The Evolutionary Process Of IT Concept Words: A Case Study .
2016 Proceedings of PICMET '16: Technology Management for Social InnovationThe Evolutionary Process of IT Concept Words: A Case Study on BigdataRieko Kataoka, Naoshi Uchihira, Yasuo IkawaJapan Advanced Institute of Science and Technology, School of Knowledge Science, Ishikawa - JapanAbstract--In information technology (IT), new concept wordsappear every few years and affect the business environment. Inseveral cases, the core technologies and architectures haveremained the same despite minor changes in concepts. Forexample, grid computing is the forerunner of cloud computingand bigdata is now regarded as a part of the Internet of Things(IoT). The trend in concept words reveals an evolutionarypattern.In this study, we applied a text mining approach to analyzeall the articles published in several popular IT magazines in theperiod of 2002-2015. This analysis revealed a gap between cloudcomputing and bigdata in the evolutionary process of IT conceptwords. An evolutionary model was identified that reached cloudcomputing, indicating that another episode of evolution mightstart from bigdata. We focused our analysis on the evolution ofprevious major concept words and examined emerging concepts,which reveal a trend from a human-oriented to amachine-oriented world; the former world is characterized byadvancements in social networking and the latter is based onadvancements in artificial intelligence. As a result of thisanalysis, we can determine a turning point in concept evolution,i.e., the change from computing series to data iledinterpretation of concept evolution.I. INTRODUCTIONIn information technology (IT), new concept wordsappear frequently and affect the business environment. Inseveral cases, the core technologies and architectures haveremained the same despite minor changes in concepts.The most popular concept word of the year is oftenadopted as the main theme at trade shows. At every cornersand booths, the products related to the theme are displayedand seminars, which have the theme in their title, are held asan annex to the trade show. Such concept words aredetermined for reasons pertaining to business marketingrather than technology. These signboards attract bothcompanies and people to the exhibition. For example, “SaaS”(Software as a Service) was the trending word in 2008 and itwas succeeded by “cloud computing” and “bigdata” in 2010and 2012, respectively. Currently, “IoT” has become popularand is frequently used in articles on the Internet and innewspapers. These concept words quickly become popularand are spread beyond the IT industry to the general public.Foster [1] compared multiple aspects of both cloud andgrid computing and concluded that cloud computing was nota new concept; in fact, it was derived from the theory of gridcomputing and the theory’s relationships with previoustechnologies. According to the definition of cloud computing1,1The definition in Special Publication 800-145 NIST (the United StatesNational Institute of Standards and Technology).one of the service models of the cloud includes SaaS.Therefore, cloud computing can be considered a mererephrasing of SaaS because its concept existed previously.Bigdata and IoT are closely related to cloud computing.Although not a part of cloud computing, they came intoexistence as a result of the development of cloud computing.In our study, we focused on the following questions. Whyis a signboard selected at a given time and replaced shortlythereafter? How does the new concept differ from otherconcepts? What aspects of a concept have been inherited anddeveloped? By verifying and systematizing the processstarting from bigdata, in addition to an analysis from gridcomputing to cloud computing, does the pattern of conceptdevelopment become clearer?We presented our previous work at PICMET2014 [2]. Inthis previous study, we analyzed all the articles in ITmagazines published between 2002 and 2012 and organizedthe process of concept evolution from technical words toservice words. With the case study of cloud computing, wecreated an evolutionary model. As a subsequent case study,we expanded the period of analyzing article data up toSeptember 2015. In this series of studies, we investigated theIT keyword’ transition at a wider scale and added moredetails to the evolutionary process of IT concepts.The purpose of the present study is to identify theevolution of a concept from a technology to a service byobserving the IT keyword transition in the case study ofbigdata. Further, we consider the results of this investigationfrom the viewpoint of knowledge science. In other words, theevolution of a pattern of IT keyword can be detected byanalyzing and organizing the evolutionary process from gridcomputing to cloud computing. After collecting andgeneralizing these patterns, we will be able to formulate amethod to measure the possibility of new concepts byobserving their evolution patterns in the future. Moreover, wewill be able to evaluate whether a new concept can bedeveloped further or will disappear without further evolution.We approached our research via analyzing the case studies ofcloud computing and bigdata. For this, we applied textmining techniques to analyze articles from popular biweeklyIT magazines in Japan (the background and structure of ourcase studies are described later in this paper).The term “concept” is defined as the generalized meaningof a phenomenon that comprehends, abstracts, andgeneralizes common items of a certain matter and plays a partin categorizing, identifying, and classifying actual events andrelationships between such matters. Keywords are themanifestations of these major concepts.IT keywords from all articles published between 2002 and2015 were analyzed using our text mining application. In the1983
2016 Proceedings of PICMET '16: Technology Management for Social Innovationcase of the IT industry, almost all concepts originated in theUnited States. The purpose of our research was not to studythe technical growth of concepts but to identify the trends ofa concept by tracking the transition of keywords. In this paper,we present our analysis results from text mining and itsimplications.II. LITERATURE REVIEWIn this section, we review previous studies in relatedfields.A. Text mining case studiesFirst, we review the literature focusing on topic transitions.There are many case studies that have analyzed documentsand articles on the Internet using text mining.Moriwaki [3] understood that topics change with time andtried to identify the consumer trend transition by the numberof appearances of a topic. Moriwaki chronologicallydisplayed the number of appearances of keywords andconcluded that a topic transition could be detected only bytracking several words with high rates of appearance.Moriwaki offered a standard rule to judge the transition bynumber; however, this did not include the time scale of thetransition.Shirai [4] insisted that keywords should be selected inadvance to obtain the trend information from text data andmaintained the text mining environment by selectingkeywords related to each purpose. He demonstrated a methodto select specific words from nouns in the text.For her selection of keywords, Okuwada [5] used themethod suggested by Shirai and then sorted the keywords bycategory. Subsequently Okuwada suggested another methodto analyze text data via free writing.Yamamoto [6] mapped the relationships of the keywordsin specific technology areas by applying text mining to patentand paper data. Yamamoto created a correlation betweenkeyword groups and years via a mapping tool. However norelationship from year to year was determined.In addition, there are multiple precession documents thatdescribe the method of analyzing text data using text mining;however, there are no descriptions of IT keyword transitionsfor time scales of more than 10 years.B. Text mining techniquesGenerally text mining techniques are not sufficientlyprecise because text mining is different from other softwareapplications and it is not possible to obtain an effective resultwithout sufficient knowledge of text mining. This is why datascientists are needed to handle these techniques.Nasukawa [7] understood that the selection of thetechnique in text mining applications is important andsuggested a procedure to analyze datasets using text miningin three steps, namely, “the trial phase,” “the core phase,” and“the application phase.” However, his procedure shows onlya conceptual framework, therefore, a detailed discussion isneeded within this framework when a real case in analyzed.Considering these factors, we attempted to analyze text datausing the framework offered by Nasukawa and accordinglyadopted it for this case study.Tsumoto [8] insisted that several new concepts appearevery year but only a few of them remain depending on thenumber of times the concept was adopted in documentsreleased to the public. Tsumoto analyzed technology trendson the basis of the frequency of appearance of technical termsin medical care research dissertations. Tseng [9] studiedpatent application documents and detected several importantresults by using text mining. Patent application documentscan be configured using a selection rule because theycomprise structured information. Data can be automaticallycollected using such selection rules even though this was aslow, manual operation until recently. By studying theemerging patterns and connections between patents, thecriticality or degree of influence of each patent could begrasped.The literature cited above describes the methodology thatdescribes the procedure of fitting the application software oftext mining to domain-structured data, such as patentapplication documents, and fetching information. Thisapproach is not applicable to text data, which includesunstructured data.C. Theory of concept trendsWe focused on previous studies on the relationshipbetween concepts. Shirota [10] affirmed that bigdata is akeyword that followed cloud computing and it arose from thepopularization of cloud computing. According to Villegas[11], cloud computing originated from grid