IT Service Markets

Transcription

IT Service Markets

Features of IT Service Markets:
A Systematic Literature Review1
(Supplementary Material)
Technical Report
tr-ri-16-350
Version: 0.1
Bahar Jazayeri, Marie Christin Platenius,
Gregor Engels, Dennis Kundisch
University of Paderborn
Zukunftsmeile 1
D-33102 Paderborn, Germany
[email protected]
Paderborn, May 23, 2016
1
This work was supported by the German Research Foundation (DFG) within the Collaborative Research Centre
“On-The-Fly Computing” (CRC 901).
Contents
1. Introduction
1.1. An Observation of IT Service Markets . . . . . . . . . . . . . . . . . . . . .
1.2. A Basic Definition of IT Service Markets . . . . . . . . . . . . . . . . . . .
2. Survey Procedure
2.1. Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1. The Need for a Systematic Literature Review . . . . . . . . . .
2.1.2. Define Research Question . . . . . . . . . . . . . . . . . . . .
2.1.3. Develop Review Protocol . . . . . . . . . . . . . . . . . . . . .
2.1.4. Evaluate the Review Protocol . . . . . . . . . . . . . . . . . .
2.2. Conducting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1. Search based on Search Strategy . . . . . . . . . . . . . . . . .
2.2.2. Filter using In/Exclusion Criteria . . . . . . . . . . . . . . . .
2.2.3. Extend Search Strategy . . . . . . . . . . . . . . . . . . . . . .
2.2.4. Filter using Extend Search Strategy and Quality Assessment . .
2.2.5. Snowballing . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.6. Extract Data . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.7. Synthesize Data . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3. Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1. Primary Features of IT Service Markets and their Interrelations
2.3.2. Popularity of Features . . . . . . . . . . . . . . . . . . . . . .
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Bibliography
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A. Search Results based on Search Strategy
List of all Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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B. Search Results based on In-/Exclusion Criteria
List of all Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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III
Chapter 1.
Introduction
This chapter presents our observations of existing IT service markets. Based on these observations, we derive a basic definition for IT service markets.
1.1. An Observation of IT Service Markets
The emergence of electronic marketplaces providing IT solutions has become very popular in
recent years. Considering electronic marketplaces providing IT solutions, many existing markets can be recognized around us (see Figure 1.1). Some of them are developed for a broad
range of end users like mobile App stores and browser App stores. Some others are specific to
a certain domain or a group of users. Examples are Mashape1 , programmableweb2 , and Mashery3 that allow users with programming expertise to trade APIs. Salesforce AppExchange4 is
designed to provide business applications. Furthermore, Amazon Web Services (AWS) marketplace5 and SAP Service Marketplace6 support enterprise application developers with cloud
computing services. Further examples are software repositories [LdPDA04], e.g., Binpress7 ,
CPAN8 , and SourceForge9 that facilitate the exchange of source codes among developers. We
call such electronic marketplaces providing IT solutions IT service markets.
1
www.mashape.com
www.programmableweb.com
3
www.mashery.com/
4
appexchange.salesforce.com
5
aws.amazon.com/marketplace
6
websmp201.sap-ag.de
7
www.binpress.com
8
www.cpan.org
9
sourceforge.net
2
1
2
CHAPTER 1. INTRODUCTION
Mobile App stores
Apple App store
Google Play
Amazon Appstore
Mobango
Cloud service marketplaces
Telecommunication carrier App store
MediaTek App Store
StrikeIron
T-Mobile Web2go
Black Berry World
SalesForce
AppExchange
Verizon VCast
Nokia Ovi Store
AWS Marketplace
Vodafone 360
Component
repository
Mashery
ATT AppCentral Research
Microsoft Store
Third-party (Android/iOS App stores)
Programmable
Web
4CaaSt
OTF Markets
FOR 1371
API repository
Vitual Fort Knox
F-Droid
Binpress
GetJar
Slideme
SourceForge
Java Store
Eclipse Marketplace
Aptoide
CPAN, CTAN
OpenIntents
SteamPowered
Cydia
Envato
Mashape
Bowser Marketplace (Firefox, Chrome,...)
