concerns with the evolution of these schemas

as a result of data- and knowledge-intensive processes

triggered by the actors in the interaction/cooperation

level of the platform. In fact, these data- and

knowledge-intensive processes, which operate on the

data/function level of the platform, can give rise to

inconsistencies such that the multidimensional

schemas of the MDUS are no longer able to capture

the (multidimensional) knowledge embedded in the

primary data sources (which feed the platform and are

processed by the platform actors). This involves in the

so-called evolution of (multidimensional) schemas,

which plays a central role in the KnowExplo platform.

The identification of inconsistencies between the

knowledge embedded in the primary sources and the

knowledge currently represented by the

multidimensional schemas of the MDUS is

implemented by the Intelligent Mining Layer

component, while the MD Schema Restructuration

component is dedicated to carry-out the necessary

restructuring of these schemas.

As regards the multidimensional modeling of the

MDUS schemas, within the KnowExplo platform the

use of the Constellations of Facts is proposed. These

latter are multidimensional logical models that have

the double positive effect of (a) capturing the

multidimensional and multi-resolution nature of

primary sources that feed the platform, also

guaranteeing the necessary flexibility that must

necessarily characterize functions exported from a

knowledge-intensive platform such as KnowExplo,

and (b) effectively and efficiently supporting the

activities of navigation, exploration and analysis of

data, information and objects of primary sources

(note that these operations/functions are then

exploited by the actors of the KnowExplo platform in

the interaction/cooperation level).

More in details, two modalities are envisaged as

related to the multidimensional modeling phase of the

MUDS schemas. The first is of a classical type, and it

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involves in a multidimensional business-oriented

modelling, according to which schemas are edited by

a knowledge worker on the basis of the knowledge of

the target application domain, and a fixed number of

business analysis objectives. The second pursues the

most innovative paradigm of automatically

discovering of multidimensional patterns directly

from the primary sources that feed the KnowExplo

platform, without the aid of the knowledge worker. It

is obvious that, at this level, the Ontologies created

within the OR1, and the related mappings, also

intervene.

As regards the evolution of the multidimensional

schemas of the MDUS, within the KnowExplo

platform the use of Data Mining algorithms (stored in

an appropriate repository - see Figure 1) is proposed,

in order to simultaneously process data, information

and objects of the primary sources that feed the

platform and the multidimensional schemes of the

MDUS in their current modeling. This with the aim

of identifying possible inconsistencies between the

(multidimensional) knowledge embedded in the

primary data sources and the knowledge currently

represented by the schemas themselves. As

mentioned earlier, this task is the main goal of the

Intelligent Mining Layer component. Among the

various alternatives of Data Mining algorithms that

can be useful for this purpose, clustering and

frequent-item set-mining are the ones that best lend

themselves to support this important functionality.

These possible inconsistencies can be of various

types. For example, a clustering algorithm could

detect that the current multidimensional partition that

characterizes a certain MDUS schema is no longer

able to effectively capture the actual clusters that can

be discovered in a certain collection of data,

information and objects of the primary sources. In this

case, multidimensional schemas must be

restructured, which is the main objective of the MD

Schema Restructuration component. The

restructuring of multidimensional schemas can take

place through a wide range of cases, which include,

among others: the discovery of a new

multidimensional entity (dimension, level,

dimensional member, aggregation, etc.), the collapse

of two or more existing multidimensional entities into

a single multidimensional entity (for example, two

dimensional members of a certain OLAP hierarchy

could merge into a single dimensional member),

adding a new OLAP dimension that was not foreseen

in the design/discovery phase of such schemas, the

addition of new aggregations that were not considered

in the design/discovery phase of such schemas, and

so forth. The latter topic is left as future work.

3.3 OR3 – Reorganization and

Production of Structures

OR3 focuses the attention on the efficient

representation in secondary memory of the

multidimensional structures stored in the MDUS.

This KnowExplo platform functionality is

implemented by the MD Schema Materialization

component. This component makes use of column-

oriented open-source database technology, in order

to ensure maximum flexibility during the

restructuring phases of the multidimensional schemas

of the MDUS. In fact, column-oriented representation

is much more flexible than traditional approaches

such as, for example, array-based (MOLAP) ones.

For example, adding a new dimensional level to a pre-

existing OLAP hierarchy corresponds, in the case of

column-oriented databases, to the simple addition of

a new column to the pre-existing ones and to the

construction of the necessary low-level references

(pointers) that implement the hierarchical

relationships between the pre-existing dimensional

levels in the reference OLAP hierarchy and the new

dimensional level to be added.

