=Paper=
{{Paper
|id=Vol-1357/paper5
|storemode=property
|title=Open Service Network Analysis
|pdfUrl=https://ceur-ws.org/Vol-1357/paper5.pdf
|volume=Vol-1357
|dblpUrl=https://dblp.org/rec/conf/websci/CardosoMBHSM13
}}
==Open Service Network Analysis==
https://ceur-ws.org/Vol-1357/paper5.pdf
Open Service Network Analysis
Jorge Cardoso1,2 , John A. Miller3 , Casey Bowman3 , Christian Haas2 , Amit P.
Sheth4 , and Tom W. Miller5
1
CISUC/Dept. Informatics Engineering
University of Coimbra, Coimbra, Portugal
jcardoso@dei.uc.pt
2
Karlsruhe Service Research Institute
Karlsruhe Institute of Technology, Karlsruhe, Germany
jorge.cardoso@kit.edu, christian.haas2@kit.edu
3
Dept. of Computer Science
University of Georgia, Athens, Georgia, USA
jam@uga.edu, bowman99@uga.edu
4
Kno.e.sis Center
Wright State University, Dayton, Ohio, USA
amit@knoesis.org
5
Dept. of Economics, Finance & Quantitative Analysis
Kennesaw State University, Georgia, USA
tmiller@kennesaw.edu
Abstract. Understanding how services operate as part of large scale
global networks, the related risks and gains of different network struc-
tures and their dynamics is becoming increasingly critical for society.
Our vision and research agenda focuses on the particularly challenging
task of building, analyzing, and reasoning about global service networks.
This paper explains how Service Network Analysis (SNA) can be used
to study and optimize the provisioning of complex services modeled as
Open Semantic Service Networks (OSSN), a computer-understandable
digital structure which represents connected and dependent services.
1 Introduction
Services that offer different capabilities are also distributed over space and time.
These services are related, dependent, connected, and form networks. They are
brought together as new services through composition or aggregation. In many
situations, the value of services and networks is influenced by existing suppliers,
competitors, value-adders and customers. It is important to be able to under-
stand and reason about these networks to assist, e.g., decision-making involving
strategic investments in service innovation. However, this is hard because of their
scale (number of services a network may have) and the complexity of technical
aspects such as the temporal and spatial distribution, and the business aspects
such as the diversity of marketing, operations, business models and financial
aspects.
45
We resort to Service Network Analysis (SNA) that offers a systematic and sci-
entific analysis of service networks to address the above challenges. SNA views
service systems and service relationships [4] in terms of network theory, con-
sisting of nodes (representing individual services within the network) and ties
(which represent relationships between services such as roles, level of integration,
involvement strength, and cause-effect [4] bindings).
The dynamic nature of service networks indicates that their topology might
be shaped according to some intrinsic property, e.g. service cost, availability,
or extrinsic property, e.g. perceived customer preference. This dynamic behav-
ior has been verified in many fields. For example, Web Science looks at World
Wide Web models as a large directed graph with an apparent random character.
Nonetheless, the topology of this graph has evolved to a scale-free network [20] by
preferential attachment [22], i.e. when establishing hyperlinks, documents prefer
the ’popularity’ of certain documents (of ’popular sites’) which overtime be-
come hubs. In the same vein, SNA targets to find explanations for the structure
and behavior of service networks. Finding the mechanisms, laws, and properties
of service networks can enable to better understand and explain the structure,
evolution, cost, reliability, and coverage of service networks.