Others
Cytoscape
Figure 1.1.: Examples of existing IT service markets
3
1.2. A BASIC DEFINITION OF IT SERVICE MARKETS
1.2. A Basic Definition of IT Service Markets
Although the mentioned markets in Section 1.1 seem different in their target group of users
and in the kind of deliverable software, the fundamental concept of two- (multi)-sided markets
[Rys09] and provisioning IT solutions over electronic marketplaces are common in all of them.
Theses marketplaces act as an operative and administrative intermediary between two main
types of market participants: providers and requesters of IT solutions.
We provide a two basic definitions that serve as a conceptual basis for our literature review.
The definitions specify the scope of our survey during the filtering process of the literature.
Definition 1 A service is an IT-solution provided by a service provider.
Definition 2 An IT service market is an electronic marketplace, which exclusively allows trading of such services between providers and requesters.
Figure 1.2 sketches the relation between the term IT service market and the other terms. We
consider Electronic marketplaces as the superset term for the IT service markets. Electronic
marketplaces [SS04] allow trading a wide range of products and/or services including software. An example of electronic marketplaces is ebay.com. Furthermore, we consider cloudbased markets, App Stores, software repositories as the subset terms of IT service markets,
since they provide specialized IT solutions to specific groups of users. We leave the set of
subset terms open, aiming at identifying further IT service markets in the future.
IT service markets
API marketplaces
Cloud marketplaces
Pogrammableweb.com
AWS Marketplace
App Stores
Software repositories
Google Play
Binpress.com
Figure 1.2.: The procedure of the survey
Chapter 2.
Survey Procedure
The objective of our investigation is to extract the primary features of IT service markets,
which are addressed in the literature. For this purpose, we performed a Systematic Literature Review (SLR) [KBB+ 09], aiming at capturing as many publications as possible related
to our research objective. We follow Kitchenham’s guidelines [KBB+ 09] in performing the
systematic literature review to assure reproducibility and minimizing biases regarding our results. The SLR consists of three main stages as shown in Figure 2.1: planning the review,
conducting the review, and reporting the findings.
Planning
1. Why SLR?
2. Define research questions
3. Develop review protocol and search strategy
4. Approve the review protocol
Conducting
1. Search based on search strategy
329
2. Filter using in-
/exclusion criteria
142
3. Filter using quality assessments and 56
extended search strategy
4. Identify more relevant studies using snowballing
60
Grounded Theory
5. Extract data: Highlighting &
Concept excerption
Open, Axial and Selective Codings
6. Synthesize data Reporting
Figure 2.1.: Survey procedure
5
6
CHAPTER 2. SURVEY PROCEDURE
2.1. Planning
At the planning stage, we specified a review protocol and further strategies regarding the actions to be taken in the conducting phase. The planning process consists of clarifying the
need for a SLR (Section 2.1.1), defining a concrete research question (Section 2.1.2), developing the review protocol (Section 2.1.3), and proving the review protocol in iterations by the
authors.
2.1.1. The Need for a Systematic Literature Review
The lack of a reference model or definition for IT service markets avoid the stockholders of
these markets to design them systematically. A first step towards a reference model for IT service markets is to gain a comprehensive view on their building blocks. Such a comprehensive
view is still missing in the literature. Related work focuses on single aspects of IT service marketsṖerforming a systematic review of the existing IT service markets is a first step towards a
holistic integrated view on IT service markets covering all their outstanding business and IT
building blocks (features) that are addressed in literature.
2.1.2. Define Research Question
The SLR need to be initiated with a concrete research question, which is derived from the fundamental context (IT service markets) and the motivation of performing the SLR. Accordingly,
our research question is:
RQ What are the primary features of IT service markets and how are they related to each
other?
