A Knowledge Discovery Framework for Civil Infrastructure: A Case Study of the Intelligent Workplace

Large databases are becoming increasingly common in civil infrastructure applications. Although it is relatively simple to specifically query these databases at a low level, more abstract questions like ‘How does the environment affect pavement cracking?’ are difficult to answer with traditional methods. Data mining techniques can provide a solution for learning abstract knowledge from civil infrastruc-ture databases. However, data mining needs to be performed within a systematic process to ensure correct and reproducible results. Many decisions must be made during this process, making it difficult for novice analysts to apply data mining techniques thoroughly. This paper presents an application of a knowledge discovery process to data collected for an ‘intelligent’ building. The knowledge discovery process is illustrated and explained through this case study. Additionally, we discuss the importance of this case study in the context of a research effort to develop an interactive guide for the knowledge discovery process.     

Accessible wayfinding and navigation: a systematic mapping study

Urban environments, university campuses, and public and private buildings often present architectural barriers that prevent people with disabilities and special needs to move freely and independently. This paper presents a systematic mapping study of the scientific literature proposing devices, and software applications aimed at fostering accessible wayfinding and navigation in indoor and outdoor environments. We selected 111 out of 806 papers published in the period 2009–2020, and we analyzed them according to different dimensions: at first, we surveyed which solutions have been proposed to address the considered problem; then, we analyzed the selected papers according to five dimensions: context of use, target users, hardware/software technologies, type of data sources, and user role in system design and evaluation. Our findings highlight trends and gaps related to these dimensions. The paper finally presents a reflection on challenges and open issues that must be taken into consideration for the design of future accessible places and of related technologies and applications aimed at facilitating wayfinding and navigation.

     

Combining data mining and Game Theory in manufacturing strategy analysis

The work presented in this paper is result of a rapid increase of interest in game theoretical analysis and a huge growth of game related databases. It is likely that useful knowledge can be extracted from these databases. This paper argues that applying data mining algorithms together with Game Theory poses a significant potential as a new way to analyze complex engineering systems, such as strategy selection in manufacturing analysis. Recent research shows that combining data mining and Game Theory has not yet come up with reasonable solutions for the representation and structuring of the knowledge in a game. In order to examine the idea, a novel approach of fusing these two techniques has been developed in this paper and tested on real-world manufacturing datasets. The obtained results have been indicated the superiority of the proposed approach. Some fruitful directions for future research are outlined as well.     

Knowledge discovery in medicine: Current issue and future trend

Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users’ requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research.     

Discovering unexpected documents in corpora

Text mining is widely used to discover frequent patterns in large corpora of documents. Hence, many classical data mining techniques, that have been proven fruitful in the context of data stored in relational databases, are now successfully used in the context of textual data. Nevertheless, there are many situations where it is more valuable to discover unexpected information rather than frequent ones. In the context of technology watch for example, we may want to discover new trends in specific markets, or discover what competitors are planning in the near future, etc. This paper is related to that context of research. We have proposed several unexpectedness measures and implemented them in a prototype, called UnexpectedMiner, that can be used by watchers, in order to discover unexpected documents in large corpora of documents (patents, datasheets, advertisements, scientific papers, etc.). UnexpectedMiner is able to take into account the structure of documents during the discovery of unexpected information. Many experiments have been performed in order to validate our measures and show the interest of our system.     

Integrity constraints of manufacturing data bases

Today's manufacturing cost reduction and competitiveness largely depends on the application of automated manufacturing. Automated manufacturing could be even more efficient if computerizations of the process is taken one step further—to the integration of the design and manufacturing processes by means of or through a well-structured data base. This paper refers to several important issues related to integrity constraints in relational data bases. The literature review provides a summary of what has been done in this area by other researchers. In addition, some of the concepts proposed, developed, and even implemented before are introduced. A brief look at the classification of integrity constraints is performed by examining the real world and engineering and manufacturing worlds. This classification is supported by various manufacturing examples for each type of integrity constraints. Further, we discuss the issues of how to express and manage integrity constraints in relational data bases with particular emphasis on manufacturing applications.     