Having provided a motivation for the importance of SNA, the contribution of
this paper is to aggregate and present four approaches originating from different
fields that can be applied to analyse service networks:
1. Optimization,
2. Evolutionary analysis,
3. Cooperative analysis and
4. Value analysis.
While these approaches have been developed in isolation and have been often
applied to distinct fields (e.g. complex systems, logistics, economics, and mar-
kets), they all constitute solutions for SNA. In the future, a convergence of these
approaches is needed to build a comprehensive body of scientific methods for
service network analysis. A second contribution of this paper is to describe how,
from a technical perspective, these service networks can be build. We relied
on the recent developments of the Linked USDL (http://linked-usdl.org)
language to represent services and a service relationship model OSSR [4] to rep-
resent relationships between services of a network. An important aspects of these
two models is that their encoding is based on Linked Data principles to retain
simplicity for computation, reuse existing vocabularies to maximize compatibil-
ity, and provide a simple – yet effective – means for publishing and interlinking
distributed service descriptions for automated computer analysis. These are fun-
damental aspects to support the creating of global service networks.
In this paper, we explore SNA as introduced above and offer some prelim-
inary concepts, models and insights. Section 2 introduces the main terms and
concepts that will be used throughout the paper. In Section 3 we provide a
motivating scenario, from the field of cloud computing, to highlight the impor-
tance of constructing complex services by aggregating simpler building blocks
46
into service networks. Section 4 explains how Open Semantic Service Networks
(OSSN)[7] can be constructed by accessing remote and distributed computer-
understandable service descriptions. In Section 5, we present the field of service
network analysis and exemplify the objective of this new field of research. Sec-
tion 6 presents the related work in this area. Section 7 offers our conclusion on the
extraordinary implications and improvements to society that the construction
of global service networks and there analysis can bring.
2 Terms and Concepts
To address the growing importance of connecting service systems, we have in-
troduced the concepts that constitute an OSSN [7]. A service network is defined
as a graph structure composed of service systems which are nodes connected by
one or more specific types of service relationship, the edges. A service system
is a functional unit with a boundary through which interactions occur with the
environment, and, especially, with other service systems. Service networks are
similar to social networks in their structure but connect service systems. When
no ambiguity arises, we will simply use the term service to refer to a service
system.
OSSNs are global service networks which relate services with the assump-
tion that firms make the information of their service systems openly available
using suitable models. Therefore, service systems, relationships, and networks
are said to be open when their models are transparently available and acces-
sible by external entities and follow an open-world assumption. The objective
of open services is very similar to the one explored by the linked data initiative
(http://linkeddata.org): exposing, sharing, and connecting pieces of data and
information on the Semantic Web using URIs and RDF. Networks are said to
be semantic since service and relationships can be represented by using shared
models, common vocabularies, and semantic Web theories and technologies. Ser-
vice networks bring together several players (e.g. service creators, aggregators,
providers, marketplaces, and consumers) that work together to deliver value to
consumers.
The (re)construction of OSSNs is the result of a peer-to-peer social process.
Firms, groups and individuals (i.e. the community) are equal participants which
freely cooperate to provide information on services and their relationships to
ultimately create a unique global, large-scale service network. The principles
of OSSN (re)construction were presented in our previous work [7] and call for
self-governance, openness, free-access, autonomy, distribution, and decoupling.
3 Motivation Scenario
Our scenario is from the field of cloud computing. As cloud applications spread,
such as platform-as-a-service (PaaS) and software-as-a-service (SaaS), the depen-
dencies between applications increases. For example, Heroku, Instagram, Pinter-
est and Netflix all establish a relationships with Amazon EC2 since they depen-
47
dent on its services. This has many implications e.g., a change in Amazon EC2
characteristics (e.g. its cost, reliability, and performance) can influence all the
dependent services.
