Data Standardization

This paper presents a comprehensive approach for optimizing industrial spare parts inventory using advanced data analysis techniques, including standardization, Principal Component Analysis (PCA), clustering methods, normality testing, and Quadratic Discriminant Analysis (QDA). These methodologies segment spare parts into specific categories, supporting informed decisionmaking in inventory management. The results suggest practical recommendations for efficient storage and cost reduction, with applications across diverse industrial sectors, contributing to sustainability and operational efficiency.

Nowadays, electrical drives generally associate inverter and induction machine. Thus, these two elements must be taken into account in order to provide a relevant diagnosis of these electrical systems. In this context, the paper presents a diagnosis method based on a multidimensional function and pattern recognition (PR). Traditional formalism of the PR method has been extended with some improvements such as the automatic choice of the feature space dimension or a "nonexclusive" decision rule based on the k-nearest neighbors. Thus, we introduce a new membership function, which takes into account the number of nearest neighbors as well as the distance from these neighbors with the sample to be classified. This approach is illustrated on a 5.5 kW inverter-fed asynchronous motor, in order to detect supply and motor faults. In this application, diagnostic features are only extracted from electrical measurements. Experimental results prove the efficiency of our diagnosis method.

Introduction: Variability in usage and definition of data characteristics in previous cohort studies on unruptured intracranial aneurysms (UIA) complicated pooling and proper interpretation of these data. The aim of the National Institute of Health/National Institute of Neurological Disorders and Stroke UIA and Subarachnoid Hemorrhage (SAH) Common Data Elements (CDE) Project was to provide a common structure for data collection in future research on UIA and SAH. Methods: This paper describes the development and summarization of the recommendations of the working groups (WGs) on UIAs, which consisted of an international and multidisciplinary panel of cerebrovascular specialists on research and treatment of UIAs. Consensus recommendations were developed by review of previously published CDEs for other neurological diseases and the literature on UIAs. Recommendations for CDEs were classified by priority into 'Core, ' 'Supplemental-Highly Recommended, ' 'Supplemental, ' and 'Exploratory. ' Results: Ninety-one CDEs were compiled; 69 were newly created and 22 were existing CDEs. The CDEs were assigned to eight subcategories and were classified as Core (8), Supplemental-Highly Recommended (23), Supplemental (25), and Exploratory (35) elements. Additionally, the WG developed and agreed on a classification for aneurysm morphology. Conclusion: The proposed CDEs have been distilled from a broad pool of characteristics, measures, or outcomes. The usage of these CDEs will facilitate pooling of data from cohort studies or clinical trials on patients with UIAs.

In the effort of Governments worldwide to effectively transform manual into electronic services, semantic interoperability issues pose as a key challenge: system-to-system interaction asks for standardized data definitions, codification of existing unstructured information and a framework for managing governmental data in a unified way. Integrating and extending recent developments in Germany, Hong-Kong, UK and US governments-but also eBusiness interoperability research results in Europe, the proposed approach is applied within the Greek e-Government Framework and bears the following steps: (a) Gathering and analysis of governmental service forms at field level, so that common structures can be identified, (b) Customization of UN/CEFACT Core Components, (c) Semiautomatic XML Schema Definition (XSD) files creation, using sets of naming, structuring and verification rules, (d) Adoption of international or development of country-specific Codelists, (e) Development of guidance material for the application of the approach by government officials at central or local level. Discussion on the results is targeting best practice examples that can be drawn at scientific and organizational level, but also the key difficulties that have to be tackled-in relevance with the administrations and industry readiness in each country. Future steps include enhancing the paradigm in order to cover vertical data standards and applying the approach to other EU or associated countries.

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an ‘umbrella protocol’ within the framework of...

