1. On the Impact of Fractal Organization on the
Performance of Socio-technical Systems
Vincenzo De Florio∗ , Hong Sun† , Jonas Buys‡ , Chris Blondia§
∗ PATS
research group
University of Antwerp & iMinds Research Institute
Middelheimlaan 1, 2020 Antwerpen, Belgium
Email: vincenzo.deflorio@uantwerpen.be
† AGFA Healthcare
100 Moutstraat, Gent, Belgium
Email: hong.sun@agfa.com
‡ PATS research group
University of Antwerp & iMinds Research Institute
Middelheimlaan 1, 2020 Antwerpen, Belgium
Email: jonas.buys@uantwerpen.be
∗ PATS research group
University of Antwerp & iMinds Research Institute
Middelheimlaan 1, 2020 Antwerpen, Belgium
Email: chris.blondia@uantwerpen.be
Abstract—Fractal organizations are a class of bio-inspired distributed hierarchical architectures in which control and feedback
information are allowed to flow independently of the position the
participating nodes have in the system hierarchy. In this paper we
discuss the adoption of a fractal organization in a class of sociotechnical systems characterized by a centralized architecture. We
present the key architectural traits of the resulting Fractal Social
Organization and put forward our conjecture that services based
on the presented solution may exhibit significant improvements,
e.g., in terms of scalability and performance. In order to provide
elements to justify our conjecture we describe how we envision the
use of the new organization in two different cases: a framework
for semantic service description-and-matching and a low-cost
telemonitoring service.
I.
I NTRODUCTION
In our past research we proposed a concept called Mutual
Assistance Community (MAC) [1], [2], [3]. In a nutshell, a
MAC is a socio-technical system coupling services provided
by assistive cyber-physical things with collaborative services
supplied by human beings into an alternative social organization for the ambient assistance of the elderly population.
Later said concept was extended into a so-called Serviceoriented Community (SoC) [4] so as to include other classes
of services—for instance crisis management and civil defense.
Both said concepts are based on similar architectural “axioms”:
•
Social actors are modeled as peer entities. No predefined classification is introduced; in particular roles
such as clients and servers or service requesters and
service providers are replaced by the simpler role of
member. Members are not locked in [5] a requester
or provider role. A member’s actual behavior is only
decided by the current context. As an example in the
domain of healthcare members may be care-givers at
a given time and care-takers at another time.
•
Semantically annotated services and requests for services are published into a service registry and trigger
semantic discovery of optimal responses [6].
•
Responses are constructed making use of the available
social resources as well as the current context knowledge with the goal of optimizing both individual and
social concerns.
A major aspect of both MAC and SoC is given by the
assumption of a “flat” society: a cloud of social resources
are organized and orchestrated under the control of a central
“hub”—a so-called service coordination center (SCC).
As common to any centralized architecture, the center of
the system is likely to become a single-point-of-failure and
a single-point-of-congestion. Evidence to the above statement
was brought by analyzing the performance of our system
under increasingly turbulent conditions [6]. In particular in the
cited reference we showed how service matching when dealing
with more than 10,000 entries implied severe performance and
scalability failures (results were obtained with a SPARQL / N3
architecture on a conventional PC).
Due to the above limiting result we set to consider alternative solutions beyond the pure centralized approach. Lessons
were learned by modeling the social activity that characterizes
flat societies of roles [7], [8]. We showed how the dynamic
evolution of the enacted social elements could be modeled as a
dynamic system governed by a simple combinatorial function.
By defining geometrical representations for said system we
could observe how the flat society gives raise to noteworthy
traits, among which the spontaneous emergence of hierarchical
structures, modularization, and self-similarity (patterns or roles
self-replicating at different scales.)

2. Inspired by the above result, in the cited references we
introduced the above traits into a novel social organization. By
construction, the new design adopts a hierarchical architecture
in which a same node—modeled as our original Serviceoriented Community—is repeated at different scale throughout
the layers of the hierarchy. A same set of rules is enacted at
each layer so as to govern inter-layer and intra-layer social
collaboration. The resulting architecture is that of a fractal
organization [9], [10], [11] that we called Fractal Social
Organization [8].