Fig. 1. A service network involving various cloud computing services
Let us consider the service network from Fig. 1. Two service aggregators (SA1
and SA2 ), part of the ACME corporation, decided to construct two new services:
1
ACME Customer Relationship Management (SCRM ) and ACME Business Intel-
2
ligence (SBI ). These two services where constructed from the aggregation of an
existing cloud processing service provided by Heroku6 (SHer 3
) and a storage ser-
7 4 1
vice provided by Amazon Elastic Block Store (SAma ). In addition, SCRM also
8 5
relied on the SugarCRM service (SSug ), an open-source, web-based customer
2
relationship management SaaS platform. On the other hand, SBI relied on the
9 6
BIME service (SBim ), a SaaS solution for business analytics and data visualiza-
tion. The relationships [4] between services (i.e. R1 and R2 ) can be summarized
as follows (x ← R(y1 , y2 , ..., yi ) is to be read as x aggregates y1 , y2 , . . . , yi ):
1 3 4 5
– SCRM ← R1 ( SHer , SAma , SSug )
2 3 4 6
– SBI ← R2 ( SHer , SAma , SBim )
1 2
The two new services, i.e. SCRM and SBI , are commercialized by service
3 4 5 6
provider SP5 and SP6 . The atomic services SHer , SAma , SSug , and SBim are
provided by SP1 , SP2 , SP3 , and SP4 , respectively. Service consumers (Ci ) can
6
http://www.heroku.com/
7
http://aws.amazon.com/ebs/
8
http://www.sugarcrm.com/
9
http://www.bimeanalytics.com/
48
purchase and use any of the aggregated services. To operate, aggregated services
must purchase computing/processing units from atomic services. In other words,
1 3 1
there is a dependency between, e.g., SCRM and SHer . Furthermore, SCRM has
10 2
a service complementor osCommerce CP1 and SBI has a service competitor
SAS Visual Analytics CO1 .
SNA can be used to, e.g., minimize the cost of providing the aggregated ser-
1
vice SCRM . The study of the network can suggest the use of other data storage
3 4
and processing services which are less expansive than SHer and SAma , possibly
with a slightly lower reliability. Minimizing the cost of a service network can
be mapped to an assignment problem which in turn is mapped to fundamental
combinatorial optimization problems. This branch of service network analysis
will be explored in section 5.1. The effect of having a higher number of comple-
mentors CPi and a lower number of competitors COi can also be studied since
1 2
it influences the perceived value of services SCRM and SBI . In this case, findings
from the filed of social network analysis can be used to explore the influence of
actors in a network.
4 Constructing an OSSN
The service network from the previous section can be constructed and repre-
sented using OSSN by accessing, retrieving and combining information from
service and relationship models. OSSN are networks which relate services with
the assumption that firms make the information of their services openly avail-
able using suitable models. In other words, we make the assumption that the
1 2 3 4 5 6
description of services SCRM , SBI , SHer , SAma , SSug , and SBim is openly and
remotely available.
4.1 Open Service Descriptions
In OSSN, services are modeled using the family of languages named *-USDL (the
Unified Service Description Language)[5,8] to provide computer-understandable
descriptions for services. USDL relies on a shared vocabulary for the creation of
service models and includes concepts such as pricing, service level, availability,
and roles. These languages11,12,13 allow to formalize business services and service
systems in such a way that they can be used effectively for dynamic service
outsourcing, efficient SaaS trading, and automatic service contract negotiation.
As an example of service description modeling, we illustrate how the service
5
SugarCRM SSug from Section 3 was modeled using Linked USDL. The infor-
mation used to model the service was retrieved from its web site. A service and
a vocabulary model were created. The vocabulary contained domain dependent
10
http://www.oscommerce.com/ is a vendor specialized in customizable web shops
platforms
11
Linked-USDL = http://linked-usdl.org/
12
α-USDL = http://www.genssiz.org/research/service-modeling/alpha-usdl/
13
USDL = http://www.w3.org/2005/Incubator/usdl/
49
concepts from the field of CRM systems (e.g., taxonomies of common instal-
lation options). Since Linked USDL only provides a generic service description
language, domain specific knowledge needs to be added to further enrich the
description of services. The excerpt from Listing 1.1 illustrates the description
of the SugarCRM service (the example was written using the Turtle language14 ).
1 <#service_SugarCRM> a usdl:Service ;
2 ...
