Critical Success Factors in Data Mining Projects.

Utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the next generation of client-server enterprise information technology. To deploy data mining projects successfully, organizations need to know the key factors for successful data mining. This article concentrates on studying the data mining and the key factors for successful data mining, pointing out the progress of the data mining and crucial success factors (CSFs) currently exist in China.

Used by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in technical waters. Revealing the lessons known to the seasoned expert, yet rarely written down for the uninitiated, Practical Data Mining explains the ins-and-outs of the detection, characterization, and exploitation of actionable patterns in data. This working field manual outlines the what, when, why, and how of data mining and offers an easy-to-follow, six-step spiral process. Catering to IT consultants, professional data analysts, and sophisticated data owners, this systematic, yet informal treatment will help readers answer questions, such as: What process model should I use to plan and execute a data mining project? How is a quantitative business case developed and assessed? What are the skills needed for different data mining projects? How do I track and evaluate data mining projects? How do I choose the best data mining techniques? Helping you avoid common mistakes, the book describes specific genres of data mining practice. Most chapters contain one or more case studies with detailed projects descriptions, methods used, challenges encountered, and results obtained. The book includes working checklists for each phase of the data mining process. Your passport to successful technical and planning discussions with management, senior scientists, and customers, these checklists lay out the right questions to ask and the right points to make from an insiders point of view. Visit the books webpage for access to additional resourcesincluding checklists, figures, PowerPoint slides, and a small set of simple prototype data mining tools. http://www.celestech.com/PracticalDataMining

The thesis starts from the basic theory, introduces the method of physical data acquisition and the concept of data mining. Marketing evolves from traditional marketing schemes to data mining, which leads to the relationship between data mining technology and modern market management. It focuses on the method of obtaining user information and data mining technology in "cross-marketing", "integrated marketing", and "integrated marketing". Applications in "real-time marketing", "green marketing", "relationship marketing", and "network marketing". At the same time, combined with case analysis, the comprehensive application of data mining in "shopping basket analysis" is discussed and elucidated from the theoretical and technical level.

Data Mining, which is known as knowledge discovery in databases has been defined as the nontrivial extraction of implicit, previous unknown and potentially useful information from data. It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to human. In the paper the authors first introduce the idea, basic concept and process of data mining, then, an example and methods of the application of data mining in physical statistics are analyzed. Data mining is applied in physical training and evaluation, such as constitution data analyzing, PE industry and competitive sports. Thus; we think data mining becomes an important task of the scientific research of sports topic in future.

The United States confronts significant challenges within the water and wastewater sectors. According to the American Water Works Association (AWWA), a capital investment of $1.3 trillion is necessary for the repair and replacement of water infrastructures in the United States over the next 25 years. As alternative delivery methods are new in the water and wastewater sectors, the utility owners lack expertise and experience in executing these delivery methods. Thus, it is important to investigate the critical success items and factors of Design-Build (DB) methods, thereby ensuring that projects are completed within scheduled timeframes and budgets with better quality. To address this need, the present study undertakes a comprehensive literature review to identify the critical success factors associated with DB methods. Subsequently, a survey is conducted among both public and private utility owners to rank these items. The study identifies top critical success items related to completing projects within schedule and budget. Through principal component analysis, five budget- and schedule-related critical success factors were extracted. The outcomes of this study are the top critical success items and factors, thereby providing valuable insights for water and wastewater utility owners to focus on these items and factors intensively and help them execute the projects with great success.

A cost model to estimate the effort of data mining projects (DMCoMo)

This paper presents the QM-CRISP-DM for data mining applications in the context of production process improvement. It proposes concrete quality management tools for every phase of the CRISP-DM cycle. Especially for beginners, the described phases of the CRISP-DM cycle are often too general. As process improvement and error analysis are core competencies of quality management, there exist many tools with the potential of also supporting data analytics and mining projects. Therefore, we synthesize quality management tools with the CRISP-DM methodology to provide a user guide for data mining beginners. The presented QM-CRISP-DM is validated in a data mining project for the development of an error forecasting system in the field of electronics production.

PurposeA significant amount of digital repository research and development activity is taking place worldwide, which calls for the investigation of issues identified as critical to the development, management and sustainability of these repositories. The purpose of this paper is to establish a comprehensive set of critical success factors (CSFs) that would enable successful digital libraries implementation.Design/methodology/approachThe design of this study is exploratory in nature due to lack of previous research about CSFs in digital library projects. The paper builds the research framework to find CSFs for digital libraries initiatives by collecting possible factors from literature on CSFs of information systems; and feedback from ten experts who have had experience in digital library research and development as well as implementation, through e‐mail interviews. Six dimensions in the enterprise architecture framework (namely motivation, resource, people, process, location and time) needed to create the environment in which digital delivery can be effective and sustained, are mapped with the CSF established from the literature.FindingsThe results revealed six dimensions of CSFs and 36 potential success factor statements contributed by the study participants. Based on these findings, this paper postulates that successful deployment of a digital library implementation may depend on this set of success factors. The identification of these factors has important implications on digital library development.Originality/valueThere has been no comprehensive study on the success factors of digital libraries in the library and information science context, as such, no investigation of the factors in some real‐life contexts of digital library activities. It may help digital library start‐ups to understand what sources and services they need to develop to make their applications successful in an already very populated network. Both researchers and practitioners in the field of digital libraries may value the novelty and results of this study.

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

The current information age has increasingly required organizations to become data-driven. However, analyzing and managing raw data is still a challenging part of the data mining process. Even though we can find interview studies proposing design implications or recommendations for future visualization solutions in the data mining scope, they cover the entire workflow and do not fully focus on the challenges during the preprocessing phase and on how visualization can support it. Moreover, they do not organize a final list of insights consolidating the findings of other related studies. Hence, to better understand the current practice of enterprise professionals in data mining workflows, in particular, during the preprocessing phase, and how visualization supports this process, we conducted semi-structured interviews with 13 data analysts. The discussion about the challenges and opportunities based on the responses of the interviewees resulted in a list of 10 insights. This list was compared with the closest related works, improving the reliability of our findings and providing background, as a consolidated set of requirements, for future visualization research articles applied to visual data exploration in data mining. Furthermore, we provide greater details on the profile of the data analysts, the main challenges they face, and the opportunities that arise while they are engaged in data mining projects in diverse organizational areas.

This study explores the success factors of concrete recycling in construction projects, using Malaysia as a case study. The objectives include (1) identifying the critical success factors for concrete recycling in construction projects, (2) comparing the critical success factors between large enterprises (LEs) and small–medium enterprises (SMEs), and (3) developing constructs that group the critical success factors. First, a list of success factors was identified through semi-structured interviews with fifteen construction industry professionals and a systematic literature review of journal articles. This list was then incorporated into a questionnaire and disseminated to industry professionals. Eighty-nine valid responses were collected and analyzed using mean score ranking, normalization, agreement analysis, and factor analysis techniques. The analyses showed ten critical success factors for concrete recycling. The critical success factors include the availability of uniform standards for concrete recycling, adequate awareness among project stakeholders on concrete recycling, appropriate construction waste management plans, government policies to support concrete recycling, good marketing strategy for concrete recycling, good communication among employees, applications for recycled concrete in sub-industries, provisions in work method statements on concrete recycling, positive legislation toward concrete recycling, and availability of concrete recycling infrastructure. However, the percentage of agreement between SMEs and LEs for the ten critical success factors was only 22%. In other words, there is no consensus on criticality across organizational sizes. Finally, the critical success factors can be categorized into two interrelated groups: external and internal. This study contributes to the literature by analyzing the necessary success factors for concrete recycling. The study findings allow researchers and practitioners to develop strategies to promote concrete recycling.

In the context of post-apartheid period, there have been certain measures introduced to have employment equity in South Africa. The integration of black leadership in a white dominant companies was one among them. The purpose of this study is to identify the critical success factors that influence black leadership integration as per the reform measures defined after post-apartheid period in South Africa. The overall research question guiding this study is: What are the critical success factors that influence black leadership integration in companies listed on the Johannesburg Securities Exchange? The quantitative correlation research design utilizes a pre-validated survey to access the success factors that influence black leadership integration. To address the research question, the independent variables – corporate culture, leadership style, diversity and strategic leadership behaviours – were correlated with three dependent variables - tenure (years of service), job satisfaction and growth satisfaction (career development opportunities) as measures of successful leadership integration. The study finds statistically significant relationships amongst many of the 32 constructs identified in the literature which using factor analysis are described as fair treatment, job hopping, commitment to transformation, Black entrepreneurship and corporate culture. As defined by Black management, job satisfaction, career development (dependent variables), fair treatment and specific leaders behaviours (inspirational motivation and idealised attributes) (independent variables) as defined by MLQ 5X leadership survey are critical success measures that could influence Black leadership integration in Johannesburg Securities Exchange (JSE) listed companies. The results of this study provide strong evidence of critical success factors that have a statistically significant influence on successful Black leadership integration into historically White dominated South African companies.

PurposeThis paper aimed to identify critical success factors (CSFs) of building information modeling (BIM) implementation among architectural, engineering and construction (AEC) firms in Malaysia using quantitative statistical methods.Design/methodology/approachA questionnaire survey was conducted on the population of all AEC firms in Malaysia to rank the relative importance of 15 success factors (SFs) for BIM implementation in the firm on a 5-point Likert scale. The sampling frame consisted of all members registered with the Construction Industry Development Board (CIDB) from which 184 responses were received. The distinction between SFs and CSFs was achieved by a normalized mean cutoff value. Principal component analysis (PCA) was used to determine the relative groupings of the CSFs and structural equation modeling (SEM) were used to examine the underlying relationships between the CSF groupings.FindingsResults of this study validated 11 previously identified CSFs for successful implementation of BIM among Malaysian AEC firms. Non-technical factors such as management, leadership and coordination were found to have higher relevance compared to technical factors such as software and hardware. Ranking analysis of the CSFs found internal coordination factors to have more importance compared to external coordination factors. PCA revealed 4 principal components (PCs) from the 11 CSFs. Together, these four PCs explain 74.3% of the variance in the data. Also, six hypothesized relationships between these groupings were supported using PLS-SEM.Research limitations/implicationsThis study found an interaction between internal and external coordination factors for the implementation of BIM. Future research could explore the dynamics of both intra- and inter-firm coordination, which may create an environment suitable for BIM implementation.Practical implicationsThis study provides Malaysian AEC firms with a general strategy on how to approach the implementation of BIM within the firm. In addition, government initiatives should focus on increasing BIM competency of graduates entering the workforce. This can be achieved through the development of a national BIM curriculum. Apart from that, the implementation of BIM requires a radical shift in the traditional building delivery process, not just simply acquiring new software and hardware.Originality/valueThis study revealed that the interactions between different firms at the industry level can influence successful implementation of BIM at the firm level. CSFs for BIM implementation at the firm level have been extensively studied. However, few studies exist which explore the inter-firm collaborations at the industry level that facilitate successful BIM implementation.

This paper is to enhance our understanding about the second-order data mining. In particularly, we examine the effect of human cognition on the creation of intelligent knowledge during the second-order data mining process. Prior studies have suggested that human cognition plays an important role in the second-order data mining process during which intelligent knowledge was discovered. Given the knowledge that no single data mining model outperforms others for all problems, a common practice in data mining projects is to run multiple data mining models at first and then invite a group of people to collaboratively make judgments on these data mining models’ performance. These judgments often diverge. Little research exists to explain why these variations of human judgments occur.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come