Understanding the biogeography of past and present fire events is particularly important in tropical forest ecosystems, where fire rarely occurs in the absence of human ignition. Open science databases have facilitated comprehensive and synthetic analyses of past fire activity, but charcoal datasets must be standardized (scaled) because of variations in measurement strategy, sediment type, and catchment size. Here, we: i) assess how commonly used metrics of charcoal scaling perform on datasets from tropical forests; ii) introduce a new method called proportional relative scaling, which down-weights rare and infrequent fire; and iii) compare the approaches using charcoal data from four lakes in the Peruvian Amazon. We found that z-score transformation and relative scaling (existing methods) distorted the structure of the charcoal peaks within the record, inflating the variation in small-scale peaks and minimizing the effect of large peaks. Proportional relative scaling maintained the structure of the original non-scaled data and contained zero values for the absence of fire. Proportional relative scaling provides an alternative scaling approach when the absence of fire is central to the aims of the research or when charcoal is infrequent and occurs in low abundances.

Understanding the biogeography of past and present fire events is particularly important in tropical forest ecosystems, where fire rarely occurs in the absence of human ignition. Open science databases have facilitated comprehensive and synthetic analyses of past fire activity, but charcoal datasets must be standardized (scaled) because of variations in measurement strategy, sediment type, and catchment size. Here, we: i) assess how commonly used metrics of charcoal scaling perform on datasets from tropical forests; ii) introduce a new method called proportional relative scaling, which down-weights rare and infrequent fire; and iii) compare the approaches using charcoal data from four lakes in the Peruvian Amazon. We found that z-score transformation and relative scaling (existing methods) distorted the structure of the charcoal peaks within the record, inflating the variation in small-scale peaks and minimizing the effect of large peaks. Proportional relative scaling maintained the structure of the original non-scaled data and contained zero values for the absence of fire. Proportional relative scaling provides an alternative scaling approach when the absence of fire is central to the aims of the research or when charcoal is infrequent and occurs in low abundances.

Introduction: Variability in usage and definition of data characteristics in previous cohort studies on unruptured intracranial aneurysms (UIA) complicated pooling and proper interpretation of these data. The aim of the National Institute of Health/National Institute of Neurological Disorders and Stroke UIA and Subarachnoid Hemorrhage (SAH) Common Data Elements (CDE) Project was to provide a common structure for data collection in future research on UIA and SAH. Methods: This paper describes the development and summarization of the recommendations of the working groups (WGs) on UIAs, which consisted of an international and multidisciplinary panel of cerebrovascular specialists on research and treatment of UIAs. Consensus recommendations were developed by review of previously published CDEs for other neurological diseases and the literature on UIAs. Recommendations for CDEs were classified by priority into 'Core, ' 'Supplemental-Highly Recommended, ' 'Supplemental, ' and 'Exploratory. ' Results: Ninety-one CDEs were compiled; 69 were newly created and 22 were existing CDEs. The CDEs were assigned to eight subcategories and were classified as Core (8), Supplemental-Highly Recommended (23), Supplemental (25), and Exploratory (35) elements. Additionally, the WG developed and agreed on a classification for aneurysm morphology. Conclusion: The proposed CDEs have been distilled from a broad pool of characteristics, measures, or outcomes. The usage of these CDEs will facilitate pooling of data from cohort studies or clinical trials on patients with UIAs.

Information technology is an everyday means that is found in all walks of life today. This is also true for almost all areas of agricultural management, which in Hungary has been extended and accelerated by the introduction of EU agro-informatic's systems. The Hungarian Association for Agricultural Informatics (HAAI) as a supporter of education in information science, the organiser of University Summer Courses in Informatics and a contact agent for fellow organisations both at home and abroad celebrated the 10th anniversary of its existence last year. It also keeps track of the activities of the European Association for Agricultural Informatics (EFITA) and takes part in its conferences. The worldwide organisation of agricultural informatics (INFITA-International Network for Information Technology in Agriculture) decided on launching a journal entitled "Journal of Information Technology in Agriculture" in 2002. Agricultural informatics serves not only the development of the management systems of the industry but also obtaining and publicising information on production, organisation and the market for the producer. The Commission in Brussels support rural development-related research and developments in informatics.

Nowadays, electrical drives generally associate inverter and induction machine. Thus, these two elements must be taken into account in order to provide a relevant diagnosis of these electrical systems. In this context, the paper presents a diagnosis method based on a multidimensional function and pattern recognition (PR). Traditional formalism of the PR method has been extended with some improvements such as the automatic choice of the feature space dimension or a "nonexclusive" decision rule based on the k-nearest neighbors. Thus, we introduce a new membership function, which takes into account the number of nearest neighbors as well as the distance from these neighbors with the sample to be classified. This approach is illustrated on a 5.5 kW inverter-fed asynchronous motor, in order to detect supply and motor faults. In this application, diagnostic features are only extracted from electrical measurements. Experimental results prove the efficiency of our diagnosis method.

To develop an infrastructure for structured and automated collection of interoperable radiation therapy (RT) data into a national clinical quality registry. Materials and methods: The present study was initiated in 2012 with the participation of seven of the 15 hospital departments delivering RT in Sweden. A national RT nomenclature and a database for structured unified storage of RT data at each site (Medical Information Quality Archive, MIQA) have been developed. Aggregated data from the MIQA databases are sent to a national RT registry located on the same IT platform (INCA) as the national clinical cancer registries. Results: The suggested naming convention has to date been integrated into the clinical workflow at 12 of 15 sites, and MIQA is installed at six of these. Involvement of the remaining 3/15 RT departments is ongoing, and they are expected to be part of the infrastructure by 2016. RT data collection from ARIA Ò , Mosaiq Ò , Eclipse TM , and Oncentra Ò is supported. Manual curation of RT-structure information is needed for approximately 10% of target volumes, but rarely for normal tissue structures, demonstrating a good compliance to the RT nomenclature. Aggregated dose/volume descriptors are calculated based on the information in MIQA and sent to INCA using a dedicated service (MIQA2INCA). Correct linkage of data for each patient to the clinical cancer registries on the INCA platform is assured by the unique Swedish personal identity number. Conclusions: An infrastructure for structured and automated prospective collection of syntactically interoperable RT data into a national clinical quality registry for RT data is under implementation. Future developments include adapting MIQA to other treatment modalities (e.g. proton therapy and brachytherapy) and finding strategies to harmonize structure delineations. How the RT registry should comply with domain-specific ontologies such as the Radiation Oncology Ontology (ROO) is under discussion.

The growing food demand and decreasing size of the rural areas require striving for optimal results of production. To achieve this, we can use decision support systems. The critical point of the application is the availability of proper quantity and quality data set. The data source can be developed using a statistical database, using a database created by conventional management application and the measurement databases, created by data acquisition systems. The use of this systems can be helpful, if constant data is required about measurable factors which can influence the result directly or indirectly. As the result of this paper, a modular data acquisition system is developed, which consists of six modules, including the control module, user interface module, sensor modules, external device controller module, communication module and wireless module. The modular system is considered to maintain cost effectivity and response to the application needs. To support the mentioned syste...

This work presents the development of an XML based language for the standardization of the information needed to build molecular rovibrational hamiltonians. This Molecular Spectroscopic Simulations Markup Language (MSSML) allows to collect and represent in a unified way the information obtained from electronic structure calculations performed in heterogeneous distributed systems, such as Internet-based Grids of computers. This standard language will permit the use of a web (Grid) services approach to automate the simulation of rovibrational spectra.

The scholars, Frederick Burwick and James McKusick, published at Oxford University Press, Faustus from the German of Goethe translated by Samuel Taylor Coleridge in 2007. This edition articulated the result that Samuel Taylor Coleridge is the actual translator of the anonymously published translation Faustus from the German of Goethe (London: Boosey: 1821). The present article tests that result. The approach to test this result is stylometric. Specifically, function word usage is selected as the stylometric criterion, and 80 function words are used to define a 73-dimensional function word frequency profile vector for each text in the corpus of Coleridge's literary works and for a selection of works by a range of contemporary English authors. Each profile vector is a point in 80-dimensional vector space, and 5 different cluster analytic methods are used to determine the distribution of profile vectors in the space. If the result being tested is valid, then the profile for the 1821 translation should be closer in the space to works known to be by Coleridge than to works by the other authors.

Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines ...

This work presents the development of an XML based lan- guage for the standardization of the information needed to build molecular rovibrational hamiltonians. This Molecular Spectroscopic Simulations Markup Language (MSSML) allows to collect and represent in a unied way the information obtained from electronic structure calculations performed in heterogeneous distributed systems, such as Internet-based Grids of computers. This standard language will permit the use of a web (Grid) services approach to automate the simulation of rovibrational spectra.

The Hungarian Soil Information and Monitoring System (SIM) covers the whole country and provides opportunity to create similar information systems for the natural resources (atmosphere, supply of water, flora, biological resources etc). The aim of the SIM is to relate these databases. The SIM territorial measuring grid consists of 1236 measuring points with 21 elements content in Hungary. These points are representatives. Distribution of the points represents the variety of soil types of the country. The SIM is the essential basis of rational agri-environmental management and at the same time is an integrated unit of the environmental diagnosis of soils. Our aim is to develope a statistically based information system from data of the measured SIM points. We developed a method for estimating element content. To determine the concentration of the elements, need only the GPS coordinates of the place based on the number of the nearest neighbouring points. This method does not calculate with spatial circumstances. The other possibility is using the kriging method (spatial interpolation) for estimating more precisely the element content. In this study these two methods are compared. After building our statistical based information system we can develop an Internet-based service that makes it possible to reach the objectives through arranging the results of analyses into a database. Based on available data, the developed Internet-based service makes it possible to estimate element content at a certain diagnostic point with some statistical errors. It can also be applied in analyzing effects of environmental pollution.

The Hungarian Soil Information and Monitoring System (SIM) covers the whole country and provides opportunity to create similar information systems for the natural resources (atmosphere, supply of water, flora, biological resources etc). The aim of the SIM is to relate these databases. The SIM territorial measuring grid consists of 1236 measuring points with 21 elements content in Hungary. These points are representatives. Distribution of the points represents the variety of soil types of the country. The SIM is the essential basis of rational agri-environmental management and at the same time is an integrated unit of the environmental diagnosis of soils. Our aim is to develope a statistically based information system from data of the measured SIM points. We developed a method for estimating element content. To determine the concentration of the elements, need only the GPS coordinates of the place based on the number of the nearest neighbouring points. This method does not calculate with spatial circumstances. The other possibility is using the kriging method (spatial interpolation) for estimating more precisely the element content. In this study these two methods are compared. After building our statistical based information system we can develop an Internet-based service that makes it possible to reach the objectives through arranging the results of analyses into a database. Based on available data, the developed Internet-based service makes it possible to estimate element content at a certain diagnostic point with some statistical errors. It can also be applied in analyzing effects of environmental pollution.

The invasion of new technologies combined with the high cost for running shop force enterprises to search for new sales methods. Network applications and ICT (Information and Communication Technology) can help achieve e-commerce goals. In Greece, many enterprises in the agro-food and drink sectors are already present on the internet. This paper studies the adoption of e-commerce on websites that support e-commerce activities within the agro-food and drink sectors. Therefore, the paper aims to identify and evaluate their qualitative and quantitative content characteristics, rank them according to 6 content characteristics/criteria using the multicriteria method of PROMETHEE II and classify them in groups of similar adoption. The findings of this study reveal the rate of adoption of e-commerce in the sectors and can serve as a valuable model for the designers of websites that promote e-commerce activities within the wider areas of food and drinks

The new technologies invasion combined with the high cost for running a shop force enterprises to search for new sales methods. Network applications and ICT (Information and Communication Technology) can help achieve e-commerce goals. In Greece, many enterprises in the agro-food and drink sector already have an internet presence. This paper studies the adoption of e-commerce in websites that support e-commerce activities within the agro-food and drink sector. Therefore, the paper aims to identify and evaluate their qualitative and quantitative content characteristics, rank them according to 6 content characteristics/criteria using the multicriteria method of PROMETHEE II and classify them in groups of similar adoption. The findings of this study reveal the rate of adoption of e-commerce in the sector and can be a valuable model for the designers of the websites that promote e-commerce activities within the wider area of food and drinks.

The standard scientific methodology in linguistics is empirical testing of falsifiable hypotheses. As such the process of hypothesis generation is central, and involves formulation of a research question about a domain of interest and statement of a hypothesis relative to it. In corpus linguistics the domain is text, and generation involves abstraction of data from text, data analysis, and formulation of a hypothesis based on inference from the results. Traditionally this process has been paper-based, but the advent of electronic text has increasingly rendered it obsolete both because the size of digital corpora is now at or beyond the limit of what can efficiently be used in the traditional way, and because the complexity of data abstracted from them can be impenetrable to understanding. Linguists are increasingly turning to mathematical and statistical computational methods for help, and cluster analysis is such a method. It is used across the sciences for hypothesis generation by identification of structure in data which are too large or complex, or both, to be interpretable by direct inspection. This book aims to show how cluster analysis can be used for hypothesis generation in corpus linguistics, thereby contributing to a quantitative empirical methodology for the discipline.