3 dcterms:title "SugarCRM service instance"@en ;
4 usdl:hasProvider :provider_SugarCRM_Inc ;
5 usdl:hasLegalCondition :legal_SugarCRM ;
6 gr:qualitativeProductOrServiceProperty
7 crm:On_premise_or_cloud_deployment ,
8 crm:Scheduled_data_backups ,
9 crm:Social_media_integration ,
10 crm:Mobile_device_accessibility .
11 ...
12 :offering_SugarCRM a usdl:ServiceOffering ;
13 ...
14 usdl:includes <#service_SugarCRM> ;
15 usdl:hasPricePlan
16 :pricing_SugarCRM_Professional ,
17 :pricing_SugarCRM_Corporate ,
18 :pricing_SugarCRM_Enterprise ,
19 :pricing_SugarCRM_Ultimate ;
20 usdl:hasServiceLevelProfile :slp_SugarCRM .
21 ...
Listing 1.1. SugarCRM service modeled with Linked USDL
The description starts with the identification of the provider (line 4), the legal
usage conditions (line 5), the general properties of the service (e.g., deployment,
schedules backups, integration, and mobile accessibility), and its price plans (line
15).
4.2 Open Service Relationships
Service networks rely on a fundamental element which connects service systems:
relationships. The Open Semantic Service Relationship (OSSR) model [4] is used
to capture the dependencies that exist between aggregated SaaS and atomic
SaaS applications (i.e. services which do not depend on other services). The
model considers that service systems are represented with existing description
languages, such as Linked USDL, and derives a rich, multi-level relationship
model. Service relationships are very different from the temporal and control-
flow relations found in business process models. They relate service systems
accounting for various perspectives such as roles, associations, dependencies,
and comparisons.
14
Turtle – Terse RDF Triple Language, see http://www.w3.org/TR/turtle/
50
4.3 Service Network Construction
An OSSN network is modelled as a time-varying hypergraph SN , such as SN (t) =
{S(t), C(t), R(t)}, where S(t) is the set of services provided and C(t) is the set
of service consumers, with both being modeled with USDL. R(t) is the set of
relationships modeled with OSSR connecting consumers and services provided.
For example, binary relationships can be depicted as edges in a directed graph
(see section 5). Time is represented by the parameter t, whose granularity is set
to appropriately model the market (e.g., days). Consumers alter the topology of
a service network by diffusion when they adopt or abandon a service by adding
or deleting an OSSR relationship to it.
To construct a service network SN , USDL and OSSR models are remotely
accessed and retrieved (an overview description of the infrastructure to access
and retrieve USDL and OSSR instances is described in [7]). OSSR models are
mapped to relationship R(t).
5 Service Network Analysis
A wide spectrum of techniques and algorithms can be developed to study OSSNs.
For example, reasoning techniques can be developed to explore the notion of re-
lationships as bonds. By discovering strong cliques, we can hypothesize that
the stronger the relationships, the stronger the unification and the greater the
commonality of fates. As a result, it would be possible to infer that a tightly
coupled service network will sink or swim together. Other fundamental algo-
rithms which are valuable to implement come from the field of network science.
For example, algorithms to detect if an OSSN is a scale-free networks [15]. This
property strongly correlates with the robustness of a network to failures. This
can prove to be important in financial markets. As another example, the pref-
erential attachment [22] can be explored to forecast the structural evolution of
service networks.
We present four methods under the umbrella of SNA: 1) OSSN optimization,
2) evolutionary analysis of OSSN, and 3) cooperative analysis of OSSN, and 4)
value analysis. Fig. 2 conceptualizes and contextualizes these methods with ser-
vice networks. Optimization deals with constructing networks by selecting the
best services, with regard to some criteria, from some set of available alterna-
tives. Evolutionary and cooperative analysis deals with the study of networks’
structures as a function of time. In other words, how do networks expend, adapt,
or collapse overtime based on internal and external factors. Finally, service value
network analysis deals with the establishment of rules and regulations which en-
sure that the construction of networks in society follows a fair and unbiased
processes.
