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In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop diseases (BBCPF) was proposed in this paper by utilizing the advantage of knowledge graph in describing complex relations between disease entities in a structured form. This model was composed of two parts, i.e., named-entity recognition and relation extraction, in the form of an assembly line. To deal with the different meanings of Chinese crop disease terms in different contexts and to better obtain the contextual information, the BERT model was introduced for dynamic vector representations. Then, the BiLSTM layer was used to learn long-distance text information, and the CRF was applied to obtain the globally optimal labeling sequence, so as to output the crop disease entities. According to the entity category, the entities were divided as subjects and objects, which were then input into the disordered language model PERT to extract the contextual features of the relation data. At last, the fully connected layer was used to decode the information and output the crop disease entity-relation triples. The experiment results show that, on the self-built disease corpus dataset, the Precision, Recall, and F1-Score values of the established model reached 85.63%, 79.46% and 82.43%, respectively, for entity extraction, and reached 97.96%, 98.43% and 98.16%, respectively, for relation extraction. This paper provides an effective method for information extraction in the construction of Chinese crop disease domain knowledge graphs. [ABSTRACT FROM AUTHOR]

Data mining with Deep Neural Networks becomes a very promising research area. In the course of our daily lives, enormous amounts of transactional data are produced. As a result, the transactional dataset is growing exponentially. The handling of data is a major issue. To handle transactional data, tools and strategies are therefore required. The tool that accelerates the decision-making process and enables unique knowledge management of the information found in transaction data is data mining. Many writers provided their methods to handle transactional data and discover new patterns. This study aims to provide a new algorithm for data mining that performs currently used algorithms in terms of speed and interest. The effectiveness of the DAENMF-ARM algorithm will be demonstrated by comparing it with the most popular and widely used data mining algorithms, namely Apriori ECLAT and FP-Growth, and establishing association rules. The organization of the paper is: the 1st portion is the introduction, the 2nd portion is the literature work, the 3rd portion is the methodology, 4th portion is the experimental setup, and last portion is conclusion of the paper. [ABSTRACT FROM AUTHOR]

This research presents an exploratory and descriptive study on the use of knowledge organization processes and systems in the context of repositories, published in journals indexed in databases between 2015 and 2020. The authors of these papers do not necessarily publish in specific events and journals in the Knowledge Organization area, but rather in the Library and Information Science arena. The study has been carried out in four steps: 1. Search, retrieval and selection of articles; 2. Development of a data codebook; 3. Identification and codification of topics; and, 4. Analysis of the data extracted. A final sample of 33 articles was defined. The methodology applied to determine the theme of journal articles is presented in detail, including the use of the Classification System for Knowledge Organization Literature (CSKOL). The illustrative data of disciplinarity and interdisciplinarity present in the articles object of this study are shown and discussed, regarding the predominance of certain ranges of CSKOL and regarding the diversity of the representative themes of the contents. It is possible to conclude that the use of CSKOL proves to be a suitable lens for analysing and understanding the literature on the field of knowledge organization in institutional repositories. It is shown that, in these 33 articles the themes of knowledge organization are combined with interdisciplinary themes from other areas of knowledge. In our opinion, this enriches and improves theoretical support for research development. [ABSTRACT FROM AUTHOR]

The focus of this paper is on the use-oriented Product Service System (PSS) adopted within the off-highway construction industry. A use-oriented PSS dictates that the product stays in the ownership of the provider and is made available in a different form to the customer (user), and is sometimes shared by a number of users. Extensive research has been carried out into PSS design and strategy, yet there is a research gap relating to the generation, analysis and management of such information and knowledge in a big data environment to support the implementation of a use-oriented PSS. This gap is a substantial barrier to the exploitation of use-oriented PSS research in the commercial world. A roadmap is presented that clarifies the data and structure requirements to enable OEMs to determine and validate their readiness level towards the adoption of a use-oriented PSS driven business model. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to provide a bibliometric and systematic literature analysis of studies published in the Journal of Intellectual Capital (JIC) from 2014 to 2018 in order to highlight emerging themes and future trends. Design/methodology/approach: The analysis focused on 187 papers published on JIC over a period of five years. A scientometric approach to data mining enabled the detection of patterns in the dataset. Precisely, the investigation was conducted by integrating a bibliometric analysis on VOSviewer with a systematic literature review. Findings: Four main streams of research on JIC emerged in the years of the analysis: reporting and disclosure of intellectual capital; intellectual capital research in universities, education and public sector; knowledge management; intellectual capital, financial performance, and market value. Research limitations/implications: The study offers valid insights to the topics covered by the Journal of Intellectual Capital by identifying the main research gaps and trends, along with future research avenues. Originality/value: Prior scholars mostly focused on systematic literature reviews, whilst the use of bibliometric methods generally seems to be a missing tile in the research domain. Also, none of the extant studies has focused on the Journal of Intellectual Capital with reference to the 2014–2018 period. The use of both bibliometric and systematic approaches to literature review delivered extremely fine-tuned results in terms of factors such as citations, contents and evolution of clusters over time. [ABSTRACT FROM AUTHOR]

Aim/Purpose The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendations for Researchers By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their "numerical" behavior in a wide spectrum of data characteristics and business or research requirements. [ABSTRACT FROM AUTHOR]

Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset. [ABSTRACT FROM AUTHOR]

Text mining is the process and technique used for searching, retrieving and extracting high quality, useful and purposeful information from the ocean of unst ructured and unclassi fied data and informat ion in the form of text written in natural language. It is also refer red to as text engineering, text data mining or text analyt ics. Text mining is an emerging field of research. The purpose of this paper is to review text mining in general and with special context to library and informat ion science. Text mining involves artificial intelligence. Hence, this system is very efficient in its work as compared to human capabilities in terms of time const raints and the counts of frequency which involves accuracy. This field has many prospects to offer and in the context of the library and informat ion science. This technique can be very helpful in managing ever-growing and proliferat ing information in every field of knowledge. Moreover, this technique can also be used for research in the field of LIS. Researchers and librarians may find it useful to put efforts in this field for being bet ter adapted to future. This review is useful for the mot ivation of LIS researchers to involve in text mining aimed at cont ribut ing new knowledge to the improvement of library profession and services. Since text mining is an emerging field in research, less literature is available part icularly related to library and informat ion science. [ABSTRACT FROM AUTHOR]

In the current market situation, mining companies are faced with the necessity to take actions to improve the efficiency of the mining process. Some of these actions enforce a centralization of activities in the field of deposit economy and planning of mining operations in these companies. In the planning process with such scope the large knowledge of designers is required, which could be additionally supported by a knowledge base, supplied by information and data obtained during the completion of mining works, which also allows for use of the expert knowledge of other organizational units of the mine or the company. The paper presents an original expert system for mining works planning in the underground hard coal mines (MinePlanEx). The aim of the developed system is to support the designers of production planning in hard coal mines within the scope of: equipment selection, mining machinery combining into equipment sets and determining characteristic curves regarding the production results in the planned excavations. Knowledge of the system is represented by the rules selected with the chosen data mining techniques (association rules and classification trees) and obtained from experts. The first part of the paper presents a knowledge base, knowledge acquisition module and inference module which are the main components of the system. The second part contains an example of system operation. [ABSTRACT FROM AUTHOR]

The concept of digital immortality has emerged over the past decade and is defined here as the continuation of an active or passive digital presence after death. Advances in knowledge management, machine to machine communication, data mining and artificial intelligence are now making a more active presence after death possible. This paper examines the research and literature around active digital immortality and explores the emotional, social, financial, and business impact of active digital immortality on relations, friends, colleagues and institutions. The issue of digital immortality also raises issues about the legal implications of a possible autonomous presence that reaches beyond mortal existence, and this will also be investigated. The final section of the paper questions whether digital immortality is really a concern and reflects on the assumptions about it in relation to neoliberal capitalism. It suggests that digital immortality may in fact merely be a clever ruse which in fact is likely to have little, if any legal impact despite media assumptions and hyperbole. [ABSTRACT FROM AUTHOR]

Background: In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field.Methods: To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future.Results: By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People's Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing).Conclusion: This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions. [ABSTRACT FROM AUTHOR]

Companies use social media to communicate customers has been increasing recently. By analyzing the content generated by users online, companies obtain the information about market and use in product management and innovation, improving the competitiveness of enterprises. As the emerging of growing user-generated content (UGC), how to achieve effective information extraction and transform into product knowledge has posed a challenge to the enterprise. In the context of smart phone product innovation, with data fetched from websites and provided by the enterprise, by using natural language processing and semantic Web tools, this paper proposes an integrated method of innovation knowledge management based on UGC, which provide a systematic solution for the interactive innovation knowledge management. [ABSTRACT FROM AUTHOR]

In this paper, a new idea is developed for improving the agent intelligence. In fact with the presented convolutional neural network (CNN) approach for knowledge classification, the agent will be able to manage its knowledge. This new concept allows the agent to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively. In addition, through this research, we developed a comparative study between the proposed CNN approach and the classical classification approaches. As foreseeable the deep learning method outperforms the others in term of classification accuracy. [ABSTRACT FROM AUTHOR]