Aim of this paper is reporting on some preliminary results
and lessons learned while making use of our Fractal Social
Organizations (FSO). This is done first by recalling in Sect. II
the major characteristics of FSO. After this we consider two
ongoing experiences. In the first case, reported in Sect. III,
we focus on SSDM and provide the elements of a novel
semantic framework to manage service matching according to
the FSO principles. Preliminary experiments conducted with
computer-generated activity graphs show that the FSO may
have a significant impact on reducing the performance and
scalability limitations that we experienced with the MAC and
SoC. Section IV introduces our second experience by briefly
describing a recently started Flemish research project that aims
at the design of a low-cost, non-intrusive monitoring solution
for tele-monitoring services. Such solution shall be based on a
predefined and static fractal social organization. In particular
we report how we envisage the FSO to play a key role in
optimizing quality vs. costs dynamic trade-offs. Conclusions
and a view to some future work are finally drawn in Sect. V.
II.
F RACTAL S OCIAL O RGANIZATIONS
Fractal Social Organizations (FSO) is the name of a novel
class of socio-technical systems characterized by a distributed,
bio-inspired, hierarchical architecture [7], [8]. Though fundamentally hierarchical, FSO is not based on the classic top-down
flow of control and bottom-up flow of feedbacks (autocracy)
but rather on a more peer-to-peer approach where every node
in the hierarchy may play both management and subordinate
roles depending on the situation at hand (sociocracy). Nodes in
FSO hierarchies are in fact similar to sociocratic circles [12] or
to the members of Service-oriented Communities and Mutual
Assistance Communities [4], in that they allow control and
information to flow in any direction of the hierarchy. A fixed
set of rules (called “canon” in fractal organizations [13], [10],
[11]) regulates the spontaneous emergence and in general the
life-cycle of “social overlay networks” (SON). Said SON are
made of those nodes in the FSO hierarchy that are “electrified” [14] by the onset of some novel condition s—for instance
the awareness of a new threat or opportunity. In other words,
SON represent dynamic aggregates of entities, both physical
and computer-based, that unite to enact a collective response
to s. In what follows we shall refer to those responses as to a
SON’s “fired activities”.
As an example scenario, an elderly woman falling in her
smart house may call for the service of a detecting device—
typically an accelerometer. This triggers the creation of an
initial SON: S0 = {elderly woman, accelerometer}. The newly
created SON may deal with the fall event, e.g., through the
following fired activity: “trigger an alarm and enrol the service
of a general practitioner”. This leads to changing the initial S0
Fig. 1. Space of all sub-communities of a society consisting of 3 roles
played respectively by 1, 2, and 3 individuals. The rendering is done with the
POV-Ray raytracer [16].
into an S1 = S0 ∪ {GP}. The GP then may in turn request the
intervention of other entities, e.g., a nurse and an ambulance,
which then leads to a S2 = S1 ∪ {nurse, ambulance}. As a
result of this dynamic process and the enacting of the corresponding fired activities, SON may change their composition
and may shrink or grow in number. A formal way to represent
this process is that of a random walk through the space of all
possible social elements in the current node. Figure 1 shows
such space for a society of six nodes (for instance, six people)1 .
Enrollment is in fact the process by means of which
the above mentioned SON self-develop. It may be concisely
described as the action of locating and appointing roles to
the available cyber-physical entities. A formal description of
activities, roles, and enrollment processes is out of the scope
of this paper and may be found in [8]. Enrollment is carried
out in FSO, MAC, and SoC, via semantic service description
and matching (SSDM) as described in [6], [7]. SSDM is in fact
the “architectural cornerstone” all the socio-technical systems
our paper focuses on are built upon.
Let us refer to either SoC or MAC as to a Community. A
major difference of the FSO with respect to both Communities
is the way said enrollment process is carried out. In SoC and
MAC this is done through a central entity (the SCC) that
works as a “hub” receiving and servicing all the available
and requested services published by its members. In particular
each new submitted entry triggers a semantic match with all
those related entries that are already known to the SCC. If
a satisfactory match can be found within the Community the
activities requiring the found role can be launched. If that is
not the case the SCC just re-enters its main processing loop
and waits for a new publication.
Enrollment in the FSO takes place through inter- and
intra-layer collaboration. In the FSO we have a hierarchy of
layers each node of which is organized as in a Community
whose SCC (predefined or elected by the participating nodes)
1 Videoclips
and pictures of this and other societies may be accessed via [15].

