Who Runs the Internet?

Classifying Autonomous Systems into Industries

Annika Baumann and Benjamin Fabian

Institute of Information Systems, Humboldt University Berlin, Spandauer Str. 1, 10178 Berlin, Germany

Keywords: Internet, Autonomous System, Industry, Classification.

Abstract: The Internet consists of a network of Autonomous Systems (ASs). To understand which kind of

organizations control those ASs can help to better assess the Internet structure in terms of economic

interests and reliability. The current paper proposes a novel classification approach by combining AS-

specific data with business data from the United States Securities and Exchange Commission. Furthermore,

more detailed industry classes than in previous works are considered, inspired by the North American

Industry Classification System (NAICS). Using our methodology on a recent data set, we were able to

classify 56.69 % of the considered ASs into industries. This lays a foundation for our future work on

investigating the important players of the Internet backbone as well as their economic interests and risks.

1 INTRODUCTION

The Internet expanded rapidly during the last

decade. From 2001 to 2013, the fraction of the world

population using the Internet increased from 8.0 %

(International Telecommunication Union, 2011) to

an estimated 38.8 % (International

Telecommunication Union, 2013), with a

simultaneous population increase from 6.1 billion

(United Nations Population Fund, 2001) to 7.2

billion people (U.S. Department of State, 2013),

resulting in approximately 2.8 billion Internet users

today versus 0.49 billion in 2001. This rapid growth

in users resulted in a heterogeneous and complex

system, making analysis and modelling of the

Internet structure difficult.

Our paper is part of an on-going research project

that is investigating how the Internet of today is

structured in terms of economic interests, control

and reliability. Who are the important players of the

Internet backbone, what are their economic interests

and risks with respect to their business models, and

what are the implications for reliability, security and

privacy as well as political control?

Our first step towards approaching these goals is to

classify the important organisations that control

Autonomous Systems (ASs) of the Internet

according to business categories, which could

support future analyses along all of those

dimensions. For example, with respect to reliability

and security, common methods assess the robustness

of the Internet structure based on graphs and

modelling the Internet as an abstract complex

network consisting of nodes (each representing an

AS) that are connected via edges. However, such

approaches solely focus on topology-based

robustness and so far ignore the highly economically

driven character of the Internet, as well as

corresponding heterogeneous risks of attack and

control.

At an organizational and global routing level of

abstraction, the Internet can be considered as

composed of ASs. An AS can be defined as “a group

of IP networks run by one or more network

operators with a single clearly defined routing

policy; when exchanging routing information to the

outside, each AS is identified by a unique number

(Réseaux IP Européens, 2011). The Internet

Corporation for Assigned Names and Numbers

(ICANN) and, via delegation, the Regional Internet

Registries (RIR) are responsible for registration of

these AS numbers (ASNs). The amount of registered

ASs increased from roughly 10,000 in the year 2000

to more than 60,000 in 2013 (Potaroo, 2012), which

is also another indicator for the substantial increase

of Internet complexity.

Classifying the major players of the Internet

backbone is an interesting challenge in itself because

publicly available business data is sparse. Our

approach presented in this article focuses on

361

Baumann A. and Fabian B..

Who Runs the Internet? - Classifying Autonomous Systems into Industries.

DOI: 10.5220/0004936803610368

In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 361-368

ISBN: 978-989-758-023-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

analysing the public registration information for AS

numbers. Moreover, we present an approach for the

classification of ASs into detailed industry classes in

order to better understand the organizational and

economic patterns of the Internet.

The rest of the paper is structured as follows:

Section 2 discusses related work. Section 3 presents

the data sources, followed by Section 4 on our

methodology. Section 5 presents our results, and

Section 6 concludes the paper.

2 RELATED WORK

Some earlier research articles proposed approaches

for classifying ASs into various categories. The

classification approach used in our paper was

initially inspired by the methods employed by

Dimitropoulos et al. (2005). Based on an expert

system that uses text classification techniques, the

authors used organization names to categorize ASs.

Each AS was assigned to one or more of the basic

classes Internet service providers (ISP), Internet

exchange points (IXP), network information centers

(NIC), companies providing no Internet service as

well as education- and research-, military-,

government- and health-related networks. The

authors were able to classify 20,598 out of 32,689

ASs in 2005, which corresponds to 63.01%.