The flexibility introduced by evolutionary algorithms (EAs) has allowed the use of virtually arbitrary objective functions and constraints—even when evaluations require, as for real-world problems, running complex mathematical and/or procedural simulations of the systems under analysis. Even so, EAs are not a panacea. Traditionally, the solution search process has been totally oblivious of the specific problem being solved, and optimization processes have been applied regardless of the size, complexity, and domain of the problem. In this paper, we justify our claim that far-reaching benefits may be obtained from more directly influencing how searches are performed. We propose using data mining techniques as a step for dynamically generating knowledge that can be used to improve the efficiency of solution search processes. In this paper, we use Kohonen SOMs and show an application for a well-known benchmark problem in the water distribution system design literature. The result crystallizes the conceptual rules for the EA to apply at certain stages of the evolution, which reduces the search space and accelerates convergence. [ABSTRACT FROM AUTHOR]

SUMMARY To create criteria for the activities involved in offshore software development, this paper proposes a method to visualize the status of an organization. The basic premises in this research are that there are 12 capabilities and each capability has five levels. First, this paper introduces a knowledge management system called SPICE, which has a visualization function. The function includes the growth path of an individual, the growth path of a group, and a dependency structure. Then, to explain the use of method in the realm of offshore software development, this paper describes an experimental analysis and the accompanying visualization. The future possibilities of the method are also examined in detail. [ABSTRACT FROM AUTHOR]

The paper focuses on the usage of data-mining techniques as a support tool for decision making. This paper describes the mining of hidden and potentially useful information from databases using data mining methods. These methods, sometimes called as techniques for knowledge discovering, help users, mostly managers, to make qualified decisions in the organization. The aim of the process of knowledge management is not only to collect information, but to transform it into knowledge and use it in a decision-making process. The purpose of this paper was to find and evaluate the different methodological approaches appropriate for customer segmentation. Various data mining techniques were used for demonstration of customer segmentation according to their purchasing behavior within a selected hypermarket. The following techniques were used for clustering: K-means clustering method, Two Step clustering method and Self Organizing Maps. The quality of final models was evaluated by Silhouette measure. It combines the principles of clusters separation and cohesion. Data mining model was constructed from approximately 60 thousand transaction records. Only the food records were selected for the analysis. The paper also examined the effect of the number of dimensions to the clustering. The original variables were reduced into a smaller number of uncorrelated principal components. These components were used for construction of a scatter plot to check the homogeneity of clusters. The results of this analysis confirmed that the reduction of dimensionality is an useful device for the evaluation of generated clusters. [ABSTRACT FROM AUTHOR]

In this information-explosion era, the quality of collective intelligence is not easy to control. Knowledge demanders are required to invest an amount of time in evaluating the collective intelligence quality of virtual community to acquire correct knowledge. In addition, some knowledge providers might provide poor quality information in this circumstance and overall collective intelligence quality would be affected. The knowledge providers would reduce the willingness to share knowledge. Therefore, this paper develops a virtual community question and answer suitability determination (QASD) model to enhance collective intelligence quality. This proposed model analyzes the Q&A title, Q&A content, and the discussion and employs keyword extraction, article’s core information extraction, article similarity determination and semantic analysis techniques to determine the quality of Q&A, the suitability of Q&A content, and the suitability of Q&A semantics. After that, the integrated suitability score between target question and answer can be obtained and regarded as the reference for knowledge demanders in acquiring correct knowledge in virtual community. In order to demonstrate applicability of the proposed methodology, a web-based system is also established based on the proposed model. Furthermore, a real-world case is applied to evaluate the proposed model. The verification results show that when the system maintains about 600 training data, the indicator performance can be improved to 80% (Recall Rate) and 85% (Accuracy Rate), and the system performance continues to grow with increasing training amount and reaches the stable and better performance level. Hence, the developed system is a high-performance Q&A suitability determination system. As a whole, this paper provides an approach for virtual community to efficiently evaluate the suitability of Q&A. [ABSTRACT FROM PUBLISHER]

