HERCMA 2009
THE USE OF ADAPTIVE ALGORITHMIC PROCEDURES FOR
COMPUTING ACADEMIC PERFORMANCE EVALUATION
MEASURES BY USING SEARCH ENGINES AND DATA BASES
Evangelia Lipitakis and Alexandra Lipitakis
Kent Business School, University of Kent
Canterbury, Kent CT2 7PE, England
Abstract
In this article a novel adaptive algorithmic approach combined with the singular
perturbation concept is proposed for the efficient solution of e-business and strategic
management problems. A characteristic e- business case study concerning the computation of
academic performance measures by using search engines and data bases is considered and it
is shown that an efficient solution can be obtained by using its corresponding adaptive
algorithm. These dynamic algorithmic schemes, by using the singular perturbation approach,
can be efficiently used for obtaining a (near) optimum solution of the considered e-business
case studies. Both the adaptability and compactness of the dynamic algorithms allow their
application for solving efficiently a wide class of e-service and strategic management
problems.
Key-words and phrases: adaptive algorithms, dynamic algorithmic schemes, ebusiness problems, e-publishing, e-publishing case studies, e-service strategy management
methodologies, strategy management (SM) methodologies
1.
Introduction
In recent years the rapid growth of scientific and technical knowledge has allowed
scientists, engineers, economists, social scientists and humanists to join in addressing
complex problems that must be solved simultaneously with deep appropriate knowledge from
different perspectives and multidisciplinary fields. Such problems of global importance
include a wide spectrum of applications in economic development, social inequality, disease
prevention, global climate change etc., which can be addressed through several
interdisciplinary advanced research topics, i.e. nanotechnology, bioinformatics, neuroscience,
genomics-proteomics, DNA computing etc. [9]
The fields of Computing, Informatics, Applied Mathematics, Engineering,
Economics, e-Business and Strategic Management have special commitment to
interdisciplinary work, with their researchers inter-collaborating in the design and
development of new methodologies, exploration of existing methods and practices, formation
of theories of on-line business and commerce, discovery of new advanced methods to extract
information from enormous data-sets, integration of library and archival sciences etc.
A thorough examination of the existing e-business and e-commerce literature reveals
the fact that it relies almost exclusively on several selected case studies and conceptual
frameworks. Few research studies use empirical data to characterize the Internet based
initiatives and indicate their impact on organization performance (Brynjolfsson et al, 2000
[4], Zhu et al, 2004 [51]). Furthermore, there is a lack of theory to guide the empirical work
(Wheeler, 2002 [49]), and an increasingly number of researchers argue that the literature is
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weak in making the linkage between theory and measures apart from subjecting proposed
measures for empirical validation for reliability and validity (Straub, 1989 [42]).
Towards these lines and in order to make such a linkage between theory and
measures, a new algorithmic treatment of e-business, e-commerce and strategy management
in digital information management methodologies, based on an adaptive algorithmic
approach, has been proposed by Lipitakis, 2005 [22], 2007a [23], 2007b [24], 2008 [26],
Lipitakis and Phillips, 2007 [25].
According to a recent McKinsey Global Institute research study, today’s business
leaders need to incorporate algorithmic decision-making techniques to successfully run their
organizations [25]. This exploratory study attempts to investigate and bridge the gap between
e-business strategy and algorithmic procedures.
In this study we show that suitable adaptive algorithmic procedures can be efficiently
used for solving a wide class of complex computational problems, including e-business, eservice and strategic Management problems. Furthermore, we point out that the selective
usage of both algorithmic and sp-concepts, i.e. the usage of adaptive algorithms in
combination with the dynamical choice of sp-parameters, can lead to their (near) optimized
solutions.
This research work is based on key-field concepts of three different sciences, i.e.
Computer Science (Adaptive Algorithmic Theory), Applied Mathematics (Singular
Perturbation Theory and Partial Differential Equations) and Management Science (Strategy
Management and e-business). The adapted basic concepts of the first two sciences are applied
for and used in connection with problems of e-business and strategy management.
2. The Algorithmic Approach and Singular Perturbation Concept
In this section we will synoptically present the important issues of adaptive algorithmic
approach and the singular perturbation concept for e-business problems and strategic
management methodologies in Digital Information Management (DIM) applications.