Another work (Dimitropoulos et al., 2006) used

even more coarse-grained classification categories,

namely only large and small ISPs, customer ASs,

universities, IXPs and NICs. The method applied

was based on the AdaBoost algorithm (Freund and

Schapire, 1997) using several attributes (e.g.,

organization description; number of inferred

providers, customers and peers; number of

advertised IP prefixes) to classify the relevant ASs

into their respective classes. The authors were able

to classify 95.3% of 19,537 ASs with an accuracy of

78.1%.

The main focus of the work by Chang et al.

(2005) was to estimate traffic volume between

individual ASs. For this, the authors classified ASs

regarding their initial utility, which resulted into the

three classes web hosting, residential access and

business access. The methodology used by the

authors is different from other work conducted in

this area. Instead of investigating an individual AS

and assigning it to a class, they created a class and

tried to find relevant ASs on the Internet. The

authors were able to identify 56% of all BGP-

advertised ASs with their approach.

The primary focus of the paper by Dhamdhere and

Dovrolis (2011) was to analyse the evolution of the

AS ecosystem over the last 12 years. ASs were

classified into the classes enterprise customers, small

and large transit providers, access/hosting providers

and content providers. A decision tree approach was

applied for classification. In order to build the

training set, for each class 50 ASs were classified

manually. Afterwards, the classification was

conducted for 42,000 ASs by using the number of

customers and the number of peers as independent

variables. Classification accuracy for the classes

ranged between 76% and 82%.

All of those articles have in common that the

proposed classes are not comprehensive and do not

resemble real industries. Thus they contribute not

much to a better understanding of the industry

structure behind the ASs comprising the Internet.

Our work addresses this research gap by proposing a

classification approach that adopts fine-grained

industry classes.

3 DATA SOURCES

3.1 CAIDA

The Cooperative Association for Internet Data

Analysis (CAIDA) “is a collaborative undertaking

among organizations in the commercial,

government, and research sectors aimed at

promoting greater cooperation in the engineering

and maintenance of a robust, scalable global Internet

infrastructure.” (CAIDA, 2011). One project offered

by CAIDA is the AS Rank project (CAIDA, 2012). It

is based on Border Gateway Protocol (BGP) routing

data collected by RouteViews (2013) and the RIPE

NCC (2013). The list of ASs that is used in our

paper contains the information of 59,576 ASs. An

excerpt of the dataset can be seen in Figure 1. For

the purpose of classifying ASs into industry classes

mainly the org name attribute was considered as

highly relevant.

Figure 1: Excerpt of CAIDA AS Rank.

3.2 SEC

The U.S. Securities and Exchange Commission

(SEC) is a government agency in the USA (United

States Securities and Exchange Commission, 2013).

Its primary purpose is to regulate securities and

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enforce federal securities laws. Every company

publicly traded in the United States has to file

certain documents with the SEC. The Electronic

Data Gathering, Analysis, and Retrieval (EDGAR)

system makes those filings available to the public.

This can be used to gather the Standard Industrial

Classification (SIC) code for the company (Figure

2). An SIC code can be directly mapped to an

NAICS code using a mapping table (CareerOneStop

U.S., 2013). Thus it is possible to uniquely identify

the industry of an AS’s organization by use of the

EDGAR system. A limitation is that only

organizations that are listed on a stock exchange in

the USA can be found in the system.

Figure 2: Excerpt of SEC EDGAR result.

3.3 RIR as Information

As an additional information source, data from the

RIRs was retrieved. The website cidr-report.org

contains AS information from all RIRs. It allows

searching for individual ASs and returns the

information that comes from the WHOIS services of

the individual RIRs. In order to simplify the data

retrieval process, this website was also used to

retrieve AS-specific WHOIS information instead of

using the WHOIS services of different RIRs. A

sample of such information can be seen in Figure 3.

Figure 3: Sample of RIR AS information.

4 METHODOLOGY

Figure 4 gives an overview of the process of

classifying the ASs presented in this paper. As a first

step, the relevant industry classes for the

classification approach needed to be defined. Their

definition draws from the North American Industry

Classification System (NAICS, 2013). Due to the

intrinsic online setting of our investigation, special

adjustment was necessary, meaning that several of

these classes were either merged, dropped or

changed. In the case of ASs, some industries are

missing at all while some of them are

overrepresented. Therefore, the NAICS was only

used as a basis for the classification approach in our

particular setting. An overview of the classes can be

found in the Appendix.

Figure 4: Process of AS industry classification.

