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With the rapid development of information technology, knowledge management has become an important means for enterprises, organizations, and individuals to gain advantages in competition. The design and implementation of Knowledge management is one of the key links of knowledge management. This paper discussed the design of Knowledge management based on Support Vector Machine (SVM), in order to improve the efficiency and quality of knowledge management. This paper introduced the application of SVM in Knowledge management, and discussed how to design an excellent Knowledge management. The research results showed that the response time of the Knowledge management designed in this paper was 87 seconds when the number of users was 300. When the number of users exceeded 300, although the response time was longer, its growth rate slowed down. SVM can be used in knowledge classification, entity recognition and relationship extraction, and can greatly improve the efficiency of Information extraction and knowledge management. [ABSTRACT FROM AUTHOR]

Abstract Data mining has been an active research area for a couple of decades, yet the complicated nature of data mining is still not fully understood. One common misunderstanding of data mining is: Give me the data set, and data mining tools will show me the hidden knowledge. However, this thinking is quite naive, and is not realistic in many real world applications. In this paper, we explore extended data mining, which has the ultimate goal of automatically collecting additional data when needed for effective data mining. Existing web crawling and scraping techniques can be incorporated, but additional steps are still needed. In this paper, we examine important technical aspects for extended data mining via web crawling and scraping. [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]

Present-day society shows keen interest in the field of medical treatment, and the diagnostic mode is now developing toward doctor–patient shared decision-making. Therefore, a patient׳s source of medical information is quite important, with that source needing to be reliable, accurate, and easily accessible. Ensuring that informational sources meet these requirements becomes a challenge with the development of the informational network, which causes the amount of material available online to steadily increase and the general public to become more aware of health- and medical-treatment-related information. Therefore, focusing on the medical information seeker, this paper will discuss two user identities: patients and healthcare professionals. For patients, online medical articles are a major source of medical information; patients with concerns about diseases often search for their symptoms on the Internet and look for related medical information. However, online medical articles are usually long, so patients sometimes self-diagnose their disease or determine the severity of their condition based on only part of an article or on limited, incomplete, or even inaccurate information in several articles related to the symptoms searched out. Consequently, patients may misdiagnose their condition or underestimate the severity or seriousness of the condition and delay treatment. In addition, present medical technology advances rapidly, so physicians and other healthcare professionals must obtain the latest medical information from the Internet. However, searching for and reading professional in-depth medical articles to find required, critical information online is time-consuming, creating a time-management challenge. To address these aforementioned problems, this paper develops an Automatic Key Medical Information Generating model, uses medical articles as the basis of analysis, and develops and designs a medical article key-information-generating methodology applicable to medical article retrieval and reading. The word segmentation is implemented for the articles according to the Chinese Knowledge and Information Processing (CKIP) of Academia Sinica, and the medical articles are then distributed to various clusters by the clustering technology of this model, so that the medical information seeker can conduct a rapid search for the required medical article information. When the medical information seeker finds the target medical article, the article׳s key statements are screened out by the keywords rule base created in this paper, and the key statement scores are calculated. The medical article key information is sequenced according to the key statements so as to generate the medical article key information table. In addition, a web-based key-medical-information-generating system will be built based on the proposed model, and the effectiveness and feasibility of the model and technology will be evaluated using a real-world case. In summary, this paper presents a model to analyze the keywords and key statements of medical articles to generate a medical article key information table. This model can help the medical information seeker look for the required health information rapidly and accurately on the Internet, shortening the time for screening medical information and increasing the probability of obtaining the required information. [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]

Clustering has always been an exploratory but critical step in the knowledge discovery process. Often unsupervised, the clustering task received a huge interest when reinforced by different kinds of inputs provided by the user. This paper presents an approach giving the possibility to incorporate business knowledge in order to guide the clustering algorithm. A formalization of the fact that an intuitive a priori prioritization of the variables might exist, is presented in this paper and applied in a direct marketing context using recent data. By providing the analyst with a new approach offering different clustering perspectives, this paper proposes a straightforward way to apply constrained clustering with soft attribute-level constraints based on feature order preferences. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

Abstract: Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context. [Copyright &y& Elsevier]