3.4 OR4 – KnowExplo Query and

Navigation

OR4 focuses attention on two different aspects of the

KnowExplo platform. The first concerns with the

definition of models, techniques and algorithms for

querying and navigating multidimensional structures

(such as data cubes and multidimensional OLAP

views) stored in the MDUS, a function to which the

MD Schema Querying/Browsing component is

dedicated. The main purpose of this component is to

support essential functionalities within the

KnowExplo platform, such as those of querying data,

information and objects of primary sources through

sophisticated multidimensional abstractions, and the

navigation of these repositories using successful

OLAP tools such as multi-level and multi-resolution

browsing, OLAP operators such as, for example, slice

& dice, pivoting, complex OLAP queries such as, for

example, similarity queries on multidimensional

metric spaces, etc.

The second problem on which OR4 focuses

concerns with the definition of models, techniques

and algorithms for the mining of the multidimensional

structures stored in the MDUS. This task is

implemented by the MD Schema Mining component.

The idea behind this component consists in making

complex functionalities for the extraction of

knowledge from multidimensional structures

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available to the actors of the KnowExplo platform. In

turn, these functionalities can be integrated by the

actors in their activities of production, management

and dissemination of the knowledge in the

interaction/cooperation level of the platform.

3.5 OR5 – Presentation and

Personalization

OR5 focuses attention on a central issue of the

KnowExplo platform, namely the presentation and

visualization of data, information and objects of

primary sources through the multidimensional

abstractions offered by the MDUS. These features are

implemented by the Presentation/Visualization

component. It should be explicitly noted that this

component is directly interfaced with the

interaction/cooperation level of the KnowExplo

platform actors, and, consequently, the greater

number of the application functionalities of the

platform are implemented and exported at this level.

In this context, the central research theme is

represented by the problem of effectively and

efficiently presenting data, information and objects of

primary sources modeled according to

multidimensional abstractions to actors who have to

elaborate them in order to support their cooperative

and decision-making processes.

There are numerous models and techniques that

are implemented by this component of the platform.

One of these concerns with the “flattening” of

multidimensional OLAP data cubes to support their

use on mobile devices with scarce computational

resources, or to support the definition of OLAP-like

interfaces for complex objects, by overcoming the

limitations of current OLAP platforms that focus only

on conventional data sources. For example, Figure 3

shows a possible OLAP-like interface for the

multidimensional and multi-resolution fruition of

images.

Figure 3: OLAP-like processing and fruition of images.

3.6 OR6 – Integrated Platform

OR6 focuses attention on the definition, development

and testing of the application architecture supporting

the KnowExplo platform, according to the Cloud

Computing paradigm. This task is implemented by

the Cloud-based Architecture component. The choice

of the Cloud Computing paradigm is motivated by the

fact that this paradigm allows considerable flexibility,

scalability and reliability. Therefore, it perfectly

adapts to the requirements of the KnowExplo

platform.

The Cloud-based architecture interfaces both to

the core of the platform and to a layer of

programmable APIs (OR7) and, above all, to the

verticalizations to be built on the basic framework

offered by the KnowExplo platform (OR8, OR9,

OR10). OR6 therefore provides not only the

definition, development and testing of the core

architecture of the KnowExplo platform, but also of

its interfaces to the layers and external components,

according to well-understood software patterns.

As highlighted above, verticalizations play a key

role within the KnowExplo platform. In greater

details, the verticalizations are built directly on the

Cloud-based architecture of the KnowExplo platform

and are concerned with complete instances of the

general platform in which the general purposes

components of the platform are “rewritten” through

specialized functionalities and procedures on the

basis of the particular application case considered by

the current verticalization.

Platform verticalizations, which are essentially

based on Cloud Computing software architectures,

and cover real-life instances coming from modern

settings, are presented in the next Sections.

3.7 OR7 – Development Environment

OR7 is dedicated to the definition, implementation

and testing of development environments that

interface with the functionalities offered by the

KnowExplo platform through a collection of

programmable APIs and support the construction of

data- and knowledge-intensive applications that

adhere to the paradigms encapsulated in the platform

(multidimensionality, cooperation, intelligent use,

etc.). Development & Programming Environments is

the component that implements this layer of the

reference architecture of the KnowExplo platform.

This component offers both the API collection

and development environments which, again, adhere

to KnowExplo’s paradigms. Thanks to this

component, the platform becomes “programmable”

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and completely open, so it can be integrated with

other next-generation platforms, systems and

applications, as well as interfaced with traditional

legacy systems.

A secondary but no less important activity of OR7

is represented by the definition and implementation

of a series of case studies which, on the one hand,

show the operational functionality of applications

based on the KnowExplo APIs and, on the other hand,

serve to test the effectiveness and efficiency of

applications built on the KnowExplo platform. It

should be noted that these topics are also relevant for

what regards the issue of scalability of big data

processing, which is an hot-topic of high interest at

now.