3. Through the fractal organization of the FSO the above
mentioned limitation can be reduced, if not fully overcome,
thanks to the fact that services are not published globally
but only in the originating layer. Each layer has its own
SCC that manages only a portion of the total amount of
services published in the system. This inherent partitioning
also reduces the workload of the SCC and therefore also
the probability that it turns into a single-point-of-congestion.
Moreover the availability of multiple autonomous SCC reduces
the consequences of failures, as a failed SCC results in a
(temporary3 ) network partitioning instead of a global failure.
Fig. 2. Exemplary Fractal Social Organization. Note how the shape reproduces the well known Sierpi´ ski triangle [18].
n
represents the whole node2 . When executing the enrollment
phase in an FSO such as the one exemplified in Fig. 2 a missing
role in one node triggers a so-called “exception” [8]: the
SCC realizes that the sought role is currently unavailable and
propagates the event to the next level upward in the hierarchy.
This goes on until the first suitable candidate member for
playing the required role is found or until some “flooding
threshold” is met. This creates a sort of inter-layered, or
bi-dimensional social overlay network whose nodes are not
restricted to a single layer but can span across multiple layers
of the FSO. This rule corresponds to the Double Linking rule
of sociocracy [12] in that it allows the restrictions of pure
hierarchical organizations to be overcome. This is done by
creating a temporary means for entities situated at different
layers to cooperate by creating a new structure complementary
to the FSO and its nodes. The new structure is in fact a
new ad hoc Service-oriented Community whose objective and
lifespan are determined by the fired activity.
In the following section we shall focus on the impact that
the fractal organization of the FSO has on the performance of
SSDM in “flat” (viz., single-layered) centralized architectures,
namely our Communities.
Figure 4 shows the semantic framework that we used to
introduce the FSO concept in our MAC. As can be seen from
that picture, the Community is decomposed into a distributed
hierarchy of sub-communities whose members may also include other sub-communities. An important consequence of
this reorganization is that service requests are propagated
upward in the hierarchy only if results are not found in the
local sub-community.
SPARQL endpoints are set up for those sub-communities at
the bottom layer of the hierarchy tree, exemplified by the layer1 communities in Fig. 3. Service publications and discovery
actions is done through the SPARQL endpoints to explore the
resources in the related community.
For the sub-communities on a higher layer, a virtual
SPARQL endpoint is set up. In so doing the services published
in the sub-communities can be queried through a SPARQL
federated query. Figure 5 shows a sample federated query to
look for services published in two sub-communities. Lines
9–21 and 23–36 specify queries to two sub-communities via
their SPARQL endpoint respectively. The results from the two
specified endpoints are aggregated together by the UNION
statement in Line 22. The aggregated results are returned
with the construct statements listed in Lines 3–7. The virtual
SPARQL endpoint may also access context information external to the Communities by querying so-called Live Data [23]
SPARQL endpoints.
A. Preliminary experiments and a few remarks
In [6] we introduced the design of a mutual assistance
community in which service publication and service discovery
are executed with a SPARQL [19] endpoint. A simple service
description is exemplified in Fig 3. The SPARQL endpoint is
built with Fuseki [20], which allows services to be published
either in memory (through the in-memory graph store) or on
disk (via TDB [21]). Setting up a SPARQL endpoint with
Fuseki using in-memory graph store has several advantages;
in particular it avoids the necessity to set up a dedicated graph
store. On the other hand, the use of in-memory graph store
also places a restriction on the size of the graph that may be
managed by the single SCC of the MAC. As a consequence of
this, the amount of services that can be effectively accommodated by the endpoint is limited (as discussed in Sect.III-A).
The already mentioned Fuseki is a Jena SPARQL server
which supports a range of operations on RDF graph. Fuseki
has been used to build the SPARQL endpoint to manage the
matching services of our Communities. Services are described
as RDF graphs with N3 syntax and are managed through
the SPARQL endpoint. In order to test the performance of
the service matching algorithm we generated sets of sample activity graphs corresponding to a different number of
activities and we run those graphs on the Fuseki SPARQL
endpoint. Two different methods have been used: the inmemory data set and TDB [21] (which persists the data-set
on disk). As can be seen from Fig. 6, the in-memory method
considerably outperforms TDB. On the other hand we found
that in-memory could only be used for data sets of up to about
230,000 services (corresponding to approximately 2.8 millions
N3 triples), beyond which we consistently experience a Java
heap space error. We observe how FSO inherently results in
2 This process is called personization and is known in Actor-Network Theory
as “punctualization” [17].