2.1.3. Develop Review Protocol
The main part of the SLR planning is the development of a review protocol. The review
protocol specifies how the research are supposed proceed with the further steps of the SLR.
The review protocol has been later extended during the conducting phase in order to sharpen
the search results and increase the chance of capturing relevant publication as suggested by
[Kee07].
2.1.3.1. Search Strategy
The search strategy specifies the search terms, search phrases and further selection criteria
(in-/ex-clusion and quality assessment criteria) that are used in filtering the literature.
To find the initial set of sources, we first defined the search terms. The ideas of the search
terms are inspired from the research question and our observation of IT service markets in
real world, which is discussed in Chapter 1. Our search terms are service and market. To
2.1. PLANNING
7
detect as many of the relevant sources as possible, alternative terms are defined. We defined
software, app, application, third-party, plug-in, and component as the alternatives to the term
service. Furthermore, the alternatives to the term market are defined as marketplace, store,
“App store”, repository, archive network, and catalog. Last, we determined our search phrase
as a combination of the search terms and boolean operators.
The search phrases follow the following pattern:
• (service OR software OR component OR third-party OR app OR specification OR interface OR plug-in) AND (market OR store OR repository OR catalog OR marketplace OR
archive network OR cloud)
The literature search is performed between February 2016 and May 2016. We chose Google
Scholar as the search database, as suggested by Kitchenham [Kee07]. Google Scholar is a
meta-search engine that performs searches through several digital libraries. During the search,
a source is selected if at least one of the terms from each set appears in the title.
2.1.3.2. In-/Exclusion Criteria
The initial set of sources are to be filtered using the in-/exclusion criteria. We included a
source if:
a) it deals with the definition of IT service markets,
b) it deals with one of the functional or the non-functional aspects of IT service markets,
c) it introduces a new instance of IT service markets,
d) it considers architecture of IT service markets.
Furthermore, the source must be available through the most prominent digital libraries, e.g.,
Springer Link, ACM Digital Library, IEEE Xplor, Citeseer library, Science Direct, etc.
We excluded a source from our survey if:
a) the service discussed by the source is not an IT solution and
b) the marketplace, provided by the source, provides other services/products than ITsolutions.
We evaluated and filtered the initial set of sources firstly based on their abstracts and conclusions using the in-/exclusion criteria. Secondly, if we still could not decide about the relevance
of a source, we read the whole source.
8
2.1.3.3. Quality Assessment
To focus on the high quality research, we have added another exclusion criterion:
a) the source should not be in the form of a preface, tutorial, book review, or presented
slide.
This allows us to exclude the publications, which have not been peer reviewed.
2.1.3.4. Data Extraction Strategy
The data extraction strategy defines the data to be extracted from the sources. RQ and the
research objective specify what data need to be extracted.
The challenge regarding IT service markets is that due to the lack of a reference model
or reference architecture, the publications do not usually address IT service markets directly.
Furthermore, the publications use inconsistent terminologies according to the underlying technologies. For instance, we encounter alternative words for “service”, e.g., “application”,
“App”, “Software”, “SaaS”, “API’", etc. As a result, we cannot directly compile a set of
features from the sources using keyword-based data search. But rather, an interpretation of
the information presented in the sources is needed. When such interpretations are needed,
[Kee07] suggests to specify processes of making such implications at the planning stage.
To extract primary features of IT service markets, we developed an extraction scheme based
on an adoption of Grouned Theory (GT). G LASER AND S TRAUSS [GS09] originally proposed
GT to support researchers to elaborate a theory or a theoretical report of the general features
of a topic by performing bottom-up conceptualizations of the data, which is collected based
on empirical observations. It is particularly suggested for the emergence of new topics [Fo04].
GT is a research methodology to extract, categorize, and conceptualize ideas in a descriptive way. We follow the guidelines provided by [WFW13], which is proposed for rigorously
reviewing and analyzing literature using GT. The core procedure of GT is excerpting the data
relevant to the research question and them performing coding techniques [WFW13].