Step 1: Preprocessing. The initial AS list included

data from the year 2012 and was taken from the

CAIDA AS Rank project (CAIDA, 2012); it

contained 59,576 ASs. In order to only include

reasonable and recent data, the list was

preprocessed. At first, the information gathered from

the RIRs was used to filter for inactive ASs. This

reduced the list by 17,830 ASs, leaving 41,746 ASs

to classify. Furthermore, all ASs that did not have an

according organization name, i.e., all entries either

containing no specification of the underlying

organization or being a no registry entry, were

removed from the list. Eliminating 1,362 ASs, this

step left 40,384 ASs in the list.

Step 2: Keyword Classification. In the next step, a

keyword list was created by analysing word and

phrase frequencies with the help of an occurrence

counting of words, bi-grams and tri-grams. All

words and phrases that appeared quite frequently

were analysed in more detail. It was assumed that

tri-grams needed to occur at least five times, bi-

grams ten times and simple words twenty times to be

selected for deeper analysis. The rationale behind

AS list from CAIDA

AS Rank Project

Definition of

Industry Classes

Step 1:

Preprocessing

ASs to be Classified

Step 2:

Keyword

Classiﬁcation

Keyword

Classification

Result

Keyword List

Step 3:

SEC

Classiﬁcation

SIC Codes

SEC Edgar

Final Classification

Result

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363

this procedure was to include only those words and

phrases that are most frequent and therefore

important. This makes it possible to classify several

ASs at the same time based on a single phrase or

keyword. Keywords were mainly defined in such a

way that the organization name or a part of it had to

comply with the complete keyword. This means that

for example in case of the keyword “ship” only the

word itself would fit and not “membership” or

“ownership”. This was done to ensure the reliability

of keywords by avoiding undesired mismatches. The

selection of keywords itself was randomly cross-

checked based on real data to further ensure their

reliability and unambiguity. Only those words or

phrases were chosen whose unambiguity in relation

to industry classification was satisfactory. For

example the keyword “Internet service provider” is

highly reliable if it comes to sorting into the

category ISPs & Networks, while “service provider”

might lead to wrong results for the same category.

Organizations having a (part of their) name such as

“content service provider” would also fit into such a

category.

In order to minimize wrong categorizations, an

iterative learning process was applied. The

procedure was as follows: based on the first

selection of keywords, the AS numbers were

categorized into the industry classes created so far.

Each category was then checked for wrong

categorizations. For this purpose, the list of

categorized ASs and their underlying organization

was reviewed manually. If the categorization of an

ASN was wrong, the reason was identified and

eliminated with the help of refined or discarded

keywords. This procedure helped to ensure that only

those keywords remained that are at the same time

reliable and general. In order to check for further yet

not identified keywords, a list was generated that

contained all non-categorized ASNs. This list was

then manually checked for further keywords at each

iteration. This was particularly important in case of

misspelling and language-specific variations. For

example, the keyword “university” was represented

by many language specific variations such as

“universitas”, “universidad” or “univ”. An example

for misspelling is “network infomation center”

which occurred at least seven times in the list. Such

variations were additionally included in the keyword

list for each category.

Based on this extended and refined keyword list,

the procedure started from the beginning and was

repeated again. The complete list of the industry

classes created and their respective definition are

shown in Figure 7. The keywords used for each

industry class are given in the Appendix.

Figure 5: Ambiguous EDGAR search result for “Sprint”.

Step 3. SEC Classification. A Java program was

written to download information from the SEC

EDGAR system. The organization name was used to

search for the company. For 40,384 search requests,

2,732 entries could be found in the EDGAR system.

However, sometimes the same company has several

names, which resulted in more than one outcome for

the organization name. An example of such an

ambiguity can be found in Figure 5. Because there

was no reliable way to uniquely identify the correct

entry in such a case automatically, all entries with

multiple search results were eliminated which led to

1,706 remaining search results. Furthermore some

companies had no SIC code and were eliminated as

well. This resulted in 469 ASs that could

additionally be classified into industry groups.

5 RESULTS

5.1 Keyword Classification

Applying the method described above and using the

keywords shown in the Appendix to classify the

40,384 ASs, resulted in 22,786 or 56.42 % of

classified ASs. The industry class distribution based

on keyword classification only can be seen in Figure

6. According to this data, most frequently the

organizations that own ASs belong to the industry

classes Education & Research, Finance &

Insurance, ISPs & Networks, and Telephone &

Communications. This class distribution seems to be

intuitive: ISPs, telephone and IT companies as well

as universities have more incentives to register an

AS than for example a travel agency because ASs

classified into these categories are often related to

communications, but often also represent major

institutions that have a high tendency to own an AS

simply because of their size.