Abstract: Many enterprises have been devoting a significant portion of their budget to new product development (NPD) in order to distinguish their products from those of their competitors, and to make them better fit the needs and wants of customers. Hence, businesses should develop products that fulfill the customer demands, since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper presents the product map obtained from data mining results, which investigates the relationships among customer demands, product characteristics, and transaction records, using the Apriori algorithm as a methodology of association rules for data mining. The product map shows that different knowledge patterns and rules can be extracted from customers to develop new cosmetic products and possible marketing solutions. Accordingly, this paper suggests that the cosmetics industry should extract customer knowledge from the demand side and use this as a knowledge resource on its supply chain for new product development. [Copyright &y& Elsevier]

Abstract: In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria. [Copyright &y& Elsevier]

Sequential pattern mining is crucial to data mining domains. This paper proposes a novel data mining approach for exploring hierarchical tree structures, named tree-like patterns, representing the relationships for a pair of items in a sequence. Using tree-like patterns, the relationships for a pair of items can be identified in terms of the cause and effect. A novel technique that efficiently counts support values for tree-like patterns using a queue structure is proposed. In addition, this paper addresses an efficient scheme for determining the frequency of a tree-like pattern in a sequence using a dynamic programming approach. Each tree-like pattern embedded in a sequence is considered to have a certain valuable meaning or the degree of importance used in different applications. Two addressed formulas are applied to determine the degree of significance for a specific sequence, which denotes the degree of consecutive items in a tree-like pattern for a sequence. The larger the degree of significance a tree-like pattern has, the more the tree-like pattern is compacted in the sequence. The characteristics differentiating the explored patterns from those obtained with other schemes are discussed. A simulation analysis of the proposed data mining approach is utilized to demonstrate its efficacy. Finally, the proposed approach is designed and implemented in a data mining system integrated into a novel e-learning platform. [Copyright &y& Elsevier]

Abstract: Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch National Public Works Department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the process perspective, (2) the organizational perspective, and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular. [Copyright &y& Elsevier]

Abstract: This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial–temporal data warehouse system designed for storing and clustering a wide range of spatial–temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results. [Copyright &y& Elsevier]

Abstract: Using the “tipping point” as a metaphor, the paper argues that the construction industry is ready to reap the benefits of using information technologies. Construction IT as an appropriating discipline is enabling us to have devices to speculate about designs, to have innovative modes of collaboration, communication and practice. “Virtuality” is the password for construction professionals to cross over into the experimental realms of installation and operation. The paper identifies the ingredients needed to ‘tip’ the balance for an accelerated penetration of information technologies into the construction industry. Core technologies are described as the prime mover of radical transformation in the construction industry. [Copyright &y& Elsevier]

Abstract: The concept of knowledge and knowledge management is not new. Researchers identified the practice of knowledge management as early as 4000 years ago. However, knowledge management systems (KMS), which involve the application of IT systems and other organizational resources to manage knowledge strategically, are a relatively recent phenomenon. While the literature on knowledge management covers various issues, it lacks comprehensive studies of factors and variables of adoption and diffusion of KMS. This paper studies these factors and variables in the context of some Australian organizations. A qualitative field study is undertaken in this research, where six organizations of various sizes, all in various stages of KMS adoption and diffusion, are studied via interviews with key personnel. Content analysis is then performed to extract the factors and variables and a comprehensive model of KMS adoption and diffusion is developed. The results of the interviews identify four major variables affecting KMS diffusion as: organizational culture, top management support, benefits to individuals, and dream of KMS. The paper also highlights the research and managerial implications of the KMS diffusion model. [Copyright &y& Elsevier]

Abstract: Ontologies are the foundation on which knowledge-intensive problem solvers depend. Ontologies encapsulate domain concepts, tasks, problem-solving knowledge, and methods. This paper reports on a research project on ontology development for constructability assessment in steel frame structures. It discusses the conceptualisation and formal knowledge representation for the problem domain. The resulting knowledge structures facilitate reasoning in intelligent systems. The paper uses a case study to demonstrate the potential applications of ontologies in developing intelligent rule-based agents for constructability assessment, and reports on the experiments conducted to teach problem-solving agents. The results indicate great potential in developing high utility ontology-driven agents for use in organisational corporate knowledge management. The paper discusses the findings from the research project. The authors propose a constructability ontology for steel structures and an agent framework for constructability design knowledge acquisition. Recommendations are given for further work in developing ontologies for construction applications. [Copyright &y& Elsevier]