In this paper, we describe the model and scenarios, based on the Semantic Web foundation, for decision support system (dss) for grain drying processes. During the creation of the dss model we have considered key knowledge bottle necks for various aspects of the grain drying process, especially when someone is trying to incorporate the WWW resources during the architecture design of such model.

In an attempt to make identification of plants easy and less cumbersome, computer-based software called Leasys was developed. It is a computerized version of a field key prepared for on-the-spot identification of savanna tree species in Nigeria. Leasys 1.1 is an improved version of the earlier version developed by the same authors. The current version is more expansive than the first version and thus has more features which make identification process faster and easier. Incorporated in Leasys 1.1 are some other features that make it more robust, flexible and versatile. These features include Leasys Property Registration which makes identification of a plant along two selection paths possible, Leasys Plant Management which inserts, updates and deletes plant data from the database, Leasys Deduction System whicht enables the user identify a plant by inputting a leaf feature and Leasys Quick Search which brings about fast identification of plant without going through the step by step process of identification. It is also possible to print one or all results generated. Leasys has a database on two hundred (200) different savanna trees. The system has the potential and capacity to accommodate very large number of species and very large number of taxonomic characters. With the implementation of a specialized algorithm based on the SQL OLEDB engine, AND operator professional reporting system, impressive nature GUI's network compatibility and an enhanced interaction level, Leasys has the great potengtial for progressive upgrading.

In an attempt to make identification of plants easy and less cumbersome, computer-based software called Leasys was developed. It is a computerized version of a field key prepared for on-the-spot identification of savanna tree species in Nigeria. Leasys 1.1 is an improved version of the earlier version developed by the same authors. The current version is more expansive than the first version and thus has more features which make identification process faster and easier. Incorporated in Leasys 1.1 are some other features that make it more robust, flexible and versatile. These features include Leasys Property Registration which makes identification of a plant along two selection paths possible, Leasys Plant Management which inserts, updates and deletes plant data from the database, Leasys Deduction System whicht enables the user identify a plant by inputting a leaf feature and Leasys Quick Search which brings about fast identification of plant without going through the step by step pr...

Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis. keywords Data visualization, multivariate data, high dimensionality, dimensionality reduction, cluster analysis. INTRODUCTION Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because, assuming that the variables describe different aspects of the texts in question, multivariate data provide a more complete description. Where the multivariate data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome. Exemplification is based on data abstracted from a corpus of English historical texts with a known temporal distribution, allowing the efficacy of the methods covered in the discussion to be readily verified by the reader. The discussion is in three main parts. The first part presents some fundamental data concepts-its nature, its representation using vectors and matrices, and its interpretation in terms of concepts of vector space and manifold, the second part describes the corpus and a high-dimensional data set abstracted from it, and the third outlines approaches to visualization of that data set using the concepts from (1) applied to (2). These approaches are of two types.  The first, dimensionality reduction, reduces high-dimensional data to dimensionality 3 or less to enable graphical representation; the methods presented are (i) variable selection based on variance and (ii) principal component analysis.  The second, cluster analysis, represents the structure of data in high-dimensional space directly without dimensionality reduction.

The proliferation of computational technology has generated an
explosive production of electronically encoded information of all kinds. In the face of this, traditional philological methods for search and
interpretation of data have been overwhelmed by volume, and  computational methods have been developed in an attempt to make the deluge tractable. These developments have clear implications for corpusbased linguistics in general, and for corpus-based study of language variation in particular. As  more and larger electronic corpora become available, effective analysis of them will increasingly be tractable only by adapting the interpretative methods developed by the statistical, information retrieval, pattern recognition, and related communities. To use such analytical methods effectively, however, issues that arise with respect to the abstraction of data from corpora have to be understood.  This paper addresses an issue that has a fundamental bearing on the validity of analytical results based on such data: sparsity. The discussion is in three main parts. The first part shows how a particular class of computational methods, exploratory multivariate analysis, can be used in language variation research, the second explains why data sparsity can be a problem in such analysis, and the third outlines some solutions.