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Figure 6: Industry class percentages based on keyword

classification.

Of all clusters (apart from the generic Company

cluster) ISPs & Networks is the category that is most

frequent and accounts in case of both classification

approaches for around ten percent of all the

classified entities. This is an expected result since

ASs pertain to the communications business, and

offering Internet access is a key factor in this

particular business area. The categories Education &

Research and Telephone & Communication occupy

the second and third positions with 5.32 % and 5.24

% respectively. Therefore, even today, companies in

the area of Internet and information technology are

still overrepresented because of their particular

Internet affinity. A bit more unexpected, however, is

that financial institutions seem well represented

since the fourth position is taken by the Finance &

Insurance cluster. All other categories are smaller

with percentage values between 2.57 %

(Government & Military) and 0.07 % (Space).

However, a limitation of our results is that the

general Company cluster still encompasses 22.54 %

of the classified ASs. This fact and the remaining

number of unclassified ASs indicate that there is still

a potential for improvement regarding the

classification process. Yet it is questionable whether

it is possible to reach much better results with semi-

automatic classification approaches because of the

presence of non self-explanatory organization names

and acronyms such as NGM or EDP. Not only is it

difficult to classify those simply based on keywords,

it is also challenging to specify what kind of

organization they represent without further manual

and individual investigations.

5.2 SEC Classification

The industry-class frequency of organizations based

on an alternative classification that is solely based

on SEC data is shown in Figure 8. The industry

classes Construction & Manufacturing as well as

Figure 7: Industry classes with their definitions and

percentages based on the keyword classification.

Consulting & Management have the most

organizations with ASs. The classes Government &

Military, ISPs & Networks, IT & Internet Service,

IXPs, NIC, as well as Space have no ASs at all.

However, because not all companies are listed with

the SEC and in particular governmental institutions

and privately held companies are not registered, the

lack of representation of these classes is inherent.

With the help of the SEC data it was possible to

classify additional 116 ASs, which could not be

classified via keywords only (Figure 9).

Furthermore, the industry classes of 206 ASs could

be specified more precisely which had previously

been assigned to the Company class (Figure 10).

Most newly specified classifications were assigned

to the Construction & Manufacturing as well as the

Consulting & Management industry classes. Their

prevalence reflects the results of the SEC

classification.

By combining both classification approaches we

were therefore able to classify 22,892 of the 40,384

AS of the preprocessed list.

This accounts for 56.69 % of all considered ASs

that could be assigned to an industry class. The final

result is shown in Figure 11.

Cluster Definition Result Ratio

Address

ASs where no underlying organization is specified but an address,

where the AS itself, the underlying organization or its managerial

unit is located.

326 0.81 %

Company

Collecting bucket for those ASs which are hard to categorize based

on their organization name but at least can be identified as a

company.

9,104 22.54 %

Construction &

Manufacturing

Mostly building firms and manufacturers are part of this class. 97 0.24 %

Consulting &

Management

ASs related to advising and leading of a company. 139 0.34 %

Education &

Research

ASs related to learning und gaining of new insights such as schools,

universities, research facilities and networks as well as laboratories.

2,150 5.32 %

Entertainment &

Information

ASs which are for example related to television, gaming, radio or

publishing.

761 1.88 %

Finance &

Insurance

This class consists mainly of banks and insurance firms. 1,664 4.12 %

Government &

Military

ASs with an authority and military character as well as areal

territories such as cities and states are relevant for this class.

1,039 2.57 %

Healthcare

Next to hospital (district) related entities, this class contains

pharmaceutical firms.

695 1.72 %

IT & Internet

Service

ASs that are affiliated with online as well as offline IT services and

computer products. In general, this includes those firms which

provide a service or product that is based on the Internet or IT, but

which do not offer Internet access.

1,371 3.39 %

ISPs & Networks

Collects ASs of those organizations which offer Internet access or

provide the necessary infrastructure.

3,775 9.35 %

IXPs

This class collects all ASs which function as exchange point in the

Internet.

134 0.33 %

NIC

Contains those ASs which are “responsible for managing and

allocating Internet resources” [6].

390 0.97 %

Space Contains ASs of the area of astronautics. 30 0.07 %

Telephone &

Communication

This class contains (mobile) telephone providers and sellers as well

as general communication-based organizations.