This paper discusses the concept and application of Web warehousing—the combination of data warehousing and Web technology. Data warehousing is a critical aspect of decision support systems. The Internet made it possible to apply Web technology to traditional data warehousing, which resulted in improved cost savings and productivity. The relationship between Web warehousing and traditional data warehousing is assessed in detail, revealing the new opportunities and challenges of Web warehousing in business settings. The Web’s contribution to Web warehousing is examined to explain how efficiency improvements are achieved. The integrated data in Web warehousing create a close tie between IT departments and other business functions. Security is also a key issue in Web-based warehouses. To win management support, a Web warehouse team must identify and promote the advantages of Web warehousing. This paper addresses key factors requisite for successful implementation of Web warehousing, thus enabling organizations to benefit from combining data warehousing and Web technology. [Copyright &y& Elsevier]

Abstract The construction industry uses project work as its main working method and is at the same time exposed to serious risks to health, safety and environment (HSE). Project managers play an important role to ensure high performance on HSE in construction projects, and the first position gained by many engineers after graduation is often the role of project manager. This paper examines how different aspects of HSE are included in the teaching of project management (PM) in the bachelor education of construction engineers in Norway. The examination includes analysing learning outcomes, curriculum, teaching and final exams in PM courses at the three largest educational institutions in respect to graduating bachelors in construction engineering in Norway. The findings show that the inclusion of HSE in PM courses varies and that students of construction engineering need to learn more about how to influence HSE in projects. Two elements are largely lacking in the education: 1) how to influence HSE performance during the early stages of construction projects, and 2) how to influence safety culture in projects. A stronger inclusion of these perspectives in teaching could improve the ability of future engineers to reinforce effective health and safety measures in the construction industry. [ABSTRACT FROM AUTHOR]

Abstract Project control requires many processes which differ from one project management standard to another. In this paper, project control is investigated based on two different standards: PMBOK and PRINCE2. The aim is to identify which processes are central to project control through network analysis. The results open up a new vision of looking at project control by stating that not only the traditional triangle of quality, time, and cost control but also change control and corrective action decision-making are at the core of project control. [ABSTRACT FROM AUTHOR]

Abstract Data mining is one of the most prominent methodologies demonstrating efficiency in various tasks related to pattern detection. Applications of data mining for fraud detection are numerous, for the detection of fraud, ranging from credit card fraud to taxation fraud, are numerous. However, the applications of data mining for fraud related to internal control are scarce. The goal of this paper is to present the application of data mining techniques (the CHAID decision tree) for discovering patterns in internal control-related fraud in one project-based organization. [ABSTRACT FROM AUTHOR]

Abstract As stressed by many academics and practitioners, knowledge is the key source for competitive advantage in modern organizations. These authors have come to realize that knowledge management is the key to organizational performance and survival in continuously changing economic, technological, political, and social environment. To be effective, deployment of knowledge management within organizations requires support materialized by knowledge management systems. Nevertheless, despite the richness of the literature on knowledge management systems, the definitions proposed for this concept are largely technology-oriented. This restrictive view which considers knowledge management systems as software tools, resulted in the failure of knowledge management in many organizations. We think that knowledge management systems have four important facets - a technological facet, a human resources facet, a process facet, and a context facet – which determine the drivers of their effectiveness. In this paper, we propose a conceptual model - based on the structuration theory - which demonstrates that the importance and the characteristics of the four facets of knowledge management systems depend on the knowledge management strategy, the knowledge management activities, and the external environment. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

A knowledge warehousing process aims to build an intelligent decision support system. It collects, homogenizes, integrates and stores knowledge for a decision-making process. In this paper, we are interested in knowledge integration. More accurately, we propose an integration process for knowledge homogenized/modeled according to the MOT (Modeling with Object Types) knowledge model. This integration process consists of three ordered steps based on the type of schemas to integrate and their similarity. For this process, we define five integration rules based on semantic relationships between elements of MOT knowledge models, and then we develop an algorithm using these integration rules. [ABSTRACT FROM AUTHOR]

In classification, every feature of the data set is an important contributor towards prediction accuracy and affects the model building cost. To extract the priority features for prediction, a suitable feature selector is schemed. This paper proposes a novel memetic based feature selection model named Shapely Value Embedded Genetic Algorithm (SVEGA). The relevance of each feature towards prediction is measured by assembling genetic algorithms with shapely value measures retrieved from SVEGA. The obtained results are then evaluated using Support Vector Machine (SVM) with different kernel configurations on 11 + 11 benchmark datasets (both binary class and multi class). Eventually, a contrasting analysis is done between SVEGA-SVM and other existing feature selection models. The experimental results with the proposed setup provides robust outcome; hence proving it to be an efficient approach for discovering knowledge via feature selection with improved classification accuracy compared to conventional methods. [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]