Excerpting the data from the final set of source is to be done during the data extraction
phase of our SLR. This includes the extraction of architectural elements and building blocks
of IT service markets that are discussed by the sources. Furthermore, GT proposes the coding
techniques in order to analyze literature and extract the knowledge based on interpretation. We
perform the coding techniques during the data synthesis stage of the SLR in Section 2.1.3.5.
2.1.3.5. Data Synthesis Strategy
Data synthesis strategy define how the extracted content of the literature is to be analyzed.
This includes clarifying whether or not a formal meta-analysis is to be used [Kee07].
As explained in Section 2.1.3.4, we follow the guidelines of GT to analyze the final set of
sources. At this stage, three stages of codings (open, axial, and selective codings) need to
be performed. Open coding is the process of grouping a set of excerpts into a concept and
2.2. CONDUCTING
9
building categories from a set of concepts. Each category is an abstract interpretation of its
concepts. Axial coding is the process of defining sub-categories and specifying the relation
between categories and sub-categories. By selective coding, main categories and the relation
between them are identified [WFW13]. At each stage of coding, we identify architectural
concepts found in the sources and group them into more abstract concepts.
2.1.4. Evaluate the Review Protocol
The review protocol was evaluated by the PhD students (Jazayeri and Platenius) and the supervisors (Engels and Kundisch) in iterations. First, Jazayeri set a review protocol, which is
later reviewed by Platenius. The modified version of the review protocol is presented to Engels. After applying further modifications, the review protocol is discussed and approved by
Engels and Kundisch, which resulted in the final review protocol.
2.2. Conducting
In the conducting phase, we performed the SLR based on the review protocol developed in
Section 2.1. This includes the initial filtering of the literature based on the search strategy (cf.
Section 2.2.1). Furthermore the initial set of results are filtered using in-exclusion criteria (cf.
Section 2.2.2) and performing snowballing (cf. Section 2.2.5), which result in a final set of
sources. Afterward, we extract the data from the final set of source(cf. Section 2.2.6). Finally,
we synthesize the extracted data (cf. Section 2.2.7).
2.2.1. Search based on Search Strategy
At this stage, we performed the initial search using the search strategy. This process resulted
in the initial set of studies including 356 sources. Afterwards, we removed the duplicates from
the initial set that resulted in 333 studies.
2.2.2. Filter using In/Exclusion Criteria
The large number of sources are filtered based on the in/exclusion criteria. By applying in/exclusion criteria, the high number of publications in the initial set of results are reduced to
142 sources.
2.2.3. Extend Search Strategy
During the conducting phase, we added an additional selection criterion:
• the source should not be published earlier than 2008. The reasons for choosing
this specific year are the introduction of the concept of cloud market by B UYYA ET
10
AL .
[BYV08] and the announcement of the first mobile App stores ever, Apple App
store and Google Play in 2008 [JB13]. Using this exclusion criterion, we focus on the
most recent work. We expect the most prominent research achievements, which were
published before 2008, to be reflected by the recent work.
2.2.4. Filter using Extend Search Strategy and Quality
Assessment
By applying quality assessment and the recently defined selection criteria, we identified 56
sources that objectively address the concept of IT service markets.
2.2.5. Snowballing
We applied snowballing [Woh14], which is the act of inspecting the publications cited by the
source. We have included snowballing in order to enrich the final set of results by identifying
more relevant publications. Figure 2.1 shows the action of snowballing in dark color showing
that it is not originally a part of the SLR methodology. At this stage of filtering, we reached
the final set of results including 60 sources.
2.2.6. Extract Data
According to the review protocol, we first inspect the sources at this stage. While reading„
we look for possible answers to our RQ. We highlighted and made notes of the data, where a
building block, component, or architectural element of IT service markets is discussed.
2.2.7. Synthesize Data
After extracting the important information, we synthesize the data. This include the analysis
and interpretation of the sources based on the GT’s coding techniques as explained in Section
2.1.3.5.