The proliferation of computational technology has generated an explosive production of electronically encoded information of all kinds. In the face of this, traditional paper-based methods for search and interpretation of data have been overwhelmed by sheer volume, and a variety of computational methods have been developed in an attempt to make the deluge tractable. As such methods have been refined and new ones introduced, something over and above tractability has emerged --new and unexpected ways of understanding the data. The fact that a computer can deal with vastly larger datasets than a human is an obvious factor, but there are two others of at least equal importance. One is the ease with which data can be manipulated and reanalyzed in interesting ways without the often prohibitive labour that this would involve using manual techniques, and the other is the extensive scope for visualization that computer graphics provide.
These developments have clear implications for corpus linguistics. On the one hand, large electronic corpora potentially exploitable by the linguist are being generated as a by-product of the many kinds of daily ITbased activity worldwide, and, on the other, more and more application-specific electronic linguistic corpora are being constructed. Effective analysis of such corpora will increasingly be tractable only by adapting the interpretative methods developed by the statistical, computational linguistics, information retrieval, data
mining, and related communities.
The present chapter deals with one type of analytical tool: exploratory multivariate analysis. The discussion is in six main parts. The first part is the present introduction, the second explains what is meant by exploratory multivariate analysis, the third discusses the characteristics of data and the implications of these characteristics for generation and interpretation of analytical results, the fourth gives an overview of the various exploratory analytical methods currently available, the fifth reviews the application of exploratory multivariate analysis in corpus linguistics, and the sixth is a select bibliography. The material is presented in an intuitively accessible way, avoiding formalisms as much as possible. However, in order to work with multivariate analytical methods some background in mathematics and statistics is indispensable.

The proliferation of computational technology has generated an explosive production of electronically encoded information of all kinds. In the face of this, traditional philological methods for search and interpretation of data have been overwhelmed by volume, and a variety of computational methods have been developed in an attempt to make the deluge tractable. These developments have clear implications for corpus-based linguistics in general, and for corpusbased study of historical dialectology in particular: as  more and larger historical text corpora become available, effective analysis of them will increasingly be tractable only by adapting the interpretative methods developed by the statistical (Hair et al. 2005; Tabachnik & Fidell 2006), information retrieval (Belew 2000; Grossman & Frieder 2004), pattern recognition (Bishop 2006), and related communities. To use such analytical methods effectively, however, issues that arise with respect to the abstraction of data from corpora have to be understood. This paper addresses an issue that has a fundamental bearing on the validity of analytical results based on such data: variation in document length. The discussion is in four main parts. The first part shows how a particular class of computational methods, exploratory multivariate analysis, can be used in historical dialectology research, the second explains why variation in document length can be a problem in such analysis, the third proposes document length normalization as a solution to that problem, and the fourth points out some difficulties associated with document length normalization

Where the variables selected for cluster analysis of linguistic data are measured on different numerical scales, those whose scales permit relatively larger values can have a greater influence on clustering than those whose scales restrict them to relatively smaller ones, and this can compromise the reliability of the analysis. The first part of this discussion describes the nature of that compromise. The second part argues that a widely used method for removing disparity of variable scale, Z-standardization, is unsatisfactory for cluster analysis because it eliminates differences in variability among variables, thereby distorting the intrinsic cluster structure of the unstandardized data, and instead proposes a standardization method based on variable means which preserves these differences. The proposed mean-based method is compared to several other alternatives to Z-standardization, and is found to be superior to them in cluster analysis applications.