Despite the critical importance of knowledge management (KM) in helping organizations to improve performance and achieve the desired goals, the reality of the application of KM indicates that there are plenty of obstacles; most prominent of which are the difficulty of tacit KM, the poor cooperation and sharing in KM, and the difficulty of dealing with its techniques. This paper aims to determine how to take advantage of the virtual communities of practice that employ Web 2.0 technologies to overcome the obstacles of KM. Employing the descriptive approach based on theoretical analysis of literature, the results revealed that Web 2.0 technologies involve important applications in personal KM, supporting participation in participatory KM, in addition to supporting communication and interaction mechanisms. Moreover, results indicated that Web 2.0-based-communities of practice play major roles in capturing tacit knowledge, facilitating innovation, as well as knowledge sharing and collaboration. [ABSTRACT FROM AUTHOR]

Attribute reduction is an important preprocessing step in data mining and knowledge discovery. The effective computation of an attribute reduct has a direct bearing on the efficiency of knowledge acquisition and various related tasks. In real-world applications, some attribute values for an object may be incomplete and an object set may vary dynamically in the knowledge representation systems, also called decision systems in rough set theory. There are relatively few studies on attribute reduction in such systems. This paper mainly focuses on this issue. For the immigration and emigration of a single object in the incomplete decision system, an incremental attribute reduction algorithm is developed to compute a new attribute reduct, rather than to obtain the dynamic system as a new one that has to be computed from scratch. In particular, for the immigration and emigration of multiple objects in the system, another incremental reduction algorithm guarantees that a new attribute reduct can be computed on the fly, which avoids some re-computations. Compared with other attribute reduction algorithms, the proposed algorithms can effectively reduce the time required for reduct computations without losing the classification performance. Experiments on different real-life data sets are conducted to test and demonstrate the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Automatic fall detection is a major issue in the health care of elderly people. In this task the ability to discriminate in real time between falls and normal daily activities is crucial. Several methods already exist to perform this task, but approaches able to provide explicit formalized knowledge and high classification accuracy have not yet been developed and would be highly desirable. To achieve this aim, this paper proposes an innovative and complete approach to fall detection based both on the automatic extraction of knowledge expressed as a set of IF-THEN rules from a database of fall recordings, and on its use in a mobile health monitoring system. Whenever a fall is detected by this latter, the system can take immediate actions, e.g. alerting medical personnel. Our method can easily overcome the limitations of other approaches to fall detection. In fact, thanks to the knowledge gathering, it overcomes both the difficulty faced by a human being dealing with many parameters and trying to find out which are the most suitable, and also the need to apply a laborious trial-and-error procedure to find the values of the related thresholds. In addition, in our approach the extracted knowledge is processed in real time by a reasoner embedded in a mobile device, without any need for connection to a remote server. This proposed approach has been compared against four other classifiers on a database of falls simulated by volunteers, and its discrimination ability has been shown to be higher with an average accuracy of 91.88%. We have also carried out a very preliminary experimental phase. The best set of rules found by using the previous database has allowed us to achieve satisfactory performance in these experiments as well. Namely, on these real-world falls the obtained results in terms of accuracy, sensitivity, and specificity are of about 92%, 86%, and 96%, respectively. [ABSTRACT FROM AUTHOR]

During the development of object-oriented software system; huge amount of software-development data especially change-history is stored in software-repositories. This change-history can be mined to measure the quality of the software systems. In this paper, we present change-history based package-changeability measures namely; a) Package Change-Proximity, b) Change-Coupling Index and c) Package-Changeability. These measures are defined and computed on the basis of change-coupling among packages. A case study has been performed and findings derived from the results are discussed. Our results show that these package-changeability measures can be very helpful for development team to predict and quantify the change-coupling among packages. [ABSTRACT FROM AUTHOR]

Data analysis plays amajor role in innovation of new trends in many applications.In most of the current applicationdatabases is being updated day to day. In order to adopt these changes, there is a need to update the present technologies and data mining algorithms in support of changing data. In many of the clustering algorithms the user has to specify the optimum number of clusters prior to execution, for static databases this value remains constant whereas, in the case of dynamic databases the value should be changed. In this paper,we implemented a method to find optimal number of clusters based onfuzzy silhouette on dynamic data by comparing traditional clustering on synthetic data and dynamic customer segmentation. [ABSTRACT FROM AUTHOR]