Simulation for manufacturing system design and operation: Literature review and analysis

This paper provides a comprehensive review of discrete event simulation publications published between 2002 and 2013 with a particular focus on applications in manufacturing. The literature is classified into three general classes of manufacturing system design, manufacturing system operation, and simulation language/package development. The paper further categorizes the literature into 11 subclasses based on the application area. The current review contributes to the literature in three significant ways: (1) it provides a wide coverage by reviewing 290 papers; (2) it provides a detailed analysis of different aspects of the literature to identify research trends through innovative data mining approaches as well as insights derived from the review process; and (3) it updates and extends the existing classification schemes through identification and inclusion of recently emerged application areas and exclusion of obsolete categories. The results of the literature analysis are then used to make suggestions for future research.     

Data mining in design of products and production systems

Data mining is acquiring its own identity by refining concepts from other disciplines, developing generic algorithms, and entering new application areas. Engineering design and manufacturing have been affected by the data mining pursuit. This paper outlines areas of product and manufacturing system design that are particularly suitable for data-mining applications. One of the emerging areas is innovation. The key challenges of data mining in the domains discussed in the paper are outlined.     

Intelligent condition monitoring using fuzzy inductive learning

Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.     

Fuzzy and rough formal concept analysis: a survey

Formal Concept Analysis (FCA) is a mathematical technique that has been extensively applied to Boolean data in knowledge discovery, information retrieval, web mining, etc. applications. During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress. In this paper, we give a systematic overview of the more than 120 papers published between 2003 and 2011 on FCA with fuzzy attributes and rough FCA. We applied traditional FCA as a text-mining instrument to 1072 papers mentioning FCA in the abstract. These papers were formatted in pdf files and using a thesaurus with terms referring to research topics, we transformed them into concept lattices. These lattices were used to analyze and explore the most prominent research topics within the FCA with fuzzy attributes and rough FCA research communities. FCA turned out to be an ideal metatechnique for representing large volumes of unstructured texts.     

Mining association rules for the quality improvement of the production process

Academics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment’s results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.     

Preface

The International Conference on Knowledge Science, Engineering and Management (KSEM), which was held in Belfast, Northern Ireland in 2010, was the fourth success in the series of such conferences. The conference focuses on the three themes of knowledge science, engineering and management, covering a wider range of research topics in the KSEM-related areas. This event offered an invaluable opportunity to bring together researchers, engineers and practitioners to present original work, the latest advances on knowledge representation, pioneered knowledge engineering, knowledge related systems, as well as discuss and debate practical challenges in deploying knowledge-based systems and research opportunities in the research community. To highlight research activities drawn by this event and provide an insight into the latest developments in the related areas, we present this special issue which is dedicated to all the delegates and researchers in the community who made the conference a success. We selected more than 10 papers that were presented in the conference and asked the authors to extend these papers. After careful review, we finally select 6 papers to be included in this special issue. This collection of extended versions of papers consists of the KSEM2010''s finest papers and covers the prominent topics of knowledge representation and reasoning, ontology engineering and applications, data mining and knowledge discovery. They represent state of the art of research in KSEM-related research areas.     

Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis

Manufacturing has adopted technologies such as automation, robotics, industrial Internet of Things (IoT), and big data analytics to improve productivity, efficiency, and capabilities in the production environment. Modern manufacturing workers not only need to be adept at the traditional manufacturing technologies but also ought to be trained in the advanced data-rich computer-automated technologies. This study analyzes the data science and analytics (DSA) skills gap in today's manufacturing workforce to identify the critical technical skills and domain knowledge required for data science and intelligent manufacturing-related jobs that are highly in-demand in today's manufacturing industry. The gap analysis conducted in this paper on Emsi job posting and profile data provides insights into the trends in manufacturing jobs that leverage data science, automation, cyber, and sensor technologies. These insights will be helpful for educators and industry to train the next generation manufacturing workforce. The main contribution of this paper includes (1) presenting the overall trend in manufacturing job postings in the U.S., (2) summarizing the critical skills and domain knowledge in demand in the manufacturing sector, (3) summarizing skills and domain knowledge reported by manufacturing job seekers, (4) identifying the gaps between demand and supply of skills and domain knowledge, and (5) recognize opportunities for training and upskilling workforce to address the widening skills and knowledge gap.     

Usability evaluation methods for the web: A systematic mapping study

Context

In recent years, many usability evaluation methods (UEMs) have been employed to evaluate Web applications. However, many of these applications still do not meet most customers’ usability expectations and many companies have folded as a result of not considering Web usability issues. No studies currently exist with regard to either the use of usability evaluation methods for the Web or the benefits they bring.

Objective

The objective of this paper is to summarize the current knowledge that is available as regards the usability evaluation methods (UEMs) that have been employed to evaluate Web applications over the last 14 years.

Method

A systematic mapping study was performed to assess the UEMs that have been used by researchers to evaluate Web applications and their relation to the Web development process. Systematic mapping studies are useful for categorizing and summarizing the existing information concerning a research question in an unbiased manner.

Results

The results show that around 39% of the papers reviewed reported the use of evaluation methods that had been specifically crafted for the Web. The results also show that the type of method most widely used was that of User Testing. The results identify several research gaps, such as the fact that around 90% of the studies applied evaluations during the implementation phase of the Web application development, which is the most costly phase in which to perform changes. A list of the UEMs that were found is also provided in order to guide novice usability practitioners.

Conclusions

From an initial set of 2703 papers, a total of 206 research papers were selected for the mapping study. The results obtained allowed us to reach conclusions concerning the state-of-the-art of UEMs for evaluating Web applications. This allowed us to identify several research gaps, which subsequently provided us with a framework in which new research activities can be more appropriately positioned, and from which useful information for novice usability practitioners can be extracted.     

An ACS-based framework for fuzzy data mining

Data mining is often used to find out interesting and meaningful patterns from huge databases. It may generate different kinds of knowledge such as classification rules, clusters, association rules, and among others. A lot of researches have been proposed about data mining and most of them focused on mining from binary-valued data. Fuzzy data mining was thus proposed to discover fuzzy knowledge from linguistic or quantitative data. Recently, ant colony systems (ACS) have been successfully applied to optimization problems. However, few works have been done on applying ACS to fuzzy data mining. This thesis thus attempts to propose an ACS-based framework for fuzzy data mining. In the framework, the membership functions are first encoded into binary-bits and then fed into the ACS to search for the optimal set of membership functions. The problem is then transformed into a multi-stage graph, with each route representing a possible set of membership functions. When the termination condition is reached, the best membership function set (with the highest fitness value) can then be used to mine fuzzy association rules from a database. At last, experiments are made to make a comparison with other approaches and show the performance of the proposed framework.     

Applications of association rule mining in health informatics: a survey

Association rule mining is an effective data mining technique which has been used widely in health informatics research right from its introduction. Since health informatics has received a lot of attention from researchers in last decade, and it has developed various sub-domains, so it is interesting as well as essential to review state of the art health informatics research. As knowledge discovery researchers and practitioners have applied an array of data mining techniques for knowledge extraction from health data, so the application of association rule mining techniques to health informatics domain has been focused and studied in detail in this survey. Through critical analysis of applications of association rule mining literature for health informatics from 2005 to 2014, it has been explored that, instead of the more efficient alternative approaches, the Apriori algorithm is still a widely used frequent itemset generation technique for application of association rule mining for health informatics. Moreover, other limitations related to applications of association rule mining for health informatics have also been identified and recommendations have been made to mitigate those limitations. Furthermore, the algorithms and tools utilized for application of association rule mining have also been identified, conclusions have been drawn from the literature surveyed, and future research directions have been presented.     