3.8 OR8 – Verticalization 1: Taxation

This verticalization (implemented by the Fiscal

Management component) focuses on the

management and payment of taxes through the

interaction and cooperation of actors such as citizens,

government companies, regions, etc.

3.9 OR9 – Verticalization 2: Justice

This verticalization (implemented by the Justice

component) focuses on the management and conduct

of civil justice cases and processes through the

interaction and cooperation of actors such as judges,

clerks, law enforcement agencies, etc.

3.10 OR10 – Verticalization 3: Social

Networks

This verticalization (implemented by the Social

Networks component) focuses on the management of

civic life of territorial areas such as metropolitan

areas, municipalities, mountain communities, etc.,

through the interaction and cooperation of actors such

as citizens, territorial authorities, politicians, etc.

4 RELATED WORK

By inspecting actual literature, there exist several

research proposals that are relevant to our research.

This further corroborates our feeling about the

relevance of the investigated research field. In the

following, we overview the most noticeable ones.

(Alam et al., 2017) presents a digital twin

architecture reference model for the cloud-based

cyber-physical system, C2PS, where the key

properties of the C2PS are analytically described. The

model helps in identifying various degrees of basic

and hybrid computation-interaction modes in this

paradigm. The C2PS smart interaction controller has

been designed using a Bayesian belief network, so

that the system dynamically considers current

contexts. The composition of fuzzy rule base with the

Bayes network further enables the system with

reconfiguration capability. Finally, authors present a

telematics-based prototype driving assistance

application for the vehicular domain of C2PS, VCPS,

to demonstrate the efficacy of the architecture

reference model.

(Vedeshin et al., 2019) highlights the advent of

personal manufacturing, where home users, small,

medium, and Fortune 500 enterprises use devices

such as 3D printers, CNC mills, and robotics to

manufacture products locally. Authors propose a

digital ecosystem of personal manufacturing, which

is currently used or being tried by 111 Fortune 2000

enterprises. In this paper, they focus on the creation

of the cloud-based manufacturing operating system,

3DPrinterOS, to address an evolving critical problem

of personal manufacturing. Therefore, authors

introduce a novel software ecosystem architecture to

sustain a massive communication load of command,

control, and telemetry data to and from millions of

manufacturing machines and users. This solution

allows users to create and deploy their own

applications into 3DPrinterOS cloud operating system.

To guide the architecture design process in the

context of digital twins, (Tekinerdogan & Verdouw,

2020) provides a pattern-oriented approach for

architecting digital twin-based systems. Authors

propose a catalog of digital twin architecture design

patterns that can be reused in the broad context of

systems engineering. The patterns support the various

phases in the systems engineering life cycle process,

and are described using a well-defined pattern docu-

menttation template. For illustrating the application of

digital twin patterns, a multi-case study approach in the

agriculture and food domain is adopted.

Finally, (Yun et al., 2022) proposes a digital twin

architecture to provide accurate disaster prediction

services with a similarity-based hybrid modeling

scheme. The hybrid modeling scheme creates a

hybrid disaster model that compensates for the errors

of physics-based prediction results with a data-driven

error correction model to enhance the prediction

accuracy. The similarity-based hybrid modeling

scheme reduces errors from the data dependency of

the hybrid model by constructing a training dataset

using similarity assessments between the target

disaster and the historical disasters.

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5 CONCLUSIONS AND FUTURE

WORK

This paper has focused the attention on innovative

Cooperative Digital Ecosystems, an emerging class of

systems where the cooperation among different actors

is the main aspect to be considered. In order to

support the main underlying process, we have

introduced and described in details KnowExplo, a

multidimensional-paradigm-centered architecture

for supporting these systems. Future work is mainly

oriented towards equipping the proposed architecture

with emerging big data trends (e.g., (Campan et al.,

2017; Cuzzocrea et al., 2014; Li et al., 2022)).

REFERENCES

Alam, K. M., & El Saddik, A. (2017). C2PS: A Digital

Twin Architecture Reference Model for the Cloud-

Based Cyber-Physical Systems. IEEE Access 5, pp.

2050-2062.

Bakhtadze, N., & Suleykin, A. (2021). Industrial Digital

Ecosystems: Predictive Models and Architecture

Development Issues. Annual Reviews in Control 51, pp.

56-64.

Campan, A., Cuzzocrea, A., & Truta, T. M. (2017).

Fighting Fake News Spread in Online Social Networks:

Actual Trends and Future Research Directions. In:

IEEE International Conference on Big Data, pp. 4453-

4457.

Cuzzocrea, A. (2009). CAMS: OLAPing Multidimensional

Data Streams Efficiently. In: International Conference

on Data Warehousing and Knowledge Discovery, pp.

48-62.

Cuzzocrea, A., Furfaro, F., & Saccà, D. (2003). Hand-olap:

A System for Delivering Olap Services on Handheld

Devices. In: 6th International Symposium on

Autonomous Decentralized Systems, pp. 80-87.

Cuzzocrea, A., Leung, C. K. S., & MacKinnon, R. K.

(2014). Mining Constrained Frequent Itemsets from

Distributed Uncertain Data. Future Generation

Computer Systems 37, pp. 117-126.

Cuzzocrea, A., & Matrangolo, U. (2004). Analytical

Synopses for Approximate Query Answering in OLAP

Environments. In: International Conference on

Database and Expert Systems Applications, pp. 359-

370.

Cuzzocrea, A., & Serafino, P. (2009). LCS-Hist: taming

Massive High-dimensional Data Cube Compression.

In: 12th International Conference on Extending

Database Technology: Advances in Database

Technology, pp. 768-779.

Cuzzocrea, A., & Wang, W. (2007). Approximate Range–

sum Query Answering on Data Cubes with

Probabilistic Guarantees. Journal of Intelligent

Information Systems 28(2), pp. 161-197.

Draheim, D., Krimmer, R., & Tammet, T. (2021). On State-

Level Architecture of Digital Government Ecosystems:

From ICT-Driven to Data-Centric. In Transactions on

Large-Scale Data-and Knowledge-Centered Systems

XLVIII, pp. 165-195.

Gray, J., Chaudhuri, S., Bosworth, A., Layman, A.,

Reichart, D., Venkatrao, M., Pellow, F., & Pirahesh, H.

(1997). Data Cube: A Relational Aggregation Operator

Generalizing Group-by, Cross-Tab, and Sub Totals.

Data Mining and Knowledge Discovery 1(1), pp. 29-53.

Kuruppuarachchi, P., Rea, S., & McGibney, A. (2022). An

Architecture for Composite Digital Twin Enabling

Collaborative Digital Ecosystems. In: 25th IEEE

International Conference on Computer Supported

Cooperative Work in Design, pp. 980-985.

Li, W., Badr, Y., & Biennier, F. (2012). Digital

Ecosystems: Challenges and Prospects. In: 2012

International Conference on Management of Emergent

Digital EcoSystems, pp. 117-122.

Li, X., Liu, H., Wang, W., Zheng, Y., Lv, H., & Lv, Z.

(2022). Big Data Analysis of the Internet of Things in

the Digital Twins of Smart City Based on Deep

Learning. Future Generation Computer Systems 128,

pp. 167-177.

Riasanow, T., Jäntgen, L., Hermes, S., Böhm, M., &

Krcmar, H. (2021). Core, Intertwined, and Ecosystem-

Specific Clusters in Platform Ecosystems: Analyzing

Similarities in the Digital Transformation of the

Automotive, Blockchain, Financial, Insurance and IIot

Industry. Electronic Markets 31(1), pp. 89-104.

Rrushi, J., & Nelson, P. A. (2015). Big Data Computing for

Digital Forensics on Industrial Control Systems. In:

IEEE International Conference on Information Reuse

and Integration, pp. 593-608.

Schultes, E., Roos, M., da Silva Santos, L. O. B., Guizzardi,

G., Bouwman, J., Hankemeier, T., Baak, A., & Mons,

B. (2022). FAIR Digital Twins for Data-Intensive

Research. Frontiers in Big Data 5, art. 883341.

Sheng, G., Zhao, X., Zhang, H., Lv, Z., & Song, H. (2016).

Mathematical Models for Simulating Coded Digital

Communication: A Comprehensive Tutorial by Big

Data Analytics in Cyber-Physical Systems. IEEE

Access 4, pp. 9018-9026.

Tekinerdogan, B., & Verdouw, C. (2020). Systems

Architecture Design Pattern Catalog for Developing

Digital Twins. Sensors 20(18), art. 5103.

Tsai, C. H., Zdravkovic, J., & Stirna, J. (2022). Modeling

Digital Business Ecosystems: A Systematic Literature

Review. Complex Systems Informatics and Modeling

Quarterly 30, pp. 1-30.

Vedeshin, A., Dogru, J. M. U., Liiv, I., Draheim, D., & Ben

Yahia, S. (2019). A Digital Ecosystem for Personal

Manufacturing: An Architecture for Cloud-based

Distributed Manufacturing Operating Systems. In: 11th

International Conference on Management of Digital

EcoSystems, pp. 224-228.

Yun, S. J., Kwon, J. W., & Kim, W. T. (2022). A Novel

Digital Twin Architecture with Similarity-Based

Hybrid Modeling for Supporting Dependable Disaster

Management Systems. Sensors 22(13), art. 4774.

A Multidimensional-Paradigm-Centered Architecture for Cooperative Digital Ecosystems

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