3 Mechanisms such as the “mutual suspicion” algorithm in [22] may be used
to seamlessly tolerate crash failures of the SCC.
III.
F IRST C ASE : F RACTAL O RGANIZATION OF
S EMANTIC S ERVICE M ATCHING

4. Fig. 3.
Exemplary service description.
Fig. 4.
Semantic framework for a Community organized as FSO.
a graph partitioning whose blocks may be designed so as to
guarantee the adoption of the faster in-memory method.
A missed opportunity for improved performance derives
from a technological limitation. In fact in its current implementation of federated queries Fuseki executes queries sent to
remote services in sequence. As an example, in the federated
query expressed in Fig. 5, the query expressed in Lines 9–21 is
executed first while the query in Lines 23–36 is only executed
after the first query is finished. On the contrary a concurrent
execution of federated queries would enable activities to be
propagated much faster through the FSO hierarchy. In other
words constructing a virtual SPARQL endpoint to run federated queries does not allow the parallelism intrinsic in the FSO
to be properly exploited.
Fig. 5.
oriented context changes may thus be associated
to and managed in the lower layers while higher
level, human-oriented situation identification may be
appointed to the higher layers. This matches well
with modern techniques for situation identification
in pervasive computing [24] and—we conjecture—
may be used to set up cost-effective services coupling
quality-of-service and quality-of-experience design
requirements. One such service is the subject of the
following section.
Additional benefits from the introduction of the FSO may
derive from the following two properties:
1)
2)
By dividing the nodes into a set of sub-communities
representing physical entities the FSO allows domainspecific “priorities” to be introduced. In particular
resources that are (physically or logically) “closer”
to the service requester may be explored first. We
conjecture this to result in a reduction of the costs of
service delivery.
As a consequence of introducing the FSO events
and service requests are either sunk or propagated
depending on their criticality and the resources available at each layer. The FSO allows nodes and corresponding roles to be decomposed according to the
nature of the monitored events: low-level, machine-
Exemplary SPARQL federated query.
IV.
S ECOND C ASE : F RACTAL O RGANIZATION OF A
T ELEMONITORING S ERVICE
The proposed concept of FSO will be applied in the design
and implementation of the software components developed
within the scope of Little Sister, an ICON project financed by

5. Fig. 6. Performance of SPARQL endpoints with services published in memory and on disk. A Java heap space exception is experienced when data sets reach
about 230,000 services.
iMinds and the Flemish Government Agency for Innovation by
Science and Technology (IWT). The project aims to deliver a
low-cost telemonitoring [25] solution for home care. As can
be seen in Fig. 2, the system may be described as a multi-tier,
distributed systems architecture, in which specially designed
low-resolution sensors [26] and RFID readers are individually
wrapped and exposed as manageable web services. These
services are then structured within a hierarchical federation
reflecting the architectural structure of the building in which
they are deployed [27]. More specifically, the system maintains
dedicated, manageable service groups for each room in the
building, each of which contains references to the web service
endpoint of the underlying sensors (as depicted in layers 0
and 1 in Fig. 2). These “room groups” are then aggregated
into service groups representative of individual housing units.
Finally, at the highest level of the federation, all units pertaining to a specific building are again exposed as a single
resource (layer 3). All services and devices situated at layers 0–
3 are deployed and placed within the building and its housing
units; all services are exposed as manageable web services and
allow for remote reconfiguration. The system was designed to
seamlessly integrate with external applications developed and
offered by our industrial project partners (layer 4).
Information between different web services in the architecture is exchanged by means of a standardised, asynchronous
publish-and-subscribe mechanism [28]; subscriptions are automatically setup while the service group federation is initialised.
Events are raised by the sensors (proxy software) at the
lower tier, and can only “flow” upward. A dedicated software
module is available within each resource to 1) accept events,
2) verify if actuation logic is available for the event to be
dealt internally by some module contained within the resource
logic, or 3) to propagate the event to the next level. Each event
is annotated with a topic identifier when it is published, such
that the system can decide on whether to trigger local actuation
logic or propagate the event to the next tier [29].