Background: Chronic diseases management outside expensive hospital settings has become a major target for governments, funders and healthcare service providers. It is well known that chronic diseases such as Type 2 Diabetes (T2D) do not occur in isolation, and has a shared aetiology common to many other diseases and disorders. Diabetes Australia reports that it is associated with a myriad of complications, which affect the feet, eyes, kidneys, and cardiovascular health. For instance, nerve damage in the lower limbs affects around 13% of Australians with diabetes, diabetic retinopathy occurs in over 15% of Australians with diabetes, and diabetes is now the leading cause of end-stage kidney disease. Our research focus is therefore to understand the comorbidity pattern, which in turn can enhance our understanding of the multifactorial risk factors of chronic diseases like Type 2 Diabetes. Our research approach is based on utilising valuable indicators present in pre-existing administrative healthcare data, which are routinely collected but often neglected in health research. One such administrative healthcare data is the hospital admission and discharge data that carries information about diagnoses, which are represented in the form of ICD-10 diagnosis codes. Analysis of diagnoses codes and their relationships helps us construct comorbidity networks which can provide insights that can be used to understand chronic disease progression pattern and comorbidity network at a population level. This understanding can subsequently enable healthcare providers to formulate appropriate preventive health policies targeted to address high-risk chronic conditions.Methods and Findings: The research utilises network theory principles applied to administrative healthcare data. Given the high rate of prevalence, we selected Type 2 Diabetes as the exemplar chronic disease. We have developed a research framework to understand and represent the progression of Type 2 diabetes, utilising graph theory and social network analysis techniques. We propose the concept of a 'comorbidity network' that can effectively model chronic disease comorbidities and their transition patterns, thereby representing the chronic disease progression. We further take the attribution effect of the comorbidities into account while generating the network; that is, we not only look at the pattern of disease in chronic disease patients, but also compare the disease pattern with that of non-chronic patients, to understand which comorbidities have a higher influence on the chronic disease pathway. The research framework enables us to construct a baseline comorbidity network for each of the two cohorts. It then compares and merges these two networks into single comorbidity network to discover the comorbidities that are exclusive to diabetic patients. This framework was applied on administrative data drawn from the Australian healthcare context. The overall dataset contained approximately 1.4 million admission records from 0.75 million patients, from which we filtered and sampled the records of 2300 diabetics and 2300 non-diabetic patients. We found significant difference in the health trajectory of diabetic and non-diabetic cohorts. The diabetic cohort exhibited more comorbidity prevalence and denser network properties. For example, in the diabetic cohort, heart and liver-related disorders, cataract etc. were more prevalent. Over time, the prevalence of diseases in the health trajectory of diabetic cohorts were almost double of the prevalence in the non-diabetic cohort, indicating entirely different ways of disease progression.Conclusions: The paper presents a research framework based on network theory to understand chronic disease progression along with associated comorbidities that manifest over time. The analysis methods provide insights that can enable healthcare providers to develop targeted preventive health management programs to reduce hospital admissions and associated high costs. The baseline comorbidity network has the potential to be used as the basis to develop a chronic disease risk prediction model. [ABSTRACT FROM AUTHOR]

This work highlights how to transform information from invoice documents to semantic models, as an implementation of ontology modeling. The migration from printed paper to digital documents in the Mexican Government Offices in the last few years has brought significant opportunities for the usage of information technologies and applications. However, when changing digital document information into knowledge, there are still many gaps to be filled. This work proposes a solution to some issues regarding ontology modeling, specifically when mapping a document that follows some XML schema to an ontology under the OWL standard. The main contribution of this work is to provide new interpretations of the XML terms in the context of OWL, so that the XML Schema Definition (XSD) structures can be mapped into more complex OWL structures. A software tool developed to test and validate the information extraction strategies proposed is presented here. [ABSTRACT FROM AUTHOR]

Business process analysis is a key activity that aims at increasing the efficiency of business operations. In recent years, several data mining based methods have been designed for discovering interesting patterns in event logs. A popular type of methods consists of applying frequent itemset mining to extract patterns indicating how resources and activities are frequently used. Although these methods are useful, they have two important limitations. First, these methods are designed to be applied to original event logs. Because these methods do not consider other perspectives on the data that could be obtained by applying data transformations, many patterns are missed that may represent important information for businesses. Second, these methods can generate a large number of patterns since they only consider the minimum support as constraint to select patterns. But analyzing a large number of patterns is time-consuming for users, and many irrelevant patterns may be found. To address these issues, this paper presents an improved event log analysis approach named AllMining. It includes a novel pre-processing method to construct multiple types of transaction databases from a same original event log using transformations. This allows to extract many new useful types of patterns from event logs with frequent itemset mining techniques. To address the second issue, a pruning strategy is further developed based on a novel concept of pattern coverage, to present a small set of patterns that covers many events to decision makers. Results of experiments on real-life event logs show that the proposed approach is promising compared to existing frequent itemset mining approaches and state-of-the-art process model algorithms. [ABSTRACT FROM AUTHOR]