2.2.7.1. Open Coding
We applied the open coding on the excerpts of the sources. This process includes the extraction
of concepts from the sources. The interesting concepts regarding our RQ are building blocks of
IT service marketsṠpecially, we considered what the sources deal with in sense of architectural
elements and components. During the code generation, the sources may share the concepts
with each other. Or, they may cause to generate new concepts.
The open coding resulted in generating a set of 43 shown in Tables 2.1 and 2.2. Table 2.3
summarizes the generated open codes.
11
2.2. CONDUCTING
Trust
X
Malware detection
Information' flow'
analysis
X
Policy
X
X
Test coverage
Access'control
X
Authentication
X
Privacy
X
Integrity' &'
confidentiality
Social
networking
X
Service'
composition
Resource
matching
X
X
Ontologies
Service'matching
X
X
X
X
X
X
X
service'discovery
Support' for'users’'
diversity
Service'categories /'
sectors
Service'space'
management
Product'
diversification
Collaboration
X
Monitoring
functions
Service'repository
X
SDK
X
X
Dynamic SLA
X
QoS analysis
X
X
X
X
X
X
X
X
X
X
X
X
X
[HO11]
[GCCJ11]
[PRS09,@LFZD09]
[PO09]
[WGB11]
[TSB10]
[BRC10]
[PO09]
[VPB09]
[GWB10,@BCPR09]
X
X
X
[CLH+14]
[MHJ+15]
[KPKL14]
Sources
Concepts
[FLL+13,GMBV12] X
Sentiment' analysis
[CG12]
[LB13]
[HJZ12]
[JBL12]
[Car12]
[LR11]
[HHYH09]
[MVG+14]
[RA12]
[MKM11]
[TTP11]
X
X
X
X
App mining
X
Ranking' chart
X
X
X
X
Download' rank
X
X
X
X
X
X
Sales'performance
Service'rank
X
X
X
X
X
X
X
X
X
X
X
X
X
Rating
X
X
Pricing
Revenue'of'
developers
X
X
X
X
General'SLA
Business'strategies
Review
interpretation
X
X
X
X
X
X
Platform'
integration
Technology'
openness
Portal'
centralization
Service time'
management'
Table 2.1.: Distribution of the sources among the open codes (1)
12
X
Trust
X
Malware detection
X
Information' flow'
analysis
X
X
X
X
X
X
X
X
X
Support' for'users’'
diversity
Service'categories /'
sectors
Service'space'
management
Product'
diversification
X
X
service'discovery
X
X
Service'matching
X
X
X
Service'
composition
Resource
matching
X
X
Ontologies
X
X
Social
networking
X
X
X
X
[BRC10]
X
X
X
[KC09]
[ZWZJ12]
Monitoring
functions
[WB10]
X
Collaboration
Service'repository
X
X
X
X
Privacy
X
Integrity' &'
confidentiality
X
X
Authentication
X
X
Access'control
X
X
Policy
X
Test coverage
SDK
Platform'
integration
Technology'
openness
Portal'
centralization
Service time'
management'
X
X
X
X
QoS analysis
X
Dynamic SLA
General'SLA
Business'strategies
Price'model
Revenue'of'
developers
X
Rating
Sales'performance
X
Service'rank
Download' rank
X
Ranking' chart
Review
interpretation
App mining
[IKC09]
[PO09]
[SK11]
[DMA+10]
[HAH12,]
[EJM+14]
[HHYH09]
[KKLK13]
[FHT+12]
[IvJ11]
[APB+14]
[LFZD09]
[LMD+13]
[CWO+11]
[DSTH12]
[CJP+11]/
Sources
Concepts
[AHKZ08,/KW08]
Sentiment' analysis
Table 2.2.: Distribution of the sources among the open codes (2)
13
2.2. CONDUCTING
No.
Open codes
No.