These four methods enable to study different aspects of a service network
and, therefore, they do not compete between them. They provide different views
on a network. For example, optimization (the first method described in the next
section) can be used to selected the most desirable combination of services to
51
Fig. 2. Overview of service network analysis methods
achieved an initial goal. On the other hand, cooperative analysis studies how the
behaviour of customers (e.g. a new service subscription or a change of the service
provider) and the characteristics of the service offered (e.g. its price) influence
the flows of material and/or immaterial resources (e.g. raw materials) inside a
service network.
5.1 OSSN Optimization
SN = (N, E, ln , le ), is a node and edge labeled directed graph (initially we are
modeling acyclic graphs) where
N = S ∪ C
E = S × S ∪ S × C
ln : N → Description
le : E → Color
The nodes N are either services S or consumers C.
The consumer nodes are sinks (no outgoing edges).
Certain services are atomic (i.e., not formed as compositions of simpler ser-
vices).
In our initial modeling we consider them to be sources (i.e., no incoming
edges).
There will be edges from services to other services as well as consumers.
To support constructing optimal constructions of OSSNs at particular time
points, we model a service network as follows: SN = (N, E, ln , le ) is a node and
52
edge labeled directed graph (initially we are modeling acyclic graphs) where
N = S ∪ C
E = S × S ∪ S × C
ln : N → Description
le : E → Color
The nodes N are either services S or consumers C. The consumer nodes are sinks
(no outgoing edges). Certain services are atomic (i.e., not formed as compositions
of simpler services). In our initial modeling we consider them to be sources (i.e.,
no incoming edges). There will be edges from services to other services as well
as consumers.
The node labels indicate the semantics of services or the demands of cus-
tomers. The edge labels are abstract representations of type. A service, in gen-
eral, takes one or more input types (colors) and produces one or more output
types (colors). Initially, type matching is simple, but we plan to generalize it
to model subsumption hierarchies. The color of an edge will be set to the most
general of the two matching colors (i.e. the more general type). If there is no
match, there will be no edge. Below, an algorithm will be outlined for maximal
service network construction. This network will then be trimmed with its flow
quantified by using mathematical optimization techniques.
The optimal construction of a service network is divided into two phases:
maximal color-compliant service network construction and cost minimization.
The algorithm for phase I builds a service network from three sets of nodes,
atomic services (sources), composite services (intermediate nodes), and con-
sumers (sinks). Starting with the sources, all intermediate and consumer nodes
are connected by edges that are color compliant, e.g., if an intermediate node
needs a blue input and green input and there exist sources producing/outputting
these colors, then this intermediate node is added to the graph. This process con-
tinues through k stages, a parameter indicating the maximum number of stages
(i.e., distance from source to sink) desired.
Once the graph has been created, it can be reduced to an optimal form
using Linear Programming. Such problems require an objective function in terms
of decision variables, and constraints on the values of those decision variables.
Our objective function is the cost of the network, and our decision variables
represent the flow of material through the network and the amount of production
at intermediate nodes. Flow is determined by the edges and intermediate nodes,
so each of these will have a decision variable. The flow is constrained by the
supply, production, or demand capacity of the nodes in the network, and by
the required number of inputs for each non-source node. Once the constraints
are gathered, a Linear Programming algorithm such as the Simplex algorithm,
can be used to find the optimal values for the decision variables. These values
determine the optimal amount of flow through the network and the value of the
objective function estimates the minimum cost.
53
5.2 Evolutionary Analysis of OSSN
In our second example, let us assume that each service system contains a value
proposition communicated to customers (i.e, the attractiveness elements or pref-
erential attachment). Service value is judged from the perspective of consumers
as they compare services among the alternatives. For simplicity reasons, we as-
sume that the value proposition is similar for all service systems and it is the
price of the services calculated from a usdl-price:PricePlan15 . This concept
if part of the Linked USDL family.
Since our objective is to forecast the evolution of a service network over time,
we use the following function to calculate the Market Share of each service pro-
vided M S(si ) = degree(si )/m; where degree(si ) is the number of relationships
established by service si with service consumers and m is the total number of
relationships established between providers and consumers. Overtime, customers
change preferences by changing from one service system to another service sys-
tem provider.
100% 100%
Forecast
75% 75%
Market share
50% 50%
Market share
25% 25%
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
Time (t) Time
Fig. 3. Service market share evolution overtime [6]
Let us assume that the (re)constructed SN topology shows that overtime
the market share is the one represented in Fig. 3 at t = 3. One question to be
answered is: ”what will happen to the market in the future if the conditions are
not changed?” (i.e. the value propositions of si remain the same and m ≯ ci ).
According to Bass model [2], the leading service system will reach a fixedpoint
market share according to the following formula (a and b are constants):
1 − e−bt
M S(si , t) = ;0 ≤ t ≤ 9 (1)
1 + ae−bt
15
For simplicity reasons, we consider that each service has only one pricing plan.
54
Fig. 3 illustrates that from the four services provided, three also rise in market
share during the early stages, reach a peak, and then decline as the service leader
accelerates because of the increasing returns effect of preferential attachment. In
this case, all but one service provided leaves the market, leaving one monopoly
competitor. The SNA field can, therefore, develop mathematical models which
can help to forecast the evolution of service network overtime.
5.3 Cooperative Analysis of OSSN
In our third example, we explored the suitability of OSSNs to model system
dynamics. Instead of looking at causes and their effects in isolation, we analyze
service networks as systems made up of interacting parts. Once an OSSN is
created from distributed service models, cause-effect or network effect diagrams
can be derived for the network. For example, Fig. 4 shows service systems Si ,
Sj , Sk , and directed edges illustrating internal and external relationships.
Service system Si
+
Si KPI = Sk KPI =
+ Si KPI = Net gains Resource Limit
# services
+
+ -
+ +
- KPI Gain per Service
Total Services Individual system Sk
+ Service
-
+ Si
+
Sj KPI = +
Sj KPI = Net gains
# services
+ Time
Service system Sj
a) b)
Fig. 4. Service networks and system dynamics [6]
Causal relationships connect Key Performance Indicators (KPI) from dif-
ferent services’ and within services. The pattern represented by this OSSN is
commonly known as the ’Tragedy of the Commons’ archetype. It hypothesizes
that if the two services Si and Sj overuse the common/shared service Sk , it will
become overloaded or depleted and all the providers will experience diminishing
benefits. Service Si and Sj provide services to costumers. To increase net gains,
both providers increase the availability of service instances. As the number of
instances increases, the margin decreases and there is the need to increase even
55
more the number of instances available. As the number of instances increases,
the stress on the availability of service Sk is so strong that the service collapses
or cannot respond anymore as needed. At that point, service Si and Sj can no
longer fully operate and the net gain is dramatically reduced for all the parties
involved as shown in Fig. 4.b).
5.4 Service Value Networks
While the previous three sections considered structural aspects, such as the anal-
ysis, optimization and evolution of the network, the described methods do not
take into account the participants’ behavior in a service network. For example,
depending on the market mechanism implemented in the service marketplace,
providers might have an incentive to report their service characteristics (such
as price, non-functional attributes, etc.) untruthfully to the system in order to
increase their chance of being allocated. Hence, the market mechanism used in
the marketplace has to be able to handle such behavior and still yield an effi-
cient market outcome. This is the focus of the economic concept of Service Value
Networks (SVNs, see [3,14,13]). SVN are defined as “Smart Business Networks
that provide business value by performing automated on-demand composition
of complex services from a steady but open pool of complementary as well as
substitutive standardized service modules through a universally accessible net-
work orchestration platform” [14]. The setup is very similar to Fig. 1. The main
constituents of SVNs are service providers which offer atomic or complex ser-
vices, service aggregators that perform the (automatic) composition of (atomic)
services to complex service compositions, and service consumers. The services
are described by a number of attributes such as availability, throughput, latency,
and price. Consumers request either atomic services or aggregated services with
certain functionalities, and have preferences over the service attributes (e.g. an
acceptable price range, availability thresholds, etc.).