The Semantic Web opens up new opportunities for the data mining research. Semantic Web data is usually represented in the RDF triple format (subject, predicate, object). Large RDF-style Knowledge Bases contain hundreds of millions of RDF triples that represent knowledge in a machine-understandable format. Association rule mining is one of the most effective techniques for detecting frequent patterns. In the context of Semantic Web data mining, most existing methods rely on users intervention that is time-consuming and error-prone due to a large amount of data. Meanwhile, rule quality factors (e.g. support and confidence) usually consider knowledge at the instance-level. Namely, these factors disregard the knowledge embedded at the schema-level. In this paper, we demonstrate that ignoring knowledge encoded at the schema-level negatively impacts the interpretation of discovered rules. We introduce an approach called SWARM (Semantic Web Association Rule Mining) that automatically mines Semantic Association Rules from RDF data. The main achievement of SWARM is to reveal common behavioural patterns associated with knowledge at the instance-level and schema-level. We discuss how to utilize knowledge encoded at the schema-level to add more semantics to the rules. We compare the semantic of rules discovered by SWRAM with one of the latest approaches in this field to show the importance of considering schema-level knowledge. Initial experiments performed on RDF-style Knowledge Bases demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPE $$ _\mathrm{umu}$$ -GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approaches. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm, namely HUIM-BPSO, is proposed to efficiently find HUIs. The designed HUIM-BPSO algorithm finds the high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of the particles based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles for the designed HUIM-BPSO algorithm. An OR/NOR-tree structure is further developed to reduce the invalid combinations for discovering HUIs. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of execution time, number of discovered HUIs, and convergence. [ABSTRACT FROM AUTHOR]

Building information modeling is instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. However, good building models do not happen by accident, but rather as a result of a managed process that involves several participants from different disciplines and backgrounds. Effective management of this process requires an ability to closely monitor the modeling process and correctly measure modelers' performance. Nevertheless, existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress. The widespread utilization of Building Information Modeling (BIM) tools presents a unique opportunity to retrieve granular design process data and conduct accurate performance measurements. As a building's 3D model is gradually developed, model generation software packages, such as Autodesk Revit, automatically create log files that record design activities. This paper investigates what information these log files contain and how one can extract and further analyze the information to provide insight into the design modeling process. The specific objectives of this study were to: (1) investigate the presence of implicit patterns in 3-D design log files; and (2) to empirically characterize the performance of modelers based on the time it takes them to execute similar modeling tasks. To fulfill these objectives, design log files provided by an international architecture and design firm were analyzed. Using a tailored text file parser, user-model interaction data including modeler characteristics, command type, and command time were extracted from the journal files. To identify implicit command execution patterns, a sequence mining algorithm based on Generalized Suffix Trees (GST) was implemented. It was shown that there is a statistically significant difference between the average time it takes modelers to execute each command sequence. This study extends the existing knowledge by proposing a novel methodology to extract meaningful patterns from time-stamped unstructured design log data. This research contributes to the state of practice by providing a better understanding of information embedded in design log files. [ABSTRACT FROM AUTHOR]

Knowledge management is a general framework, concept that comprises the knowledge discovery process, learning techniques and collection of data mining algorithms. The paper researches the possibilities and advantages of supervised learning and knowledge base within the whole knowledge management in the process of market research. Knowledge management is a general concept of organizing, extracting, deployment und using the knowledge within an organization. The final goal is addition of the new knowledge in all functional areas of organization and use of knowledge to achieve strategic advantages and realize the defined and accepted goals of the organization. The paper is focused on marketing and discovering customers' behavior determined by their demographic characteristics. It is shown in the paper that the supervised learning and knowledge base as components of knowledge management may be successfully implemented in marketing in description and forecasting of customers' behavior. [ABSTRACT FROM AUTHOR]

The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery. In this paper, a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors (STSNN) is proposed to detect spatio-temporal clusters of different sizes, shapes, and densities in spatio-temporal databases with a large amount of noise. The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatio-temporal density for a spatio-temporal entity with definite mathematical meanings. Then, the density-based clustering strategy is employed to uncover spatio-temporal clusters. The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities. Experiments are undertaken on several simulated data-sets to demonstrate the effectiveness and advantage of the STSNN algorithm. Also, the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively. [ABSTRACT FROM PUBLISHER]