In order to exemplify this approach, let us consider the
application of this service-oriented architecture in the context
of an elderly home. In this setting, one may reasonably
expect permanent surveillance by mean of, e.g., a warden who
interacts with the system by means of a user interface that
connects to a back-end web service hosted at layer 3. If a
fall is detected, the appurtenant software modules in the hub
deployed in that room, fed with raw data from the underlying
sensor set, will raise an event. The corresponding fired activity
calls for a warden to go and inspect the flat where the event
originated. As no such role can be found neither in the room
nor in the flat ambient, the event propagates to layer 3. Here the
warden is notified and therefore he goes to the flat to provide
the necessary assistance and get a first idea of the situation. An
inter-layered social overlay network is set in motion for as long
as it is necessary for it to deal with the fall. As the fired activity
also calls for other higher level services, e.g., an ambulance and
its driver, the event is also propagated upward until those assets
are located. The driver in particular is instructed to expect a
call from the warden within a certain time interval. The call
may for instance inform the driver that 1) his/her service is
indeed required; or 2) it is a case of a false alarm; or 3) extra
roles are necessary (e.g., a specialist in certain treatments). In
absence of a call the driver initiates his/her standard service
procedure.
We conjecture that the dynamic adaptation of the involved
social overlay networks now exemplified will play a key role in
facilitating the expression and the management of the quality
vs. costs dynamic trade-offs mandated by Little Sister.
V.
C ONCLUSIONS
The choice of the organizational structure is a key design
factor as it determines the emergence of important design
properties including, e.g., responsiveness to altered environmental conditions, timeliness, determinism, scalability, and
performance—or the lack thereof. This paper focused on a
case study—our Communities, socio-technical systems both
characterized by a “flat” and centralized organization. Several
shortcomings of these systems. were highlighted. After this
we provided a high level description of the key elements of
a second organization—the Fractal Social Organization. The
FSO constitutes a natural evolution of our Communities in
that it introduces a new, vertical “dimension”: Communities
become the nodes of a distributed, hierarchical organization.
As in sociocracy, said nodes are free to overcome the typical
flaws of the hierarchic and centralized scheme by creating
Social Overlay Networks that span across the hierarchy so as
to provide reliable and cost-effective responses to the onset of
change. Preliminary evidence of the effectiveness of FSO is
reported through two ongoing experimentations.
In the first case we argued that fractal organization may
be beneficial in the framework for semantic description and

6. matching of our Communities. In particular we showed how
dividing a big monolithic SPARQL endpoint for a flat community into a set of SPARQL endpoints responsible for a set
of sub-communities avoids single points of failure and allows
services to be queried with smaller target graphs. The reduced
size of graphs enhances maintainability and allows services to
be published through an in-memory graph store rather than on
disk. We showed how this results in considerable improvement
and conjectured that further enhancement shall be reached
when technology will allow the intrinsic parallelism of the
FSO to be exploited.
A qualitative argument is put forward in the second
case, which focuses on the design of a novel low-cost telemonitoring service that is being devised in the framework
of Flemish ICON-program project “LittleSister”. A key requirement for this project is the definition of a service combining hard safety guarantees with low cost and low energy
consumption. The fractal organization discussed in this paper
matches well with those requirements in that it allows the
monitoring and analysis processes to be partitioned according
to the level of criticality and according to the complexity of
the reflected information. Simple context changes may then be
appointed to the comparably simpler lower layers of the FSO
hierarchy while more and more complex and human-oriented
situations may be assigned to the more advanced higher layers
capable to enact complex high-order predictive behaviours as
exemplified, e.g., in [30]. In turn—we conjecture—this may
pave the way towards future effective architectures for the
optimal self-adaptive reconfiguration of system resources [31].
ACKNOWLEDGMENT
This work was partially supported by iMinds—
Interdisciplinary institute for Technology, a research institute
funded by the Flemish Government—as well as by the
Flemish Government Agency for Innovation by Science and
Technology (IWT). The iMinds LittleSister project is a project
co-funded by iMinds with project support of IWT. Companies
and organizations involved in the project are Universiteit
Antwerpen, Universiteit Gent, Vrije Universiteit Brussel,
Xetal, Christelijke Mutualiteit vzw, Niko Projects, JF Oceans
BVBA, and SBD NV.
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