Daniel J. Lewis

Bio: Daniel J. Lewis is an academic researcher from University of Bristol. The author has contributed to research in topics: Fuzzy set & Fuzzy classification. The author has an hindex of 3, co-authored 5 publications receiving 666 citations.

What is web 2.0

Abstract: Ask a dozen Internet experts what the term Web 2.0 means, and you'll get a dozen different answers. Some say that Web 2.0 is a set of philosophies and practices that provide Web users with a deep and rich experience. Others say it's a new collection of applications and technologies that make it easier for people to find information and connect with one another online. A few journalists maintain that the term doesn't mean anything at all -it's just a marketing ploy used to hype social networking sites.

X-μ fuzzy association rule method

Abstract: Association rule mining theory, and practice, requires the ability to calculate the cardinalities of subsets. In association rule mining on fuzzy sets, this is also the case. However, there are multiple options for calculating cardinalities due to the nature of fuzzy sets. In this paper we introduce the “X-μ Fuzzy Association Rule method” of calculation, a methodology for use within fuzzy association rule mining. This method uses the X-μ representation of fuzzy sets and its respective cardinality calculation, which retains the fuzzy nature of fuzzy set cardinality through the full process of association rule processing.

The X-μ Approach: In Theory and Practice

Abstract: Criticisms exist for fuzzy set theory which do not reside in classical (or “crisp”) set theory, some such issues exhibited by fuzzy set theory regard the law of excluded middle and law of contradiction. There are also additional complexities in fuzzy set theory for monotonicity, order and cardinality. Fuzzy set applications either avoid these issues, or use them to their advantage. The X-μ approach, however, attempts to solve some of these issues through analysis of inverse fuzzy membership functions. Through the inverse fuzzy membership function it is possible to computationally calculate classical set operations over an entire fuzzy membership. This paper firstly explores how the X-μ approach compares to both classical/crisp set theory and conventional fuzzy set theory, and explores how the problems regarding the laws of excluded middle and contradiction might be solved using X-μ. Finally the approach is implemented and applied to an area of big data over the world-wide-web, using movie ratings data.

Approximating Grouped Fixed Effects Estimation via Fuzzy Clustering Regression

Abstract: We propose a new, computationally-efficient way to approximate the “grouped fixed-effects” (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of unobserved heterogeneity. To do so, we generalize the fuzzy C-means objective to regression settings. As the regularization parameter m approaches 1, the fuzzy clustering objective converges to the GFE objective; moreover, we recast this objective as a standard Generalized Method of Moments problem. We replicate the empirical results of Bonhomme and Manresa (2015) and show that our estimator delivers almost identical estimates. In simulations, we show that our approach delivers improvements in terms of bias, classification accuracy, and computational speed.

Business intelligence and analytics: from big data to big impact

Abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

Web 2.0: A New Wave of Innovation for Teaching and Learning?

Abstract: © 2 0 0 6 B r y a n A l e x a n d e r chronological structure implies a different rhetorical purpose than a Web page, which has no inherent timeliness That altered rhetoric helped shape a different audience, the blogging public, with its emergent social practices of blogrolling, extensive hyperlinking, and discussion threads attached not to pages but to content chunks within them Reading and searching this world is significantly different from searching the entire Web world Still, social software does not indicate a sharp break with the old but, rather, the gradual emergence of a new type of practice These sections of the Web break away from the page metaphor Rather than following the notion of the Web as book, they are predicated on microcontent Blogs are about posts, not pages Wikis are streams of conversation, revision, amendment, and truncation Podcasts are shuttled between Web sites, RSS feeds, and diverse players These content blocks can be saved, summarized, addressed, copied, quoted, and built into new projects Browsers respond to this boom in

Sociality Through Social Network Sites

Abstract: This chapter reports authoritative insights into one of the most significant developments related to social interaction – social network sites – and offers an analytic framework for exploring these new sites, while underscoring the centrality of social interaction since the Internet's earliest days, such as through email. Social network sites (SNSs) presented several characteristics that made it possible for individuals to easily update their profiles. The implicit role of communication and information sharing has become the driving motivator for participation. The concept of ‘Web 2.0’ was an industry-driven phenomenon, hyped by the news media and by business analysts alike. Social network sites emerged out of the Web 2.0 and social media phenomena, mixing new technologies and older computer-mediated communication practices infused by tech industry ideals. Server-level data offer a unique opportunity to access elaborated behavioural data about what people are doing on SNSs.

Impression Management 2.0. The Relationship of Self-Esteem, Extraversion, Self-Efficacy, and Self-Presentation Within Social Networking Sites.

Abstract: Investigated the relationship between self-reported (offline) personality traits and (online) self-presentation in social networking profiles on the Internet. Social networking sites like MySpace, Facebook, and StudiVZ are popular means of communicating personality. Recent theoretical and empirical considerations of homepages and Web 2.0 platforms show that impression management is a major motive for actively participating in social networking sites. 58 users (mean age 22.98 years) of the German Web 2.0 site, StudiVZ.net, completed online questionnaires assessing extraversion (NEO Personality Inventory, NEO-FFI), self-esteem (Rosenberg Self-Esteem Scale, RSES), and efficacy of self-presentation, and their profiles were subject to a content analysis. Findings reveal a slight influence of extraversion on self-presentation, whereas there was no significant effect for self-esteem. Moveover, analyses of respondents' profiles showed that self-efficacy with regard to impression management is strongly related to the number of virtual friends, the level of profile detail, and the style of the personal photo.