Open codes
1
Sentiment a nalysis
22
Collaboration
2
App mining
23
Product d iversification
3
Review interpretation
24
Service space management
4
Ranking chart
25
Service categories / sectors
5
Download rank
26
Support for u sers’ d iversity
6
Service rank
27
service d iscovery
7
Sales p erformance
28
Service matching
8
Rating
29
Resource matching
9
Revenue o f d evelopers
30
Service composition
10
Price model
31
Ontologies
11
Business strategies
32
Social networking
12
General S LA
33
Privacy
13
QoS analysis
34
Integrity & confidentiality
14
Dynamic SLA
35
Authentication
15
36
Access control
37
Policy
17
Monitoring functions
Service time management Portal centralization
38
Test coverage
18
Technology o penness
39
Information flow a nalysis
19
Platform integration
40
Malware detection
20
SDK
41
Trust
21
Service repository
16
Table 2.3.: Total number of generated open codes (concepts)
14
2.2.7.2. Axial Coding
In the second step, we performed axial coding. We grouped the extracted architectural concepts (the open codes) into more abstract concepts. To ensure the validity of the conceptualization, we needed to refer to the sources or review the excerpts and then refer to the codes
and group them. This process is repeated in iterations.
In addition, the relations between the concepts are defined at this stage. Specially, we
considered what interrelations the sources discover regarding the concepts (research results).
Figure 2.2 presents the results of the axial coding procedure. The tree-like arrangement of
the concepts represents their grouping. The dashed arrows represent the relations between the
concepts exposed during the literature analysis. An arrow from concept A to concept B shows
that A influences B in a certain way.
Figure 2.2.: Results of the axial coding
Trust
Code Malware
Policy
analysis detection
Test coverage
Information Authentication
flow analysis
Access control
Integrity & confidentiality
Privacy
Security
Support for users’ d iversity
Service categories
Product
diversification
Service
Service
matching composition
Service specification
matching
Resource
matching
Semantic Service
interpretation Discovery
Recommendation system
Price model
Download rank
Ranking chart
Service rank
Ranking
Ontologies Social
networking
Rating
Sales performanceRevenue o f developers
Revenue model
Product p ortfolio
QoS analysis
Monitoring Collaboration
functions
Service
repository
App mining
Review
interpretation
Sentiment
analysis
Reviewing
Repository
Portal
Platform
SDK
integration
Service space Technology
management
openness
Portal
centralization
Business strategies
QoS analysis
Monitoring functions
Dynamic SLA
Service time
management
SLA
2.2. CONDUCTING
15
16
2.2.7.3. Selective Coding
By performing selective coding, the previously generated categories are grouped based on the
conceptual similarites in architectural elements that they address. Finally, we developed six
main categories by performing selective coding: business model, reputation system, security, MEPC, Service Level Agreement (SLA), recommendation system. We terminated the process of coding, when, so called, theoretical saturation happened. This means no
new category, concepts, or interesting relations could be found [SC94].
The result of our extraction scheme is shown in Figure ??. The tables demonstrate the
research focus of the sources regarding the main categories. A table is dedicated to each main
category. Each table represents the sub-categories of each category. A cell marked with X,
denotes that a sub-category is discussed by a source. In addition, some sources studied the
relation between a main category with other categories. In this case, each table demonstrate
such relations by the columns with border lines at the right side of the tables. A cell marked
with O denotes that such a relation exists between the main category and another category that
is discussed by the source.
2.3. Reporting
In this section, we report the results of our literature analysis shown in Table ??. The results
are the identification of 6 main categories and their sub-categories related to our RQ. The
main categories represent the most abstract architectural elements of IT service markets that
we call primary features. We also call the sub-categories sub-features.
The reporting phase focuses on three important aspects of the results: 1) the set of primary
features and their sub-features (Section 2.3.1) and 2) the popularity of the features (Section
2.3.2).
2.3.1. Primary Features of IT Service Markets and their
Interrelations
Figure 2.4 gives an outline for the primary features of IT service markets and their three most
important sub-features addressed in the literature, which have been synthesized from the data
in Section 2.2.