2,118 5.24 %

Travel

Contains all ASs that are related to mobility and travel, such as

airports, train stations, hotels and travel agencies.

103 0.26 %

Trade & Transport

Collects ASs of the area of wholesale and logistics including apparel

and food.

232 0.57 %

Utilities

Organizations which provide electric power, water as well as other

basic materials; also services such as waste disposal, coal and mining

belong to this class.

256 0.63 %

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Figure 8: Industry class sizes based on SEC classification.

Figure 9: New classifications.

Figure 10: More precise SEC classification.

6 CONCLUSIONS

This paper proposed a classification approach for

categorizing ASs into detailed industry classes in

order to better understand the economic background

of the Internet structure. The industry classes are

inspired by the NAICS (2013), which had the effect

that an unprecedented level of detail regarding the

industry classes for classification could be achieved.

Data was mainly obtained from the CAIDA AS

Rank project as well as from SEC.

The classification of ASs into industry classes

based on their underlying organization revealed an

on-going strong dominance of telecommunication

Figure 11: Final classification using both keyword and

SEC data

(Note: It was possible to categorize an AS into

more than one industry class.).

and IT-related firms in the current Internet as well as

of large institutions such as banks and universities.

It was possible to classify 56.69 % of all ASs (after

preprocessing). Nevertheless, the amount of

unclassified ASs indicates that there is room for

improvement regarding the categorization process.

A refined and extended keyword selection process

could provide better results. Nevertheless, since

there is a non-negligible amount of ASs having

organizational specifications that are not self-

explanatory or acronyms, this would involve a

difficult challenge.

Some of our further explorative attempts to find

new ways for AS classification with the help of

clustering algorithms had limited success so far.

However, another possible route could be to apply

methods from Natural Language Processing (NLP)

to the AS data and also for analysing search results

from the Web for acronyms or other challenging

organization names.

Moreover, customers of the various ISPs cannot

be captured by the current method. It is often the

case that large Internet providers also represent

smaller customers who are not registered in the

organizational information of the ASs. Here,

studying the level of IP addresses could provide

further insights but will also involve complex

challenges.

Cluster Result Ratio

Address 326 0.81%

Company 8,898 22.03%

Construction & Manufacturing 198 0.49%

Consulting & Management 254 0.63%

Education & Research 2,155 5.34%

Entertainment & Information 771 1.91%

Finance & Insurance 1,685 4.17%

Government & Military 1,039 2.57%

Healthcare 698 1.73%

IT & Internet Service 1,371 3.39%

ISPs & Networks 3,775 9.35%

IXPs 134 0.33%

NIC 390 0.97%

Space 30 0.07%

Telephone & Communication 2,134 5.28%

Travel 104 0.26%

Trade & Transport 2,546 6.30%

Utilities 269 0.67%

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Various other classification approaches might be

feasible. In future work we will try to find other

valuable classification systems aiming to take an

even closer look at the composition of the Internet.

Furthermore, we will use our classification results to

further investigate the important players of the

Internet backbone as well as to assess their

economic interests and risks, at individual as well as

global scales. Moreover, we aim to derive

implications for Internet reliability and control

assessments as well as for security and privacy

analyses.

ACKNOWLEDGEMENTS

The authors thank Sebastian Dombrowski for his

programming work during parts of this research.

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APPENDIX

Cluster K eywords

Address avenue, building, flat, floor, strasse, gpo box, handelsweg, mcpo box, no., po box, road, street, suite(s), tower

Company associates, agency, a\.s\., bv, b\.v\., cjsc, co, co kg, companies, company, coporation, corp, corporation, d.o.o., de

c.v., enterprise(s), gmbh, inc, incorporated, l\.l\.c, limited, llc, llp, lp, l\.p\., ltd(a), organization, s.a. de c.v., s.p.a.,

s.r.l., sp. z o.o., srl, s\.a\., s\.l\., sa, sas, sl, trust, z\.s\.p\.o, zspo

Construction &

Manufacturing

architect(s), builders, building company, building society, construcoes, construction, constructora, constructors,

electronics, machine, manufacturer(s), manufacturing, producers

Consulting &

Management

beratung, business solutions, capgemini, consultance, consultancy, consultant(s), consulting, ernst & young,

management company, pricewaterhousecoopers

Education &

Research

.*universitaet, academic, academisch, colegio, college(s), desire2learn, ecole, education(al), fachhochschule,

forschungsgemeinschaft, forschungsgesellschaft, fraunhofer, institute, instituto, knowledge network, laboratories,