Nowadays, data integrity and data standardization are significant topics for information retrieval systems and also for digital libraries. Although, many standards (such as VIAF, AACR2 and MARC) and institutional regulations developed for data standardization by the nature of library and information science field, consistency between different content resources is still a problem for today's information systems. It is also one of the most important steps for digital library projects especially for the ones who are planning to gather data from different content resources. As one of these projects, a digital library for electronic thesis and dissertations (ETD) for LIS in Turkey has been developed via a digital library project executed by PhD students of Hacettepe University Department of Information Management. In this direction, this paper aims to explain data standardization processes and limitations due to regulations, language and cultural characteristics of the country with examples from the indicated project. As a result of the project a standardized structure for ETD systems were created. In this context, author and advisor names, Turkish and English titles, keywords, access restrictions were determined as the main elements for standardization processes. In the end of the project, an authority file was created for advisors via VIAF, RDA and AACR2 in order to improve efficiency of access points by ensuring data integrity.

In my previous article entitled, " Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to " Shakespeare Authorship Question " I used Mean Proximity, as a linear hierarchical clustering method and Principal Components Analysis, as a non-hierarchical linear clustering method, Self-Organizing Map U-matrix and Voronoi Map, as non-linear clustering methods to examine various works and plays assumed to have been written by Shakespeare and Sir Francis Bacon, Christopher Marlowe, John Fletcher, and Thomas Kyd to determine which of them wrote some of Shakespeare's disputed plays based on similarities in the use of function words, word-bi grams, and character-tri grams. The article showed that Shakespeare is not the author of all the disputed plays traditionally attributed to him according to the validated cluster analytic results and the stylistic criteria used. The article also indicated that the author did not consider it fair to include Edward de Vere (the strongest candidate in the Shakespeare authorship debate) and compare his poems to Shakespeare's disputed plays because poetry tends to have a particular style and a different structure than plays, and additional test was promised. The present article provides that test. In this article, I examined the 154 sonnets traditionally attributed to Shakespeare and 38 surviving poems attributed to Edward de Vere. The purpose is to give a hypothesis whether de Vere has an identifiable self-similarity and a measure of how far from/similar to Shakespeare based on the use of function words, word bi-grams, character bi-grams, and character tri-grams applying four different clustering methods: four hierarchical linear methods using Euclidean distance (Single, Average, Complete, and Ward), non-hierarchical linear multidimensional Scaling (MDS), and Kernel K-means clustering and Voronoi map as non-linear methods. The cophenetic correlation coefficient is used to select the best result obtained from a set of hierarchical analyses applied on the data matrices. The best hierarchical result is then compared

Matrix In a previous tutorial article I looked at a proximity coefficient and, in the light of that proximity created a vector-distance matrix and used it to construct a hierarchical tree using different hierarchical clustering methods which will be the basis for exploratory multivariate analysis. The present article deals with three topics: (i) standardization for variable scales variation, (ii) normalization for sample length variation, and (iii) dimensionality reduction or minimization of data space. These techniques reflect the author's academic background and particular area of interest and are, by necessity, not a particular purpose and are straightforwardly applicable to other kinds of data, and thus to a wide range of analysis in Linguistics. My treatment of these techniques is, necessarily, introductory and brief. I hope that this article will provide practitioners with an introductory overview of these techniques used for cluster analysis of electronic corpora of linguistic data. The assumption is that the data is in the form of an m x n matrix D in which, may require to transform it in various ways prior to cluster analyzing it. Standardized data matrix enables practitioners to measure the variation between n-variables and to cluster the cases they describe in common scales and values, regardless of their original scales and values. Normalized data matrix enables practitioners to eliminate the effect of variation in length among n-samples and to cluster them as if they were all (about) the same length, regardless of their original length. Dimensionality-reduced space data matrix enables practitioners to select and/or extract n-most interesting variables relevant to the research question and to visualize an existing pattern, regardless of the original space. A worked example is given to illustrate the effect each transformation technique has on a given data matrix. These transformation techniques have their own strengths and weakness but are beyond the scope of my objectives in this