computing. Foster[1] compared multiple aspects of both cloud computing andgrid computing and concluded that the cloud is not a newconcept; rather, it is a result derived from the theory of gridcomputing and the theory’s relationships with previoustechnologies. Even though these concepts include severaldifferent aspects of business models and security, the vision,architecture, and basic technologies are the same. Youseff[12] reported that the cloud evolved by converting multipletechnologies such as grid computing, SOA (service-orientedarchitecture), and virtualization and then subdivided cloudcomputing into five layers to discuss the meaning of itsexistence.SaaS was included as one of three service models in thedefinition of the cloud computing by the National Institute ofStandards and technology (NIST).There are multiple studies that describe the relationshipbetween two concept words. In this case study, we identifythe transformation of concept words that occurred during aperiod of more than 10 years as evolution and confirm theirrelationship.Several studies have focused on terminology trends fromthe viewpoint of management consultants. Giroux [13]expressed that from this viewpoint, concept labeling is notbased on mere interest but on the change and progressive1984
2016 Proceedings of PICMET '16: Technology Management for Social Innovationelements of society. Alvesson [14] noted that knowledgemanagement could be sorted and classified by determiningthe type of concept because knowledge and management areconsidered to be combined concepts. Previously, knowledgeand management were recognized as two different concepts.Yamamoto [15] arranged commodity sorting by includingintangible commodities along the following two axes: (1) thesource that generates use and (2) the movement of theproprietary of the source that generates use. Our previousstudy that we presented at PICMET2014 verified theconcepts of grid computing, SaaS, and cloud computing usingYamamoto’s sorting method. The meaning of a phenomenonchanges from a tangible commodity and the informationproduced by a technology to the service being an entity. Inother words, the character of the concept moves from beingtechnology-related to service-related.In this section, we reviewed previous studies in relatedfields. To conclude our literature review, we note that there isa description of IT keyword transitions for time scales ofmore than ten years has not been given yet. In the nextsection, we identify the transformation of concepts as anevolutionary process of IT concept words and attempt toconfirm the relationships between IT concept words.III. ANALYSIS OF CONCEPT TRANSITIONBecause of popularization on the Internet, various textdata such as the technical information of patents and papersand the mail and twitter data that people post daily arespreading and expanding. Consequently, many documents ordata cannot be used effectively and may be thrown away in ashort period of time. Komoda [16] judged that it wasimpossible to examine this enormous amount of documentdata; however, we have been able to extract and effectivelyuse information from these sources using text mining. Textmining is not a tool where an effective result can be providedby merely inputting document data. Text mining applicationsare unlike normal application software. The analysis resultdepends on how people direct the analysis and interpret theoutput.In this section, we illustrate a technique to extractknowledge with time scale information from enormousamounts of document data by effectively using text mining.We develop a technique not only to obtain the relationshipbetween keywords, but also to obtain the transition ofconcepts with time. Normally, the text mining applicationName of publicationNikkei ComputerNikkei CommunicationNikkei NetworkNikkei Personal ComputerNikkei SystemsNikkei Information Strategyprogram can offer information on the time scale; however, asuitable method for each purpose is required to reach aneffective conclusion. First, we will explain the analysismethod and techniques needed to extract the transition ofconcepts from a huge volume of document data in an ITmagazine. Then, we will show our analysis result via textmining for a case study.A. The analysis methodThe design of this case study is based on the technique ofYin [17]. Yin listed five reasons that can be used as bases forthe case study in this paper. The form of the case study isreflected in the decisions concerning five components: thenature of the research questions, propositions, analyses of thedata, logic plan that links the data to the propositions, andcriteria for the analyses. Yin identified the theory that will beexamined in the case study, and we have listed a counter-hypothesis that might account for the data. Five reasons whysingle case designs might be selected are their criticalness,extremeness, typicality, revelatory power, and longitudinalpossibility.In this study, we examined the concept word transitiontowards bigdata that occurred during a period of more than 10years, which is sufficient for a