laboratory, labs, learning, mitre, physics, polytechnic, recherché, research, school(s), science(s), supercomputer,

supercomputing, univ, universidad, universidade, universitaet, universitaria, universitas, universite, universiteit,

universitesi, universitet, universiti, universities, university, univerzitet

Entertainment &

Information

advertising, bbc, bertelsman, book(s), broadcasting, entertainment, football, fun, game, gaming, library,

magazine(s), mcgraw-hill, media, marketing, medien, multimedia, news, newspaper(s), printing, publications,

publishing, radio, reuters, television, times, tv, weather, zdf

Finance &

Insurance

allianz, american express, asset management, assurance, banca, banco, bank, banka, banque, blue shield, capital,

credit, finance, e\*trade, financial, goldman, guggenheim, hsbc, insurance, investment, leasing, payment, real

estate, reinsurance, rental, societe generale, stock exchange, stonepeak, visa

Government &

Military

administration of, agency, air force, army, authority, board of, bureau of, city of(fice), committee, commonwealth

of, congress(ional), council, county of(fice), department of, dept, district of(fice), dod, embassy, federated states,

gov, government, house of, iles de, military, ministry, nato, navy, northrop grumman, parliament(ary), province

of, senate, state of, united nations, united states postal service, US geological survey

Healthcare bayer, blood, dental, drug(s), drugstore, elektromedizinische, emergency, health(care), hospital(s), johnson &

Johnson, klinikum, medical, medicine, medizinische, merck, novartis, pfizer, pfizerswitzerland, pharma(cy),

pharmaceuticals, pharmafarm, propharma, social security, transplant

IT & Internet

Ser vi ce

akamai, apple inc, computer hardware, computer products, computer science, computer service(s), computer

software, computer solutions, computer systems, content provider, content service provider, content solution(s),

data center(s), data corporation, data processing, data service(s), data solution(s), data systems, dell, fujitsu,

general electric, google, hewlett-packard, host, hosting, ibm, information systems, information technology,

internet service(s), internet systems consortium, it services, microsoft, neterra, network service(s), network

systems, oracle, othello, samsung, sap, schuberg philis, siemens, sony, sungard availability, thinktech, verisign,

web service(s), yahoo

ISPs & Networks aol, arcor, at&t, backbone, broadband, bt italia, cable network(s), cogent, comcast, connection(s), esnet, exatel,

fibernet, freenet, gts, iletisim hizmetleri, internet access, internet provider, internet service provider(s), internet

solution(s), isp, lattelekom-apollo, level 3, linxtelecom, netassist, netcologne, network access, network provider,

network service(s), network solution(s), networks, ntt america, optical network, prometey, qwest

communication(s), reseau national, reseau regional, retn, road runner, rostelecom, singtel optus, smartcity, sprint,

surfnet, swisscom, t-2, telecom, telekom, telia latvija, teo, time warner cable, towerstream, transit, true internet,

uzbektelecom, verizon, versatel, vimpelcom, west call, wireless

IXPs exchange point, internet exchange, internet exchange point, ix, ixp(s), link, open exchange, peering exchange

NIC afnic, american registry, apnic, arin, east-ukrainian, internic, network information center, network infomation

center, network information centre, nic, ripe ncc,

Telephone &

Communication

alcatel, bell canada, communication(s), e-plus, elisa, ericsson, lambdarail, mobile, motorola, nokia, o2, phone,

radiotelephone, rockefeller group, singtel optus, telecommunication(s), telecomunicaciones, telefonica,

telekommunikation, telekomunikacije, telekomunikacja, telekomunikasi, telecomunicazioni, telephone(s),

telianet, turkcell, vodafone

Trade &

Transport

amazon, apparel, clothing, coca-cola, fedex, food(s), logistic(s), logisticare, retail(ers), shaya magazacilik,

shipping, shoe(s), supply, trade, trading, transport(ation), wal-mart, wholsesale

Travel air canada, airline(s), airport, bahn(hof), boing, flughafen, klm, lufthansa, hotel(s), railway, reisebuero, resort,

travel, vacation

Space aeronautics, aerospace, astronomy, nasa, space administration, space agency, space research, space telescope

Utilities bp, coal, electric power, electricity, energy, farmer, farms, fiber, gas, mine, mining, offshore, petroleum, utilities,

utility, waste, water

WEBIST2014-InternationalConferenceonWebInformationSystemsandTechnologies

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