The Algorithmic Approach
The basic concepts of the adaptive algorithmic approach on e-business problems and
strategic management methodologies have been recently presented in [22, 23, 24, 25, 26]. It is
known that an algorithm can be simply defined as a finite set of rules which leads to a
sequence of operations for solving specific types of problems, with the following five
important characteristic features: Finiteness, Definiteness, Input, Output and Effectiveness, as
described in [18, 38]. The algorithms can be easily presented by the so-called ‘pseudoalgorithmic’ form, that is also called ‘pseudoalgorithm’ or ‘pseudocode’ in the case of
preparation computer programs/codes.
A psedoalgorithm/ pseudocode can be considered as an outline of a program, written in
a form that can easily be converted into real programming statements. In fact, everyone can
easily write a pseudocode without even knowing what programming language is going to be
used for the final implementation of his algorithm. A pseudocode consists of short English
phrases used to explain specific tasks within a program's algorithm should not include
keywords in any specific computer languages and should be written as a list of consecutive
phrases.
The main purpose of a pseudocode is not only to help the programmer to efficiently
write the corresponding code (program), but under certain circumstances (by using suitable
parameterized forms) it can be effectively used in solving a wider class of computational
problems apart of the original given problem.
In view of these facts the usage of the adaptive algorithms, with their previously
mentioned advantages and the additional important features of compactness, adaptiveness and
incorporation of singular parameters that allow the computation of (near) optimum solutions,
can be extended for the efficient solution of e-business and SM problems and in a wide area
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of corresponding applications, a part of which is presented in the references and bibliography
section [4, 20, 22, 23, 24, 29, 32, 36, 37, 41, 49]
The term ‘adaptive algorithm’ denotes here an algorithm which changes its behavior
based on the available resources. The adaptive algorithm functions provide a way to indicate
the user’s choice of adaptive algorithm and let the user specify certain properties of the
algorithm. The concept of adaptive algorithm is closely related to the corresponding concept
of ‘adaptive’ or ‘variable’ choices of the parameters (input elements) of the algorithm.
Adaptive algorithms are algorithms that adapt to contention, often have adjustable steps that
repeat (iterate) or require decisions (logic or comparison) until the task is completed, are
simple and easy to program. The general technique of adapting simple algorithmic methods to
work efficiently on difficult parts of complex computational problems can be a powerful one
in the algorithm design, evaluation and practice [23, 24, 25, 36].
The Singular Perturbation Concept
In the last decade research has been directed in the study of a class of
initial/boundary-value problems and the behaviour of the approximate solutions of the
resulting linear systems by considering a small positive perturbation parameter, affecting the
derivative of highest order, cf. [46, 27].
The singular perturbation (sp) parameters have been firstly used by Tikhonov [46] for
solving numerically certain classes of initial/boundary value problems. Following this
approach, a class of generalized fully parameterized singularly perturbed (sp) non-linear
initial and boundary value problems can be considered and the way that the sp parameters
variation affects their numerical solution can be studied [27].
In this article we show that suitable adaptive algorithmic procedures can be
efficiently used for solving a wide class of complex computational problems, including ebusiness and strategic Management problems. Furthermore, we point out that the selective
usage of both algorithmic and sp-concepts, i.e. the usage of adaptive algorithms in
combination with the dynamical choice of sp-parameters, can lead to (near) optimized
solutions. The applicability of the proposed adaptive algorithmic approach and sp-concept
methodology is demonstrated by considering a characteristic case study in E-Publishing
Business applications.
3.
Web and Scientific Communication, Web Search Engines and
Databases, Citation Analysis and Research Performance Evaluation
The evaluation of the academic research work, the assessment of the published
faculty members’ research papers and the overall research work performance measurement in
an academic environment are considered to be critical factors of major importance for the
improvement of services of academic institutions and research centers in the scientific
community and the society at large.
In the last decades several research searching engines/ machines and scientific data
bases have been derived and extensively used for research work assessment and retrieval of
bibliographic database elements and reference information. Such data bases and search
engines include the Google Scholar, Web of Knowledge, Social Science Citation Index
(SSCI), Academic Search Elite, AgeLine, ArticleFirst, GEOBASE, POPLINE etc.