Abstract: Knowledge management (KM) and data mining (DM) have become more important today, however, there are few comprehensive researches and categorization schemes to discuss the characteristics for both of them. Using a bibliometric approach, this paper analyzes KM and DM research trends, forecasts and citations from 1989 to 2009 by locating headings “knowledge management” and “data mining” in topics in the SSCI database. The bibliometric analytical technique was used to examine these two topics in SSCI journals from 1989 to 2009, we found 1393 articles with KM and 1181 articles with DM. This paper implemented and classified KM and DM articles using the following eight categories—publication year, citation, country/territory, document type, institute name, language, source title and subject area—for different distribution status in order to explore the differences and how KM and DM technologies have developed in this period and to analyze KM and DM technology tendencies under the above result. Also, the paper performs the K–S test to check whether the distribution of author article production follows Lotka’s law. The research findings can be extended to investigate author productivity by analyzing variables such as chronological and academic age, number and frequency of previous publications, access to research grants, job status, etc. In such a way characteristics of high, medium and low publishing activity of authors can be identified. Besides, these findings will also help to judge scientific research trends and understand the scale of development of research in KM and DM through comparing the increases of the article author. Based on the above information, governments and enterprises may infer collective tendencies and demands for scientific researcher in KM and DM to formulate appropriate training strategies and policies in the future. This analysis provides a roadmap for future research, abstracts technology trend information and facilitates knowledge accumulations, therefore the future research can concentrated in core categories. This implies that the phenomenon “success breeds success” is more common in higher quality publications. [Copyright &y& Elsevier]

Traditional knowledge (TK) is the knowledge base of a particular society or community which has been generated over a period of time through the process of learning and sharing by its members. TK is time tested and has been playing a vital role in sustainable development of the indigenous communities. Commercialization of biodiversity and traditional knowledge has increased the threat of misappropriation. Indian cultural and scientific heritage is discussed in this paper. Proper KM practices are required to tap that knowledge. This paper highlights the need to develop an Indigenous Knowledge Management System (IKMS) for indigenous communities which should document the knowledge heritage (without jeopardizing local culture, societal practices, IPRs, etc.) and it should also improve the process of adaptation, adoption and experimentation of the traditional knowledge. To develop an IKMS, various types of knowledge management tools and platforms are discussed. This paper also highlights the major issues/challenges in management of traditional knowledge. [ABSTRACT FROM AUTHOR]

In mining data streams the most popular tool is the Hoeffding tree algorithm. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splitting attribute. In the literature the same Hoeffding's bound was used for any evaluation function (heuristic measure), e.g., information gain or Gini index. In this paper, it is shown that the Hoeffding's inequality is not appropriate to solve the underlying problem. We prove two theorems presenting the McDiarmid's bound for both the information gain, used in ID3 algorithm, and for Gini index, used in Classification and Regression Trees (CART) algorithm. The results of the paper guarantee that a decision tree learning system, applied to data streams and based on the McDiarmid's bound, has the property that its output is nearly identical to that of a conventional learner. The results of the paper have a great impact on the state of the art of mining data streams and various developed so far methods and algorithms should be reconsidered. [ABSTRACT FROM PUBLISHER]

A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct. [ABSTRACT FROM AUTHOR]

In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

Abstract: Subgroup discovery is a descriptive data mining technique which aims at obtaining interesting rules through supervised learning. In general, there are no works analysing the consequences of the presence of missing values in data in this task, although improper handling of this type of data in the analysis may introduce bias and can result in misleading conclusions being drawn from a research study. This paper presents a study on the effect of using the most relevant approaches for pre-processing of missing values in a determined group of algorithms, the evolutionary fuzzy systems for subgroup discovery. The experimental study presented in this paper show that, among the methods studied, the KNNI pre-processing approach for missing values obtains the best results in evolutionary fuzzy systems for subgroup discovery. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to apply a fuzzy quality function deployment (QFD) approach to prioritize knowledge management (KM) solutions for an international port in Taiwan. First, the paper examines house of quality (HOQ) matrices to facilitate handling of the ‘what’ (i.e., KM requirements) and ‘how’ (KM solutions) aspects of the QFD problem, and proposes procedures for the use of a fuzzy QFD method. A case study concerning port K in Taiwan is then used to demonstrate a systematic appraisal process for prioritizing KM solutions, and twenty attributes with sixteen feasible KM implementation solutions are measured employing an HOQ matrix. Finally, the top five feasible solutions for implementing KM at port K are identified. The empirical results show that ‘establishment of a data storage and data mining system’ in the technology dimension is the most urgent requirement for KM implementation at port K in Taiwan. [Copyright &y& Elsevier]

Rule-based knowledge bases are constantly increasing in volume, thus the knowledge stored as a set of rules is getting progressively more complex and when rules are not organized into any structure, the system is inefficient. The aim of this paper is to improve the performance of mining knowledge bases by modification of both their structure and inference algorithms, which in author's opinion, lead to improve the efficiency of the inference process. The good performance of this approach is shown through an extensive experimental study carried out on a collection of real knowledge bases. Experiments prove that rules partition enables reducing significantly the percentage of the knowledge base analysed during the inference process. It was also proved that the form of the group's representative plays an important role in the efficiency of the inference process. [ABSTRACT FROM AUTHOR]