An important part of the synthesized data reveals the relations that exist between the primary
features. Figure 2.5 illustrates the relations between the main categories (features). An arrow
from feature A to feature B shows that A influences B in a certain way that is summarized as
an arrow label.
17
[FLL+13]
X
X
[GMBV12]
X
X
X
X
X
[CG12]
O
[ZWZJ12]
X
O
[DSTH12]
O
[CJP+11] X
[CWO+11] X
[GCCJ11]
[BRC10]
[LFZD09]
X
X
O
O
X
X
X
[16]
O
X
(d) Mediating Electronic
Product Catalog (MEPC)
[19]
X
X
X
X
[23, 12, 62,
64, 14, 54]
X
[29]
X
[66]
[40]
Service composition
Semantic
interpretation
Service discovery
Service matching
X
[KKLK13]
X
[WB10,
AHKZ08, KW08]
X
X
[HHYH09]
X
X
(e) Security
X
X
[FHT+12]
X
X
X
X
X
X
X
X
X
[MVG+14]
X
X
X
[APB+14]
O
O
O
X
X
X
O
[TSB10]
X
X
Sources
Business strategies
Reputation system
Price model
Product portfolio
Revenue model
X
[10, 71]
X
[HO11]
X
X
X
X
(b) Business Model
Security
QoS analysis
Reputation System
Business model
Service repository
Portal Sources
(a) Reputation System
X
Code
analysis
Malware
detection
X
X
X
Policy
X
Privacy
[CLH+14]
O
X
X
(c)
Recommendation
system
[WGB11]
X
[TSB10]
X
[BRC10, VPB09, PO09]
[PRS09, LFZD09]
Business model
O
X
X
QoS analysis
Dynamic SLA
Monitoring system
O
X
Sources
O
Sources
Security
Review
interpretation
Sentiment
analysis
Rating
X X
[HJZ12]
X X
[JBL12, X
Car12]
[LR11]
X
[TSB10, X
HHYH09]
[MHJ+15]
X
[KPKL14]
[MVG+14, MKM11]
[LMD+13]
[HJZ12, LR11]
[JBL12]
[Car12]
[RA12]
[TTP11]
[IvJ11, HHYH09]
[WB10]
[GWB10, BCPR09]
[PO09]
Sources
[LB13]
App mining
Open Codes
Service rank
Ranking chart
Download rank
Selective Reputation system
Code
Axial Ranking Reviewing
Codes
Business model
2.3. REPORTING
O
X
X
(f) SLA
Figure 2.3.: The distribution of features and their relations among the sources
Monitoring
system
Dynamic
QoS
SLA
analysis
SLA
Revenue
model
Business
model
Reputation
system
MEPC
Security
QoS
Malware
Business
Rating
analysis
detection
Portal
strategies Ranking
Service
Static
Product
discovery Service
analysis
Service
Policy
Reviewing
portfolio
matching
repository
Semantic
Privacy
Price
interpretation
model
Service
composition
Recommendation
system
IT service market
18
Figure 2.4.: Outline of the primary features and their sub-features
19
Reputation
system
«ensures the validity
of rating and ranking»
Security
«improves rating
and ranking»
Business
model
«improves sales
performance»
SLA
«imposes extra costs
in dynamic markets»
«enhances
collaborative work»
«attracts
developers»
MEPC
«ensures malware-free services,
privacy, and access control»
Figure 2.5.: Outline of the relations between the primary features
Data Analysis – Feature Popularity
2.3.2. Popularity of Features
Figure 3 shows how many percentage of the contents of the sources from 2008 to May 2016
is dedicated to each feature on the basis of our literature analysis.
Business Model
22%
14%
© Heinz Nixdorf Institut, Universität Paderborn
Recommendation
System
Security
21%
19%
11%
Reputation System
13%
SLA
MEPC
Figure 2.6.: Distribution of feature popularity in the period from 2008 to May 2016
1
C5 – Architectural Management of On-The-Fly Computing Markets
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IT Service Markets

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