Information Technology (IT) is a vital and an integral part of every organization. IT executives are constantly faced with problems that are difficult to tackle and time consuming. Experience is required to solve these problems easier and faster. We can utilize case-based reasoning (CBR), data mining and information retrieval (IR) techniques to automate IT problem solving and experience management. In this paper, we propose an IT ontology-based system for semantic retrieval that increases the efficiency and quality of IT support service. The proposed approach retrieves similar problem/solution pairs based on the concepts in the query and performs better than the traditional keyword-based approach especially in cases where the keywords of the relevant documents do not match the keywords in the query. [ABSTRACT FROM AUTHOR]

With the advent of K-economy, all leading business organizations are incorporating knowledge management as an integral part of their functioning. Education sector, though a non-profit domain has also witnessed increase in the implementation of business intelligence and knowledge centric processes over the years. The paper exemplifies the importance of knowledge evaluation in higher education organizations, through measures developed using data mining techniques. Applying these knowledge measures or indicator in the education domain eventually help the higher education organizations to establish themselves as knowledge centric higher education organizations (KCHEO). [ABSTRACT FROM AUTHOR]

Implementation of data mining algorithms with low cost is one of the challenging tasks in the present world of massively increasing data. The key idea of this paper is to utilize the functionalities of Mathematica which is freely accessible on Raspberry Pi for the purpose of implementing Multi-label classification algorithm with low cost. With the facilities available in Mathematica software for Raspberry Pi, the line of code required for implementing data mining algorithms can be reduced sufficiently. Use of Random Kitchen Sink algorithm improves the accuracy of Multi-label classification and brings improvement in terms of memory usage for large dataset. [ABSTRACT FROM AUTHOR]

A major challenge to digital forensic analysis is the ongoing growth in the volume of data seized and presented for analysis. This is a result of the continuing development of storage technology, including increased storage capacity in consumer devices and cloud storage services, and an increase in the number of devices seized per case. Consequently, this has led to increasing backlogs of evidence awaiting analysis, often many months to years, affecting even the largest digital forensic laboratories. Over the preceding years, there has been a variety of research undertaken in relation to the volume challenge. Solutions posed range from data mining, data reduction, increased processing power, distributed processing, artificial intelligence, and other innovative methods. This paper surveys the published research and the proposed solutions. It is concluded that there remains a need for further research with a focus on real world applicability of a method or methods to address the digital forensic data volume challenge. [ABSTRACT FROM AUTHOR]

Abstract: One of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules. [Copyright &y& Elsevier]

Abstract: This paper proposes an open and distributed clinical decision support system architecture. This technical architecture takes advantage of Electronic Health Record (EHR), data mining techniques, clinical databases, domain expert knowledge bases, available technologies and standards to provide decision-making support for healthcare professionals. The architecture will work extremely well in distributed EHR environments in which each hospital has its own local EHR, and it satisfies the compatibility, interoperability and scalability objectives of an EHR. The system will also have a set of distributed knowledge bases. Each knowledge base will be specialized in a specific domain (i.e., heart disease), and the model achieves cooperation, integration and interoperability between these knowledge bases. Moreover, the model ensures that all knowledge bases are up-to-date by connecting data mining engines to each local knowledge base. These data mining engines continuously mine EHR databases to extract the most recent knowledge, to standardize it and to add it to the knowledge bases. This framework is expected to improve the quality of healthcare, reducing medical errors and guaranteeing the safety of patients by helping clinicians to make correct, accurate, knowledgeable and timely decisions. [Copyright &y& Elsevier]

Abstract: An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new research field. The current paper reviews this research on HR data mining to systematically uncover recent advancements and suggest areas for future work. Based on the approach of domain driven data mining, an initial framework with significant domain-specific requirements is elaborated. Relevant research contributions are identified and reviewed against the background of this framework. The review reveals that HRM constitutes a noteworthy new domain of data mining research that is dominated by method- and technology-oriented work. However, specific domain requirements, such as evaluating the domain success or complying with legal standards, are frequently not recognized or considered in current research. Therefore, the systematic consideration of domain-specific requirements is demonstrated here to have significant implications for future research on data mining in HRM. [Copyright &y& Elsevier]