longitudinal study. In addition,we described that these concept words have extremeness andtypicality and designed a single case study on these bases.For the analytical method used in the case study, weadopted a text mining methodology. The target text data fortext mining analysis are the articles in six major IT magazines(listed in Table 1) published by Nikkei Business Publications,Inc. during the study period.All articles published after 2002 are available assearchable, readable, and downloadable content on the webhome page of Nikkei Premium. For our study, only thesummary section of each article was downloaded as theobject data. There were a total of 84,147 articles publishedbetween January 2002 and September 2015; this was asufficient amount of data for text mining.For text mining, we used an IBM Content Analyticsversion 3.0 (ICA3.0) released in June 2012. ICA3.0 canhandle several hundred million cases or petabyte-classinformation and is, therefore, appropriate for treating bigdata.ICA3.0 is a software product that collects only the necessaryinformation from unstructured information, such as writingsor documents on the Internet, and can consistently sort andanalyze this information.TABLE 1. LIST OF PUBLICATIONS.DescriptionA comprehensive IT information magazine that offers detailed descriptions, columns, and contentregarding the use of two-way communications in networks.An IT information magazine that supports decision making for telecommunications and networks.An IT information magazine that provides information about network technology at a basic level.A comprehensive IT information magazine that offers current information and skills.An IT information magazine that cultivates the skill of system development.An IT information magazine that uses IT to innovate management.1985
2016 Proceedings of PICMET '16: Technology Management for Social InnovationFigure 1. Flow of chart of the Nasukawa method.To obtain effective results from the text mining process,the knowledge and techniques of the person implementing thetext mining method are crucial. Without this ability or by justfreely using the text mining application to analyze bigdata,obtaining meaningful analysis results is not possible. To solvethis problem, we used for our analysis the Nasukawa methodintroduced by Nasukawa [7], who is an ICA3.0 developer andresearcher. The Nasukawa method originated from theknowledge and experience that Nasukawa obtained fromactual projects. He described his method in three steps thatare designed to obtain an effective result using text mining(Fig.1).The goal of Step 1 is to grasp the entire image of the data.We aim to grasp the features of the distribution by analyzingvarious output images produced by ICA3.0. The goal of Step2 is to discover the bias and change and to execute thecontent bias, which is detected by correlation analysis; thetime wise bias, which is detected by deviation analysis; andthe trend analysis, which is detected by change analysis. Ifsomething is observed in this phase, further enhancements inthe object data are needed to clearly grasp the features. InStep 3, an application scenario concerning the data featuresidentified in Step 2 is investigated and a story using bigdata isdetected.1) Step 1By making full use of the output form of the analysis fromthe ICA3.0 application, reading documents one by one usinga list of the documents, and changing the facet items (e.g.,nouns and verbs) and chronological order memory, weconfirmed that the distribution kept the perspective of thedata under control. The mining environment should bemaintained according to the purpose of the analysis if itshows some directionality during this step.Nouns and verbs include words that are not useful to graspof the meaning of a sentence. We exclude such words becausethey are unnecessary information even if they are put on amap. In this study, we followed the method suggested byMoriwaki [3] and selected the keywords to be analyzed. Then,we used the keyword selection phase in Step 1 as apre-transaction for Step 2. In ICA3.0, there is a function thatperforms text mining focused on a term; this is made possibleby registering a specific keyword with “a dictionary" togather the synonymous expressions of the keyword. Wedecided to use this function for keyword selection.To enroll keywords in the dictionary for this study, wereferenced a page of an article published in the NikkeiComputer magazine [18] that listed a glossary of keywordsone should know. Nikkei Computer is a comprehensive ITmagazine that has represented the IT industry for severaldecades. The selection criteria of the keywords from this pagewere as follows: (1) they should succinctly explain atechnical te
computing and bigdata in the evolutionary process of IT concept words. An evolutionary model was identified that reached cloud computing, indicating that another episode of evolution might start from bigdata. We focused our analysis on the evolution of previous major concept words and examined emerging concepts,
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