In the information systems, customer relationship management (CRM) is the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction with the goal of improving the business relationships with customers. Also, it is the strongest and the most efficient approach to maintaining and creating the relationships with customers. However, to the best of our knowledge and despite its importance, there is not any comprehensive and systematic study about reviewing and analyzing its important techniques. Therefore, in this paper, a comprehensive study and survey on the state of the art mechanisms in the scope of the CRM are done. It follows this goal by looking at five categories in which CRM plays a significant role: E-CRM, knowledge management, data mining, data quality and, social CRM. In each category, a couple of studies are presented and determinants of CRM are described and discussed. The major development in these five categories is reviewed and the new challenges are outlined. Also, a systematic literature review (SLR) in each of these five categories is provided. Furthermore, insights into the identification of open issues and guidelines for future research are provided. [ABSTRACT FROM AUTHOR]

knowledge management, data mining, and text mining techniques have been adopted in various successful biomedical applications in recent years. Data Mining (DM) is the most important subfields in knowledge management (KM). It has been proven that data mining can enhance the KM process with better knowledge. In this paper, we investigate the application of DM techniques for mining knowledge of the patient record. The patient record represents documents of the patient's examinations and treatm ents. Data Mining is the process of mining or extracting information from a data set and transform it into an understandable structure for further use. We propose a methodology for mining medical knowledge based on the Bayesian Classification to predict and detect anomalies in breast cancer. We use the Naïve Bayes Algorithm to develop this methodology. We illustrate the knowledge mining process by real examples of medical field. We investigate through these illustrations how knowledge is better mined an d thus, reused when applying concepts and techniques of Data Mining. On the other hand, we investigate the potential contribution of the Naive Bayesian Classification methodology as a reliable support in computer-aided diagnosis of such events, using the well-known Wisconsin Prognostic Breast Cancer dataset. Finally, we will demonstrate the suitability and ability of the Naive Bayes methodology in Classification/Prediction problems in breast cancer. [ABSTRACT FROM AUTHOR]

This paper discusses the role of knowledge management for sustainable development in research and teaching organizations (RTOs) by stressing the need that they would develop a common model of making sense when trying to couple "scientific research and social goals" to solve the wicked problems of humanity related to poverty, land degradation and climate change. After recalling the central role of the "ugly duckling" theorem in knowledge management, the paper suggests the use of the ontology approach within the single disciplines in order to promote the interdisciplinary and transdisciplinary work. After considering the spatial dimension of the RTOs and the failure of the current use of science and technology to solve these wicked problems, this paper supports the idea of "rethinking development" on the basis of a paradigm of cooperation between RTOs of a transdisciplinary nature. The paper concludes that, notwithstanding an already active global computer network (Internet), it is necessary to develop and improve the research for adapting the "Web Evolution" to human needs. [ABSTRACT FROM AUTHOR]

The focus of this paper is to present the idea of knowledge base in software project estimation process. It begins by covering the importance of estimation in IT project lifecycle. There are presented selected software estimation methods: Function Points, Use Case Points, Story Points. The paper points the place of knowledge base in an estimation process and shows benefits of using it. The Authoring Tool for Software project Effort Estimator (ATSEE) is presented [ABSTRACT FROM AUTHOR]

A self-aware system has the possibility of dealing with novel situations more effectively than a system without self-awareness, because it would have the capacity for introspection that would allow it to inspect and exploit its representations. A self-aware system can attend to its own internal states, thus providing a means of generating introspection and self-modification capabilities. In this paper we consider robot self-awareness from the point of view of temporal relation based data mining. In particular, we consider the problem of learning effects of mobile robot actions and self-detection. This paper describes a new method for representing and reasoning about temporal knowledge. In particular, this paper describes a new method for maintaining the relationships between temporal intervals. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the effect of organizational demographic variables on successful knowledge management (KM) implementation which insofar has not been thoroughly reported in the KM literature. For meaningful results to be generated, four organizational demographic variables, namely functional areas, years of KM involvement, KM development stage, and degree of knowledge intensity were moderated against a comprehensive set of KM activities, which comprise of KM preliminary success factors, KM strategies and KM processes, with organizational performance. The respondents comprised of middle managers working in the telecommunication industry in Malaysia. Based on the data collected from 289 respondents using a set of structured questionnaire, the results reveal that all the four demographic characteristics interacted with the degree of implementation of the KM activities, while three of the characteristics, with exception of functional areas, show significant relationships with organizational performance. The contributions of the paper, along with the implications of the results are discussed and interpreted to provide guidance to organizations for improved business performance through KM implementation success. [Copyright &y& Elsevier]