Fig. 5. Service Value Network and combination of atomic services to aggregated ser-
vices
56
The abstract model and topology of service aggregation in SVNs is shown
in Fig. 5. Given that the consumer requests an aggregated service consisting
of two service functionalities, we have two candidate pools of atomic services
from different service providers. From a Mechanism Design perspective, which
is the focus of most current work on SVNs, the question is how we can select a
combination of these atomic services out of the candidate pools that best satisfies
the consumer requirements (in Fig. 5, there are 3 × 2 = 6 different combinations
for the requested aggregated service). In SVNs, this is implemented through a
“complex service auction” [3]. The main goal of this mechanism is to maximize
the welfare of the SVN, which is the sum of consumer and provider utilities. The
provider utility depends on the costs of service provisioning and the revenue for
the service. The consumer utility is calculated as the difference of the consumer’s
monetary valuation of the aggregated service and its price. Based on desired
minimum and maximum values for the service attributes, each customer has
a certain valuation (maximum willingness to pay) for a perfect service, i.e. a
service that completely fulfills (or exceeds) the requirements. This valuation is
then multiplied by the score (∈ [0, 1]) for the actual aggregated service, which
depends on how close the aggregated service attributes are to the consumers
requirements.
The mechanism implements two steps. In the first step, the calculation of
the allocation, the mechanism computes the different potential combinations of
atomic services to the desired aggregated service (the aggregation operation of
service attributes depends on the attribute type, e.g. the price for the aggregated
service is the sum of prices for the atomic services). The mechanism selects the
aggregated service with the highest (positive) difference between consumer val-
uation minus the costs of the atomic services. In the next step, the calculation
of the payments, the mechanism implements a Vickrey-Clarke-Groves (VCG)
payment scheme to determine the actual payments to the providers of the allo-
cated atomic services. In contrast to other payment schemes, the VCG scheme
is desirable as service providers have the incentive to report the attributes and
prices of their services truthfully to the marketplace, without the incentive to
manipulate. This property is achieved by rewarding service providers according
to their relative importance (added value) to the SVN, which means they can
receive an additional discount on their service provisioning price.
This mechanism, with the described allocation and payment calculations, has
certain desirable properties. For example, it is known to be allocative efficient,
i.e. it selects the best combination of atomic services given the consumer prefer-
ences. Further, as mentioned earlier it is also strategy-proof, which means that
the dominant strategy for service providers is to submit their service attributes
truthfully to the marketplace, as other strategies will not yield better outcomes
for the providers. However, the Impossibility Result by [16] shows that such a
mechanism is not budget-balanced, which means that in certain circumstances
the discounts to the providers together with the price for the allocated atomic
services exceeds consumer payments. In other words, in such case the market
would have to be (externally) subsidized which might not be practical for many
57
scenarios. On the other hand, implementing other payment schemes to achieve
budget-balance yield a loss of the strategy-proofness, i.e. service providers might
gain by misrepresenting their service attributes to the SVN which can lead to
complex strategic behavior.
For the study of SVNs, Service Network Analysis has been applied in various
research questions. Considering the dynamic behavior of the SVN, we can look at
the incentives the providers have to join the network. As a competitive and vital
SVN has to provide many (functionally) different services to accommodate for
diverse consumer requirements, Conte et al. [9] propose a scheme that rewards
service providers to participate in SVNs (even if their services are not selected).
The value of each service provider is calculated through a metric that is a proxy
for the relative power of the provider in the network. Once service providers
are participating in the SVN, their goal is to be allocated and receive revenues
from their allocated services. As unsuccessful providers might leave the network,
an important question is how the providers can select or adjust their service
attributes such that they better fit the customer requirements. Haas et al. [13]
show that through appropriate learning strategies, the providers are able to
adjust to (potentially time-dependent) consumer requirements, and are even able
to tacitly collude by dividing existing market segments among the providers.