双库协同机制对知识发现主流发展的驱动

The paper, by a research report, summarizes emergence and definition of double bases cooperating mecha-nism, and introduces its driving force and influence to many sides of main stream of knowledge discovery from struc-tural model to algorithm , from structuring data mining to complex type data mining. The influence also expands tophilosophy field. It has been above five years from proposing it to now. Summarizing it makes us learn a thing clear-ly : its functions are not simply improvement to algorithm, are to bring forward many new structural models and tech-nology methods . It answers those urgent questions in the one paragraph of the paper to a greater extent. So we maysay: double bases cooperating mechanism has important driving force to main stream of knowledge discovery.     

Answering ad hoc aggregate queries from data streams using prefix aggregate trees

In some business applications such as trading management in financial institutions, it is required to accurately answer ad hoc aggregate queries over data streams. Materializing and incrementally maintaining a full data cube or even its compression or approximation over a data stream is often computationally prohibitive. On the other hand, although previous studies proposed approximate methods for continuous aggregate queries, they cannot provide accurate answers. In this paper, we develop a novel prefix aggregate tree (PAT) structure for online warehousing data streams and answering ad hoc aggregate queries. Often, a data stream can be partitioned into the historical segment, which is stored in a traditional data warehouse, and the transient segment, which can be stored in a PAT to answer ad hoc aggregate queries. The size of a PAT is linear in the size of the transient segment, and only one scan of the data stream is needed to create and incrementally maintain a PAT. Although the query answering using PAT costs more than the case of a fully materialized data cube, the query answering time is still kept linear in the size of the transient segment. Our extensive experimental results on both synthetic and real data sets illustrate the efficiency and the scalability of our design. Moonjung Cho is a Ph.D. candidate in the Department of Computer Science and Engineering at State University of New York at Buffalo. She obtained her Master from same university in 2003. She has industry experiences as associate researcher for 4 years. Her research interests are in the area of data mining, data warehousing and data cubing. She has received a full scholarship from Institute of Information Technology Assessment in Korea. Jian Pei received the Ph.D. degree in Computing Science from Simon Fraser University, Canada, in 2002. He is currently an Assistant Professor of Computing Science at Simon Fraser University, Canada. In 2002–2004, he was an Assistant Professor of Computer Science and Engineering at the State University of New York at Buffalo, USA. His research interests can be summarized as developing advanced data analysis techniques for emerging applications. Particularly, he is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by Natural Sciences and Engineering Research Council of Canada (NSERC) and National Science Foundation (NSF). He has published over 70 papers in refereed journals, conferences, and workshops, has served in the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught in the areas of database and data mining. Ke Wang's research interests include database technology, data mining and knowledge discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real life problems. Ke Wang has published in database, information retrieval, and data mining conferences, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM and ICDM. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences including DASFAA, ICDE, ICDM, PAKDD, PKDD, SIGKDD and VLDB.     

Knowledge acquisition for decision support systems on an electronic assembly line

Increasing global competition has made many manufacturing companies recognize that competitive manufacturing in terms of low cost and high quality is crucial for success. Real-time process control and production optimization are, however, extremely challenging areas because manufacturing processes are getting ever more complex and involve many different parameters. This is a major problem when building decision support systems especially in electronics manufacturing. Although problem-solving is a knowledge intensive activity undertaken by people on the production floor, it is quite common to have large databases and run blindly feature extraction and data mining methods. Performance of these methods could, however, be drastically increased when combined with knowledge or expertise of the process.This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the efficient acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the factory-wide sharing of the resulting information for an improved defect management. Software with dedicated interfaces has been developed using a knowledge representation that supports portability and flexibility of the system. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.