A new type of data mining considers the case where the instances over which induction takes place are intelligent agents who might act strategically to thwart the learner. Instances comprised of humans, companies, or governments all have this capability. One paper calls this adversarial learning and proposes an iterated learning process—much like reinforcement learning—to determine a classifier. The current authors proposed a different approach that uses rational expectation ideas to alter the learner’s problem to directly anticipate possible strategic gaming by agents during the induction process. This paper explores differences between solutions produced by these two approaches on a credit dataset and draws some general insights. [ABSTRACT FROM AUTHOR]

Class imbalance is a problem that is common to many application domains. When examples of one class in a training data set vastly outnumber examples of the other class(es), traditional data mining algorithms tend to create suboptimal classification models. Several techniques have been used to alleviate the problem of class imbalance, including data sampling and boosting. In this paper, we present a new hybrid sampling/boosting algorithm, called RUSBoost, for learning from skewed training data. This algorithm provides a simpler and faster alternative to SMOTEBoost, which is another algorithm that combines boosting and data sampling. This paper evaluates the performances of RUSBoost and SMOTEBoost, as well as their individual components (random undersampling, synthetic minority oversampling technique, and AdaBoost). We conduct experiments using 15 data sets from various application domains, four base learners, and four evaluation metrics. RUSBoost and SMOTEBoost both outperform the other procedures, and RUSBoost performs comparably to (and often better than) SMOTEBoost while being a simpler and faster technique. Given these experimental results, we highly recommend RUSBoost as an attractive alternative for improving the classification performance of learners built using imbalanced data. [ABSTRACT FROM AUTHOR]

Abstract: Difference detection is actual and extremely useful for evaluating a new medicine B against a specified disease by comparing to an old medicine A, which has been used to treat the disease for many years. The datasets generated by applying A and B to the disease are called contrast groups and, main differences between the groups are the mean and distribution differences, referred to structural differences in this paper. However, contrast groups are only two samples obtained by limited applications or tests on A and B, and may be with missing values. Therefore, the differences derived from the groups are inevitably uncertain. In this paper, we propose a statistically sound approach for measuring this uncertainty by identifying the confidence intervals of structural differences between contrast groups. This method is designed significantly against most of those applications whose exact data distributions are unknown a priori, and the data may also be with missing values. We apply our approach to UCI datasets to illustrate its power as a new data mining technique for, such as, distinguishing spam from non-spam emails; and the benign breast cancer from the malign one. [Copyright &y& Elsevier]

The paper evaluates the data-information-knowledge-wisdom (DIKW) hierarchy. This hierarchy, also known as the 'knowledge hierarchy', is part of the canon of information science and management. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error that DIKW makes. The paper also identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a semantically and pragmatically sound way, that information is what is known in other literature as 'weak knowledge', that knowledge also is 'weak knowledge' and that wisdom is the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge management capability (KMC) is the source for organizations to gain the sustainable competitive advantage. KMC evaluation is a required work with strategic significance. However it still has not been addressed in the existing literatures. So the objective of this study is to investigate a fuzzy multiple attributes decision-making method (FMADM) for evaluating KMC. In this paper, a framework for evaluating KMC is presented, which includes two parts, one is an evaluation hierarchy with attributes, the other a judgment matrix model with two dimensions to identify the evaluation results of KMC. Then, a fuzzy linguistic approach is proposed to evaluate the KMC of organizations. The evaluation results of KMC obtained through the proposed approach are objective and unbiased due to two reasons. Firstly, the results are generated by a group of experts in the presence of motile attributes. Secondly, the fuzzy linguistic approach employed in this paper has more advantage to reduce distortion and losing of information than other fuzzy linguistic approaches. Through evaluation result of KMC, managers could judge the necessity to improve the KMC and determine which dimension of KMC is the most needed direction to improve. Additionally, an example is used to illustrate the availability of the proposed method. [Copyright &y& Elsevier]

Abstract: A good clustering method should provide flexible scalability on the number of dimensions as well as the size of a data set. This paper proposes a method of efficiently tracing the clusters of a high-dimensional on-line data stream. While tracing the one-dimensional clusters of each dimension independently, a technique which is similar to frequent itemset mining is employed to find the set of multi-dimensional clusters. By finding a frequently co-occurred set of one-dimensional clusters, it is possible to trace a multi-dimensional rectangular space whose range is defined by the one-dimensional clusters collectively. In order to trace such candidates over a multi-dimensional online data stream, a cluster-statistics tree (CS-Tree) is proposed in this paper. A k-depth node(k ⩽ d) in the CS-tree is corresponding to a k-dimensional rectangular space. Each node keeps track of the density of data elements in its corresponding rectangular space. Only a node corresponding to a dense rectangular space is allowed to have a child node. The scalability on the number of dimensions is greatly enhanced while sacrificing the accuracy of identified clusters slightly. [Copyright &y& Elsevier]