Systematic Analysis of Engineering Change Request Data - Applying Data Mining Tools to Gain New Fact-Based Insights

Large, complex system development projects take several years to execute. Such projects involve hundreds of engineers who develop thousands of parts and millions of lines of code. During the course of a project, many design decisions often need to be changed due to the emergence of new information. These changes are often well documented in databases, but due to the complexity of the data, few companies analyze engineering change requests (ECRs) in a comprehensive and structured fashion. ECRs are important in the product development process to enhance a product. The opportunity at hand is that vast amount of data on industrial changes are captured and stored, yet the present challenge is to systematically retrieve and use them in a purposeful way.This PhD thesis explores the growing need of product developers for data expertise and analysis. Product developers increasingly refer to analytics for improvement opportunities for business processes and products. For this reason, we examined the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge for ECR information needs, and applying mathematical tools for solution design and implementation.Results from extensive interviews generated a list of engineering information needs related to ECRs. When preparing for data mining, it is crucial to understand how the end user or the domain expert will and wants to use the extractable information. Results also show industrial case studies where complex product development processes are modeled using the Markov chain Design Structure Matrix to analyze and compare ECR sequences in four projects. In addition, the study investigates how advanced searches based on natural language processing techniques and clustering within engineering databases can help identify related content in documents. This can help product developers conduct better pre-studies as they can now evaluate a short list of the most relevant historical documents that might contain valuable knowledge.The main contribution is an application of data mining algorithms to a novel industrial domain. The state of the art is more up for the algorithms themselves. These proposed procedures and methods were evaluated using industrial data to show patterns for process improvements and cluster similar information. New information derived with data mining and analytics can help product developers make better decisions for new designs or re-designs of processes and products to ensure robust and superior products

Perspectives on reusing codified project knowledge: a structured literature review

Project documentation represents a valuable source of knowledge in project-based organizations. The practical reality is, however, that the knowledge codified in project documents is hardly re-used in future projects. A central problem in this context is the extensive amount of usually textual material. As a consequence, computer-assisted processes are indispensable in order to analytically manage the constantly growing and evolving databases of available project documents. The goal of this study is to summarize the current research focusing on the computer-assisted reuse of textually codified project knowledge and to define the corresponding state-of-the-art in this this specific field of information systems research. As a result of a literature review, this study structures the body of research contributions and outlines what kinds of computer-assisted techniques are incorporated, what practical application areas these solutions address, and in what business domains they are applied. In particular, this should point out research opportunities and thereby make a contribution to the further development of knowledge management in project environments

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Perspectives on reusing codified project knowledge: a structured literature review

Project documentation represents a valuable source of knowledge in project-based organizations. The practical reality is, however, that the knowledge codified in project documents is hardly re-used in future projects. A central problem in this context is the extensive amount of usually textual material. As a consequence, computer-assisted processes are indispensable in order to analytically manage the constantly growing and evolving databases of available project documents. The goal of this study is to summarize the current research focusing on the computer-assisted reuse of textually codified project knowledge and to define the corresponding state-of-the-art in this this specific field of information systems research. As a result of a literature review, this study structures the body of research contributions and outlines what kinds of computer-assisted techniques are incorporated, what practical application areas these solutions address, and in what business domains they are applied. In particular, this should point out research opportunities and thereby make a contribution to the further development of knowledge management in project environments

What to Do With All These Project Documentations? – Research Issues in Reusing Codified Project Knowledge

Project-based organizations invest a lot of time and effort into the extensive documentation of their projects. These project documents usually contain innovative knowledge and represent a significant source of information for the continual development of a learning organization. However, this codified project knowledge often remains untapped afterwards. A central problem in this context is the sheer information overload due to the often very large documentation stocks in project-based organizations. Against this background, this paper poses the following question: what can be done with the extensive project documentation after it has been created? To answer this question, two methodological approaches are combined. First, a literature review summarizes the current status quo of research in this special area. Then, expert interviews with IT project managers provide a deeper understanding of common practical problems. The combination of respective findings makes it possible to uncover research gaps and subsequently to define future needs for research. In sum, this paper formulates six research issues, which represent a starting point on the path to more comprehensive solutions for practically coping with large stocks of codified project knowledge

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

SOCIAL NETWORKS AS CHALLENGE FOR MARKETING INTELLIGENCE

Social networks are changing the way of connection and communication between people by increasing the amount of publicly available information and knowledge. People of similar professional backgrounds and occupations link to online communities to share information. This has a direct impact on what is one of the most difficult aspects of marketing intelligence "efficient and rapid collection and sharing of data and information". The aim of marketing intelligence is not only access data but manage them, analyze them and based on the analysis to make the right decisions related to customers, products, price, promotion, sale. Therefore, a large number of companies today are looking for solutions by marketing intelligence that will enable access to text data, analyze them and improve the quality of marketing decisions. The paper raises the hypothesis that it is possible to build a system for marketing intelligence that collects and analyzes data from social networks and uses the analysis results (information) to make precise, concise and accurate marketing decisions. In the paper is used the R programming language for marketing intelligence system and the R language demonstrated satisfactory simplicity and application power