In this study we will discuss some research performance measuring indicators, the
citation, research citation and the citation of research papers for an academic environment
using two Internet databases, namely the Google Scholar and Web of Knowledge.
It is universally acknowledged that the discovery and usage of the Internet and World
Wide Web (WWW) or simply Web are considered to be the major scientific and
technological breakthroughs of the last decades with still unpredictably consequences on the
evolution of knowledge and culture of the human race.
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The Web is a dynamic, distributed and interactive hypermedia environment, where
almost anyone can be an information producer and consumer. Members of the scientific
community can create their symbolic presence by constructing individual home pages
showing their scientific and research work, published research papers, projects, personal
information, and to use efficiently the linkage to other selected sites. The Web can be used as
a valuable resource space for scientists and researchers in both producer and consumer
perspective (Cronin et al, 1995 [7]). It has been said that the Web could result in the
emergence of a universal bibliographic and citation database ‘linking every scholarly work
ever written-no matter how published- to every work that it cites and every work that cites it’
(Cameron, 1997 [6]).
The Web can be used both as a publishing and conversational medium allowing
scholar interaction and signaling, i.e. scientists can post, publish, review papers on the Web,
discuss issues and debate positions, put forward informally new ideas for discussion,
exchange ideas and opinions on scientific matters etc.
The Web can afford both economics of scale (larger markets, lower distribution costs)
and economics of scope (global reach, diversity of audience), and can efficiently use a great
variety of digital information objects, such as plain texts, images, data, video contents etc, in
an effective manageable and easily reconfigurable manner which cannot be compared by any
traditional print production tools.
The Web is considered to be the largest and richest contemporary information
repository. The most common access routes to this valuable source are the search engines.
The important role of these search engines is often limited to the retrieval of lists of
potentially relevant documents. The critical task of analysis of the returned documents and the
identification of knowledge of interest is usually left to the users.
It should be noted that the citations are generally subject to several forms of errors,
such as common typographical errors in the source papers, errors due to some non-standard
reference formats, errors in GS parsing of the references etc. Citations of several publication
works can be complete, incomplete, completely missing according to inconsistent treatment
of books, conference proceedings etc. and that may lead to inaccurate citation counts in
certain disciplines (Harzing, 2008 [16]).
Citation analysis is usually performed to evaluate the impact of scientific collections,
i.e. research journals and conferences, publications and scholar authors during promotion,
tenure, merit pay decisions, hiring processes etc. The ranking of scientific publications can be
performed by using several ranking algorithms. The ranking algorithms that are used in
bibliometrics can be classified into the following categories:
(i) the collection-based ranking algorithms (ISI Impact Factor algorithm, Garfield, 1972
[12] and 1996 [14], SCEAS algorithm, Sidiropoulos et al, 2005 [39] and
(ii) the publication-based ranking algorithms (PageRank algorithm [Google Scholar [53,
54], HITS algorithm, Kleinberg, 1999 [17]). Both ranking algorithm categories use citation
graphs and graph nodes in order to represent the total number of citations that point from one
collection to another (Sidiropoulos et al, 2006 [40]).
It is reported that citation analysis in conjunction with advanced visualization
techniques will help to understand the structure of individual fields and it will be particularly
useful for emerging and rapidly developing top research areas, such as biotechnology and
nanotechnology (Leydesdorff, 2007 [21]).
It should be noted that the traditional citation analysis can provide indicative
information about the intellectual influence of researchers, but it is unable (as Web can do) to
provide any indications about the scientist’s ideas, thinking and general professional presence.
The evaluation of scientific research work is an important problem of great
theoretical and practical importance. The quality of the published papers is closely related to
the analysis of the citations received, that enables the approximate measurement of several
academic performance concepts, such as the quality of research work, the academic
reputation and scientific influence of researchers.
It should be pointed out that in all the research performance evaluation models the
citation reception is considered as a positive factor (whatever is the nature of citation).
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A well-known evaluation measurement indicator of the importance of a scientific
journal is the so-call Impact Factor computed by the ISI and introduced by E. Garfield, 1972
[12]. Note that the scientific community does not agree unanimously about the effectiveness
of this measurement, that is a metric of popularity but does not capture certain evaluation
criteria such as the importance, prestige etc. (Bollan et al, 2006 [3]). A brief description of the
Impact Factor and its computation can be found in [26].
An interesting problem is the evaluation of scientific research journals, authors,
papers, fields, academic institutions and research centers. The importance of a paper should
be determined by certain factors, such as the quality of paper and its author, the quality of the
citations from other papers, the importance of journal, the importance of author(s) and coauthors. A paper is considered to be important if it is published in important journal, is written
by important author(s) and receives citations from other important papers.
An integrated multi-class research evaluation model, that considers all the above
described subjects with appropriated weight coefficients, can be efficiently used by taking
into account the classes of citations, authors and journals respectively (Bini et al, 2007 [2]).
This model for evaluating papers, authors and journals is based on citations, co-authorship
and publications.
The rapid development of Web search engines followed by their subsequent
evaluation that aimed mainly to inform the Web users in their choice of search engines and to
the development of search algorithms and search engines. Among the most commonly used
commercial search engines are: the Google (www.google.com), Alta Vista
(www.altavista.com) and Teoma (www.teoma.com).
Google is the largest publicly available search engine (Notes, 2002 [35]), using the
PageRank algorithm, a new generation of link-based ranking algorithm.
Alta Vista is one of the oldest Web search engines, the largest search engine by
database size (Notes, 2002 [35]) and the most commonly used search engine for data
collection for Webmetrics studies (Vaughan et al, 2003 [47]).
Teoma is the youngest of the previous search engines, using the Subject Specific
Popularity (SSP) algorithm, a unique ranking algorithm for the determination of quality and
authority of a site’s content (Teoma, 2002 [44]).
Google Scholar is an Internet based search engine designed to locate scholarly
literature, including peer reviewed papers, abstracts, books, book chapters, theses, preprints
and technical reports from all broad areas of research, available in the Web or occasionally in
print format only. The results are presented in a relevance ranked format (Myhill, 2005 [33]).
It should be noted that the great majority of academic literature can be found in the so-called
hidden Web. The Google Scholar includes a wide range of appropriate resources, but cannot
include all the partner publishers offerings and often misses a considerable number of quality
resources that are usually accessible to academic researcher through institutional
subscriptions. Its searchability can be greatly improved by using advanced search pages with
standard criteria such as authors, publishers, publication dates etc.
The GS offers the options of Simple Search and Advanced Search. The Simple Search
is very fast, can be used for searching on authors names in several ways and uses the
stemming technology, which retrieves documents with word variations based on the
keywords entered. By using the GS advanced search a number of search options for articles is
available. The GS keyword searching includes searching by all words, exact phrases, at least
one of the words, without the words, and where the words occur in the considered documents
(Burright, 2006 [5]).
The Google Scholar is a free resource with very basic and familiar interface, can
serve as alternative to several metasearch engines, such as MetaLib and WebFeat
and can be used more expensive citation indexes, i.e. Scopus of Elsevier Publishers and Web
of Knowledge of Thomson Scientific. Although the reviews and critiques of GS about its
contest, search engine, interface, citation counts, the academic scholars, attracted mainly by
its simplicity and familiarity of GS interface, are increasingly use it and in substantial
numbers (Neuhaus et al, 2008 [34]).
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It should be noted that the Google Scholar is considered to be the largest new multidisciplinary tool that can be used for accessing and measuring hidden citations, especially
from non-journal web documents, which cannot traced by conventional bibliographic and
citation databases. GS contain citation information from many publishers, by doesn’t use any
quality control procedure for its collection of journal publications and includes substantial
coverage of non-journal documents, such as research papers, conference papers, books, book
chapters, theses, research/technical reports etc. (Kousha et al, 2008 [19]), (Google Scholar,
2006 [52, 53]).
A universal Internet-based bibliographic and citation database that would link every
scholarly work ever written, no matter how published, to every work that it cites and every
work that cites it, has been proposed by Cameron, 2003 [6]. The usage of this database could
revolutionize several aspects of academic communication, such as literature research,
evaluation of research work, keeping current with new literature, choice of publication venue
etc. [6].