The scholars, Frederick Burwick and James McKusick, published at Oxford University Press, Faustus from the German of Goethe translated by Samuel Taylor Coleridge in 2007. This edition articulated the result that Samuel Taylor Coleridge is the actual translator of the anonymously published translation Faustus from the German of Goethe (London: Boosey: 1821). The present article tests that result. The approach to test this result is stylometric. Specifically, function word usage is selected as the stylometric criterion, and 80 function words are used to define a 73-dimensional function word frequency profile vector for each text in the corpus of Coleridge's literary works and for a selection of works by a range of contemporary English authors. Each profile vector is a point in 80-dimensional vector space, and 5 different cluster analytic methods are used to determine the distribution of profile vectors in the space. If the result being tested is valid, then the profile for the 1821 translation should be closer in the space to works known to be by Coleridge than to works by the other authors. The cluster analytic results show, however, that this is not the case, and the conclusion is that the Burwick and McKusick result is falsified relative to the stylometric criterion and analytic methodology used. Where, in Popperian terms, falsification does not mean 'prove to be false'. It means that evidence which contradicts a hypothesis has been presented, and it is up to the proposer of the hypothesis either to show that the evidence is inadmissible or irrelevant, or else to emend the hypothesis accordingly

The rest of the article is organized as follows. In section 1 we give the motivation for doing this work. In section 2 we provide a quick introduction to the 1821 Faustus translations that we hope will shed some light on the problem. In section 3 we discuss the previous attempts to attribute the 1821 Faustus to Coleridge. In section 4 we outline the methodology used to address the 1821 Faust translation authorship debate. In section 5 we present data preparation. In section 6 we present our main analytical arguments deriving the evidence to refute Coleriadge’a authorship of Faustus. We also present the clustering results obtained in section 6. In section 7 we provide additional interpretation for the analytical results obtained in section 6. We conclude in section 8 with a summary of our results, and discussing open questions and possible future directions.

This paper addresses an issue that has a fundamental bearing on the
validity of analytical results based on such data: sparsity. The discussion is
in three main parts. The first part shows how a particular class of
computational methods, exploratory multivariate analysis, can be used in
language variation research, the second explains why data sparsity can be a
problem in such analysis, and the third outlines some solutions.

The present chapter deals with one type of analytical
tool: exploratory multivariate analysis. The discussion is
in six main parts. The first part is the present
introduction, the second explains what is meant by
exploratory multivariate analysis, the third discusses the
characteristics of data and the implications of these
characteristics for generation and interpretation of
analytical results, the fourth gives an overview of the
various exploratory analytical methods currently
available, the fifth reviews the application of exploratory
multivariate analysis in corpus linguistics, and the sixth
is a select bibliography. The material is presented in an
intuitively accessible way, avoiding formalisms as much
as possible. However, in order to work with multivariate
analytical methods some background in mathematics and

The proliferation of computational technology has generated an
explosive production of electronically encoded information of all
kinds. In the face of this, traditional philological methods for search
and interpretation of data have been overwhelmed by volume, and a
variety of computational methods have been developed in an attempt
to make the deluge tractable. These developments have clear
implications for corpus-based linguistics in general, and for corpusbased
study of historical dialectology in particular: as more and larger
historical text corpora become available, effective analysis of them
will increasingly be tractable only by adapting the interpretative
methods developed by the statistical (Hair et al. 2005; Tabachnik &
Fidell 2006), information retrieval (Belew 2000; Grossman & Frieder
2004), pattern recognition (Bishop 2006), and related communities.
To use such analytical methods effectively, however, issues that arise
with respect to the abstraction of data from corpora have to be
understood. This paper addresses an issue that has a fundamental
bearing on the validity of analytical results based on such data:
variation in document length. The discussion is in four main parts.
The first part shows how a particular class of computational methods,
exploratory multivariate analysis, can be used in historical
dialectology research, the second explains why variation in document
length can be a problem in such analysis, the third proposes document
length normalization as a solution to that problem, and the fourth
points out some difficulties associated with document length
normalization

Where the variables selected for cluster analysis of linguistic data are
measured on different numerical scales, those whose scales permit relatively
larger values can have a greater influence on clustering than those whose
scales restrict them to relatively smaller ones, and this can compromise the
reliability of the analysis. The first part of this discussion describes the nature
of that compromise. The second part argues that a widely used method for
removing disparity of variable scale, Z-standardization, is unsatisfactory for
cluster analysis because it eliminates differences in variability among
variables, thereby distorting the intrinsic cluster structure of the
unstandardized data, and instead proposes a standardization method based
on variable means which preserves these differences. The proposed meanbased
method is compared to several other alternatives to Z-standardization,
and is found to be superior to them in cluster analysis applications.