While current work on SVNs has mainly focused on economic topics, the
augmentation of the SVN concept with semantic capabilities to an OSSN along
with the use of Social Network Analysis promise to be fruitful. Such an amal-
gamation would enable a better description and usability of SVNs as well as an
improved understanding of their dynamic behavior.
6 Related work
In most work done so far, existing approaches fail to adhere to service-dominant
logic [18] and focus too much inward the company instead of the service network
they belong to (c.f. [12,1,10,11,17]). Service networks are not viewed as global
structures. Furthermore, the efforts to analyze networks was carried out as iso-
lated activities from the Business Process Management (BPM) field (e.g. [10])
or from the economical side (e.g. [11,17]), among others.
For example, e3 service [12] provides an ontology to model e-business models
and services. The model targets to represent very simple relations between ser-
vices from an internal perspective, e.g. core-enhancing, core-supporting, and sub-
stitute. From an external perspective, the value chains proposed do not capture
explicitly service networks across agents and do not try to analyze quantitatively
the effect of relationships.
In [10], the authors look at service networks from a BPM and Service Ori-
ented Architecture (SOA) perspectives and present the Service Network Nota-
tion (SNN). SNN provides UML artifacts to model value chain relationships of
economic value. These relationships take the form of what we can call ’weak’
relationships since they only capture offerings and rewards which occur be-
tween services. The notation is to be used to describe how a new service can
58
be composed from a network of existing services. The focus is on compositions,
processes, and on establishing how new services can be created using BPM to
describe the interactions of existing SOA-based services.
Allee [1] uses a graph-based notation to model value flows inside a network of
agents such as the exchange of goods, services, revenue, knowledge, and intangi-
ble values. In the same lines, Weill and Vitale [21] have developed a formalism,
called the e-business model schematic, to analyze businesses. The schematic is
a graphical representation aiming at identifying a business model’s important
elements. This includes the firm relationships with its suppliers and allies, ben-
efits each participant receives, and the major flows of product, information, and
money. Both approaches only take into account value flows and do not consider
other types of relationships that can be established between agents.
From a business perspective, there is an apparent trend for companies (e.g.
service providers) to specialize by focusing on core competencies and becoming
a member of adaptable networks [11]. As the transition to such networks of spe-
cialized service providers leads to challenges and new requirements for business
models and service components, the field of service networks has been identified
as important research priority [17].
Nonetheless, a challenge yet to be solved involves modeling and optimizing
the functions of service-centric organizations from technological, business and
legal points of view, extending work on optimizing Web process [19]. Seman-
tic Web technologies of today will need to be extended to provide or at least
support large-scale modeling, analytics, and optimization. In this regard, new
approaches are need to express and quantify the impact that one service has
on other services, as well as to understand the collective behavior and perfor-
mance/profitability characteristics of service networks.
7 Conclusions
We envision a world that is well connected via global OSSNs. Semantics will
have a major role in creating a large-scale and integrated service network. Or-
ganizations, groups and individuals will have tools and platforms to advertise
their know-how, capabilities and skills in the form of services to the world. A
huge number of detailed firm-generated (or even user-generated) services will be
available worldwide. Some services will be shared, composed, co-created, person-
alized, others are crowdsourced.
As service networks emerge, their study will enable to understand how a
service-based society grows and changes overtime. Service network analysis (SNA)
can provide theories, mathematical models, algorithms, techniques, and tools to
achieve this goal. This paper presented four applications of SNA: optimization,
evolution, network effect, and service value. Optimization targets to construct
networks of services which minimize the overall cost. Network evolution relies on
time to study and forecast how a network structure will evolve overtime. Net-
work effect explores the impact of changing the characteristics of one or more
service nodes in the other services and in the network itself. Finally, service
59
value network enable to analyze the influence providers’ strategies in the net-
work. These four methods provide the first building blocks to demonstrate the
practical application of SNA to better understand how service networks function
in a global, interconnected service-based societies.
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