The commercial Scopus service was introduced in 2004 by Elsevier Publishers,
while the open access database Google Scholar in beta version followed. The Web of
Science (WoS) database is also a commercial database based on a family of ISI (Institute of
Scientific Information) citation indices.
The Scirus database of Elsevier Publishers [55] it is reported that have the best
science, technology and medicine coverage on the Web, with more than 200 million specific
pages indexed (Felter, 2005 [10]). Scirus is considered as a good alternative to GS and lists its
content sources and 13 million patents from Europe, USA and Japan, while Elsevier tries to
make more content available by negotiating with other scientific publishers (Scirus, 2005
[55]).
In the framework of the objective assessment of the research work of a faculty
member, the promotion committee members thoroughly examine the impact and quality of his
scholarly/ research publications, the subjective opinions of peers, tenure and several other
decisive factors including the citation analysis. In relation to this faculty members try to
identify as many citations to their published research work as possible to provide a
comprehensive assessment of their publication impact on the academic, scientific and
professional communities. The ISI citation database, a well known source for locating
citations, appears to have several limitations in the full coverage of citations to a faculty
members’ research work.
It should be noted that the WoS is the portal used to search the three ISI citation
databases, namely Arts & Humanity Citation Index, Science Citation Index, Social Sciences
Citation Index.
GS has been mainly introduced as a research oriented web search machine providing
full text searching of relative material and extracts formal citations from the material. These
capabilities show that GS can act both as a citation index and a search engine. Furthermore,
GS extracts citations to print materials that have been referenced in web publications. In this
case the database provides access to certain material in the conventional print environment
and to web based material.
GS has become widely used for searching research oriented material, it has proved to
be a useful reference tool for academic librarians, White 2006 [50] and on several occasions
contained citations to significant resources that were not covered by the Science Citation
Index database, Kousha & Thelwall 2006 [19].
The cybermetric analysis of research publications can be made, apart of the GS, by
using Web of Knowledge (the web interface to ISI’s citation databases) and SCOPUS (The
Elsevier citation database) [52].
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4.
Search Engines Literature Review
In this section we synoptically comment on the evaluation and research performance
measurements of Search Engines for World Wide Web.
In a recent paper of Bettencourt and Houston, 2001 [1] the impact of paper method
type and subject area on paper citations and reference diversity in certain journals have been
studied. The authors combine a content analysis of a selected sample of 561 Marketing
research papers with citation analysis and reference analysis on article citation rates and
reference diversity. Several experimental results for the impact of method type and subject
area on reference diversity of published papers of Marketing Journals (Journal of Marketing,
Journal of Marketing Research, Journal of Consumer Research) are presented and several
limitations (distinct sampling time periods, transfer of knowledge methodologies, usage of
other methodologies) of the proposed research methodology are reported [1].
A set of new measurements, adapted from the concepts of recall and precision, for the
evaluation of Web search engine performance have been proposed by Vaughan, 2003 [47].
These concepts are commonly used criteria in the evaluation of traditional information
retrieval systems. The precision is the proportion of retrieved documents that are relevant,
while recall is the proportion of relevant documents that are retrieved (Voorhees et al, 2001
[48]). The two ranking measurements have been proposed as counterparts of traditional recall
and precision and correspond respectively to the quality of result ranking and the ability to
retrieve top ranked pages [47].
Some other new measurements have been also derived for the evaluation of search
engine performance stability, which is proposed to be used as an evaluation criterion [45]. It
is noted that if a search engine is not stable, then the results obtained from the search engine
may not represent the general performance of the search engine. Furthermore, the testing of
search engine stability can provide information for Webmetrics research, i.e. for users of Web
search engines who collect data [47].
The research study proposed a set of measurements for the evaluation of performance
stability and the commercial search engines Google, Alta Vista and Teoma have been selected
in order to examine the stability of search engine performance over time. The three stability
measurements were:
(i)
the stability of the number of pages retrieved,
(ii)
the number of pages among the top 20 retrieved pages that remain the same
in two consecutive tests over a predetermines short period of time and
(iii)
the number of pages among the top 20 retrieved pages that remain in the
same ranking order in two consecutive tests over a predetermines short period
of time [45].
The concept of the invisible web and its implications for academic librarianship has
been presented in Devine et al, 2004 [8]. The majority of the Internet and Web users,
including both faculty members and students, are often aware of the limitations of standard
search engines. A guide to tools that can be used to mine the invisible web and the benefits of
using the invisible web to promote interest in certain library services are discussed. Several
suggestions for incorporating the invisible web in reference work and library promotion are
given [8]. The authors report several facts associated with the invisible web, such as

The size of Invisible Web (IW) is about 500 times larger than the surface web
(SW),

IW contains approximately 550 billion individual documents as compared with the
SW’s one billion

IW is the largest growing category of new information on the web

The greatest part of IW (about 95%) is publicly accessible information

More than 50% of IW resides in topic specific databases
Several characteristics examples of the IW content are given, such as database content,
real time information, subscription or fee based services, sites requiring password access or
registration for use, sites with no-index protocols etc. [8].
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A novel approach aiming for automatic ranking of scientific conferences using digital
libraries, such as the Science Citation Index by the Institute for Scientific Information and
CiteSeer by the NEC Research Institute, has been presented by Sidiropoulos et al, 2005&
2006 [39, 40]. A web-based system that has been constructed by extracting data from the
Databases and Logic Programming (DBLP) website of the university of Trier is presented and
a new citation metrics for ranking scientific collections is used [39].
An extensive overview of the two major technological changes in academic
publishing during the last decades, i.e. the development of Bibliometrics and the creation of
the Webometrics, a modern rapidly growing branch of Bibliometrics has been presented by
Thelwall, 2007 [45]. Both changes are related to the measurement of science. Bibliometrics
encompasses the measurement of properties of documents and related processes by using
special techniques such as citation analysis, frequency analysis, co-word analysis and
document counting. i.e. number of publications by an author, research group, academic
institution or country.
After the development of Science Citation Index (SCI) by Garfield, 1979 [13], two types
of bibliometric application have been created: (i) the evaluative bibliometrics with main
purpose to assess the impact of scholarly work (comparison of relative research contributions,
research funding) and (ii) the relational bibliometrics, aiming to the relationships within
research (new research fronts, structure of research fields etc.).
In a recent article of Leydesdorff, 2008 [21] some explanations for the use of citation
indicators in research and journal evaluation are presented. It is reported that the evaluations
using publication and citation counts have become standard practices in several research
assessment exercises and evaluations in academic environments.
The US National Science Foundation (NSF) recently launched new programs for the study of
sciences and innovation policies
http://www.nsf.gov/pubs/2007/nsf07547/nsf07547.htm
while the Seventh Research Framework Program (FP7) includes the science indicators in its
research announcements [21].
In the basic question ‘can journals and journal rankings be used for the evaluation of
research?’, the author points out that Garfield’s original purpose when created the impact
factor and the Journal Citation Reports was to evaluate journals and not to evaluate research
work! The impact factors can provide a summary statistics that allows the ranking of research
journals. The statistics however are based on the means of a highly skewed distribution and
the context of the measurement is of great importance [19].
The use of Google Scholar as a cybermetric tool and methodology issues in obtaining
citations counts for academic institutions has been presented by Smith, 2008 [41]. The GS is
used to compare the web citation rate of New Zealand universities with the results of New
Zealand’s performance based research funding (PBRF) research assessment exercise.
Two methodology problems occurred with GS have been considered, i.e.
(i)
the case of identification of a work related to a university, GS does not have
an institutional name field
(ii)
the case that a total citation count is obtained, GS displays only a citation
count for each item ,and allows only the first 1000 hits to be displayed from a search.
5. Adaptive Algorithms for computing Citations
In order to describe analytically the computational procedure that has been used in
our experimental research work for the computation of citations of KBS Faculty members for
a certain number of publication types in the period 1980-2007 by using specific Search
Engines [28], we present the corresponding algorithmic procedure in its ‘pseudoalgorithmic
form’ or ‘pseudoalgorithm’. This presentation has the advantage that the several stages (stepby-step) of the algorithmic procedure can be easily understood and can be also presented by
corresponding flow charts [28].
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It should be noted that an algorithm is simply defined as a procedure (a finite set of
well-defined instructions) for accomplishing some task which, given an initial state, will
terminate in a defined end-state. Alternatively, an equivalent definition can be used, i.e. a
step-by-step problem solving procedure, especially an established recursive computational
procedure for solving a problem in a finite number of steps. The algorithms posses five
important characteristic features, i.e. finiteness, definiteness, input, output, effectiveness, with
the additional advantage of adaptivity, Knuth, 1968 [18], Sedgewick, 1984 [38].
In the following we present the algorithm COCISE, for the COmputation of CItation
by using a specific Search Engine under certain hypotheses.
Algorithm COCISE (PT, TI, AU, NMAX, CIT)
Purpose: This algorithm computes the number of citations and related publication
information by using a specific search engine and available databases
Input: Publication Type (PT), Publication Title (TI), Author(s) (AU), Max number of
publication types (NMAX)
Output: Citation Information Table (CIT)
Computational Procedure
Step 1: Determine the max number of the original publication types
Step 2: Select a group of data (i.e. author(s), title, year of publication etc)
Step 3: Enter the selected (primary) data to the specific search engine for
processing
Step 4: Check the resulting output
Step 4.1: if there are not any resulting records, then
manipulate, add or select the proper secondary data in order to determine the
required output
Step 4.1.1: continue the iterative procedure until you get the
acceptable result
Step 4.1.2: if there is no such result, then update properly the
database and print a proper message, i.e. ‘no records found’
Step 4.2: else, check the results for the matching output/ record
Step 5: Check the resulting output.
Step 5.1: If the output is an acceptable one, then
Step 5.1.1: print the required information, i.e. number of citations,
related references etc.
Step 5.1.2: Update the database
Step 5.2: else, make the necessary corrections and update the corresponding
correction columns in the database
Step 6: if the maximum number of the original publication types has been
exceeded, then
Step 6.1: print an appropriate message and
Step 6.2: Form the final Citation Information Table (CIT)
Step 7: else, go to step 2 and continue the iterative procedure
The algorithm COCISE describes step-by-step the computational procedure for
computing the number of citation and presenting related publication information by using a
specific search engine and available databases.
It should be noted that the computational steps of the algorithm COCISE can be
further refined in order to give a more detailed version of the computational procedure. The
corresponding algorithms for computing the number of citation and presenting related
publication information by using the Google Scholar and World of Science search engines
can be derived in a similar manner.
The proposed adaptive algorithmic procedures in combination with the singular
perturbation approach (i.e. use of sp-parameters) can be efficiently used for the numerical and
algorithmic modelling of the computation of Citations, leading to their corresponding (near)
optimized solution.
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A (near) optimized algorithmic scheme of an Academic Performance Evaluation
model problem by using the Citation system and singular perturbation parameters approach
[23] can be derived by using the adaptive algorithm COCISE-OPT , i.e.Algorithm COCISEOPT-1 (εPT PT, εTI TI, εAU AU, NMAX, εUF OCIT), where the sp-parameters εPT, εTI, εAU
affect the input parameters of the algorithm COCISE-1 of the previous section and εUF (the
uncertainty factor parameter) affects the output.The algorithm COCISE-OPT-1 describes
step-by-step the computational procedure for computing the optimized number of citation and
presenting related publication information by using a specific search engine and available
databases.
6. Experimentation and Numerical Results
In this section we present some numerical results related to the application of GS and
WoS in the KBS academic environment.
Following the research methodology used by Meho and Yang, 2007 [30, 31], we
present general information about specific elements of Databases and tools used in the
research study [28], such as breadth, depth, subject coverage, citation browsing and searching
options, analytical tools, downloading and exporting options of the two sources.
Web of Science (WoS)
Google of Scholar (GS)
Breadth of Coverage
36 million records (1955- ),
8700 titles (190 open access
Journals & hundreds conference
Papers)
Over 30 document types,
Unknown number of sources,
Unknown number of records
Depth of Coverage
ISI Citation Databases:
SSCI (1956- )
AHCI (1975- )
SCI (1900- )
Unknown
Subject Coverage
All Subjects
All Subjects
Citation Searching Options
Cited Author,
Keyword & Phrase Searching
Cited Year,
Limit-Search Options include:
Cited Work: (requires
Author, Publication, Date,
Journal, Book, Conf. Paper
Subject areas etc.
Title in which the work appeared)
Citation Browsing Options
Cited Author
Cited Year
N/A
Analytical Tools
Ranking by Author, Publication
Year, Source Name, Subject Category,
Institution Name, Document Type
N/A
Downloading and Exporting options
to Citation Management Software
Yes
Yes
Table 1: Comparisons of GS and WoS Databases and Tools used in this research study. Note
that ISI=Institute for Scientific Information, SSCI=Social Science Citation Index, AHCI=Arts
& Humanities Citation Index, SCI=Science Citation Index.
In the following Table a comparison of GS and WoS concerning the citation coverage
of journal articles of KBS researchers.
10
Publication
Types
Books
Book
Sections/Chapte
rs
Conference
Papers
Conference
Proceedings
Edited Books
Journal
Articles/Papers
Reports
(research &
technical)
Unpublished
Work
Web Pages
TOTAL
No of
Pubs
found
in
WoK
Number
n
19
n%
1.57
No of
Pubs
found
in GS
11
109
8.99
64
58.72
479
7.48
330
27.23
154
46.67
399
2.59
29
12
2.39
0.99
14
8
48.28
66.67
58
313
4.14
39.13
593
48.93
548
115
9.49
63
54.78
319
5.06
4
1
1,212
0.33
0.08
100
3
1
866
75.00
100
71.45
18
1
7,600
6
1
8.76
292
% GS
57.89
92.41
292
%
WoK
49.24
49.24
No of
Citation
s found
in GS
405
No of
Citation
s found
in WoK
5,608
GS
Citation
Per
Output
36.82
1,519
1,519
10.23
Table 2: WoS and GS citation count by KBS Publication type (1980-2008).
The results of Table 2 show that among Google Scholar and Web of Knowledge, the
former offers the most comprehensive citation coverage of journal articles. In specific, GS
includes records for 548 of 593 articles. This number equals to 92.41% coverage of the total
of journal articles that KBS has published. Web of Knowledge provides coverage for 292 of
593 articles which corresponds to 49.24%.
In the following Figures 1 and 2, the percentages of KBS faculty member
publications per document type and the KBS authors academic ranking are respectively
presented.
KBS Member Staff Publications per Document Type in
Percentages 1980 - 2008
0%
9%
0% 2%
Books
9%
Book Section
Conference Papers
27%
Conference Proceedings
Edited Books
Journal Articles
Reports
50%
1%2%
Unpublished Work
Web Pages
Figure 1: KBS faculty members’ publications per document type
11
WoK
Citatio
n Per
Output
5.20
5.20
KBS Author Ranking in Percentages
5%
5%
40%
Professors
Senior Lecturer
Lecturer
Reader
35%
Other
15%
Figure 2: KBS Authors academic ranking
Further statistical analysis and numerical results can be found in [28].
7. Conclusions and Future Research Work
The adaptive dynamic algorithmic approach and the singular perturbation concept
have been applied for solving efficiently several e-business problems.
A characteristic e- business case study concerning the computation of academic
performance measures by using search engines and data bases has been studied and its
corresponding adaptive algorithmic scheme has been presented. Furthermore, the adaptability
and compactness of the proposed algorithms through the choice of singular perturbation
parameters can lead to an (near) optimum solution of the considered e-business case studies.
The choice of the singular perturbation parameters, leading to efficient solutions, is
an interesting subject of future research work. The proper choice can be related to quantitative
and qualitative nature of the input parameters (data) of the given problems and their
corresponding dynamical algorithms can lead to (near) optimum solutions.
The use of adaptive algorithms for academic research performance evaluation, the
study of several academic research evaluation models by using advanced search engines and
data banks, and the usage of universal Internet based databases for academic communication
and transfer of knowledge are some very interesting related research topics for future research
work.
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