The needs and benefits of Text Mining applications on Post-Project Reviews

Post Project Reviews (PPRs) are a rich source of knowledge and data for organisations - if organisations have the time and resources to analyse them. Too often these reports are stored, unread by many who could benefit from them. PPR reports attempt to document the project experience – both good and bad. If these reports were analysed collectively, they may expose important detail, e.g. recurring problems or examples of good practice, perhaps repeated across a number of projects. However, because most companies do not have the resources to thoroughly examine PPR reports, either individually or collectively, important insights and opportunities to learn from previous projects, are missed. This research explores the application of knowledge discovery techniques and text mining to uncover patterns, associations, and trends from PPR reports. The results might then be used to address problem areas, enhance processes, and improve customer relationships. A case study related to two construction companies is presented in this paper and knowledge discovery techniques are used to analyze 50 PPR reports collected during the last three years. The case study has been examined in six contexts and the results show that Text Mining has a good potential to improve overall knowledge reuse and exploitation

Multi-facet graph mining with contextualized projections

The goal of my doctoral research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various domains. Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. To overcome such barriers, my research harnesses the power of multi-facet context modeling and focuses on the principle of contextualized projections, which provides generic but subtle solutions to knowledge discovery over graphs with the mixtures of various semantic contexts

Aligning business processes and work practices

Current business process modeling methodologies offer little guidance regarding how to keep business process models aligned with their actual execution. This paper describes how to achieve this goal by uncovering and supervising business process models in connection with work practices using BAM. BAM is a methodology for business process modeling, supervision and improvement that works at two dimensions; the dimension of processes and the dimension of work practices. The business modeling component of BAM is illustrated with a case study in an organizational setting

Anticipating user eXperience with a desired product: The AUX framework

Positive user experience (UX) has become a key factor in designing interactive products. It acts as a differentiator which can determine a product’s success on the mature market. However, current UX frameworks and methods do not fully support the early stages of product design and development. During these phases, assessment of UX is challenging as no actual user-product interaction can be tested. This qualitative study investigated anticipated user experience (AUX) to address this problem. Using the co-discovery method, participants were asked to imagine a desired product, anticipate experiences with it, and discuss their views with another participant. Fourteen sub-categories emerged from the data, and relationships among them were defined through co-occurrence analysis. These data formed the basis of the AUX framework which consists of two networks which elucidate 1) how users imagine a desired product and 2) how they anticipate positive experiences with that product. Through this AUX framework, important factors in the process of imagining future products and experiences were learnt, including the way in which these factors interrelate. Focusing on and exploring each component of the two networks in the framework will allow designers to obtain a deeper understanding of the required pragmatic and hedonic qualities of product, intended uses of product, user characteristics, potential contexts of experience, and anticipated emotions embedded within the experience. This understanding, in turn, will help designers to better foresee users’ underlying needs and to focus on the most important aspects of their positive experience. Therefore, the use of the AUX framework in the early stages of product development will contribute to the design for pleasurable UX

Contextuality and Information Systems: how the interplay between paradigms can help

Through this paper, we theorize on the meanings and roles of context in the study of information systems. The literatures of information systems and information science both explicitly conceptualize information systems (and there are multiple overlapping definitions). These literatures also grapple with the situated and generalizable natures of an information system. Given these shared interests and common concerns, this paper is used as a vehicle to explore the roles of context and suggests how multi-paradigmatic research ??? another shared feature of both information science and information systems scholarship ??? provides a means to carry forward more fruitful studies of information systems. We discuss the processes of reconstructed logic and logic-in-use in terms of studying information systems. We argue that what goes on in the practice of researchers, or the logic-in-practice, is typified by what we are calling the contextuality problem. In response, we envision a reconstructed logic, which is an idealization of academic practices regarding context. The logic-in-use of the field is then further explained based on two different views on context. The paper concludes by proposing a model for improving the logic-in-use for the study of information systems

CASP-DM: Context Aware Standard Process for Data Mining

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs