A Revised Publication Model for ECML PKDD

ECML PKDD is the main European conference on machine learning and data mining. Since its foundation it implemented the publication model common in computer science: there was one conference deadline; conference submissions were reviewed by a program committee; papers were accepted with a low acceptance rate. Proceedings were published in several Springer Lecture Notes in Artificial (LNAI) volumes, while selected papers were invited to special issues of the Machine Learning and Data Mining and Knowledge Discovery journals. In recent years, this model has however come under stress. Problems include: reviews are of highly variable quality; the purpose of bringing the community together is lost; reviewing workloads are high; the information content of conferences and journals decreases; there is confusion among scientists in interdisciplinary contexts. In this paper, we present a new publication model, which will be adopted for the ECML PKDD 2013 conference, and aims to solve some of the problems of the traditional model. The key feature of this model is the creation of a journal track, which is open to submissions all year long and allows for revision cycles.Comment: 13 page

Astronomy and Computing: a New Journal for the Astronomical Computing Community

We introduce \emph{Astronomy and Computing}, a new journal for the growing population of people working in the domain where astronomy overlaps with computer science and information technology. The journal aims to provide a new communication channel within that community, which is not well served by current journals, and to help secure recognition of its true importance within modern astronomy. In this inaugural editorial, we describe the rationale for creating the journal, outline its scope and ambitions, and seek input from the community in defining in detail how the journal should work towards its high-level goals.Comment: 5 pages, no figures; editorial for first edition of journa

The Smart Book Recommender: An Ontology-Driven Application for Recommending Editorial Products

Promoting books and journals to the relevant research communities is an important task for major academic publishers. Unfortunately, identifying which are the best editorial products to market at a certain academic venue is a time-consuming and error-prone process. Here we present the Smart Book Recommender (SBR), an ontology-based recommender that supports the Springer Nature editorial team in selecting the editorial products to market at specific venues. SBR provides an interactive visualisation for analysing the topics characterizing conference series and books. It builds on a dataset of 27K books, journals, and conference proceedings annotated with topics from the Computer Science Ontology, a large-scale ontology of research areas. A user study showed that SBR is able to produce useful recommendations for both editors and researchers

Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?

Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcher-authors about how their data are mined, by whom, for what purposes, and to whose benefits.Comment: Forum Statement: Data Mining with Limited Access Text: National Forum. April 5-6, 2018. https://publish.illinois.edu/limitedaccess-tdm

WormBase: A modern Model Organism Information Resource

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase\u27s role as a founding member of the nascent Alliance of Genome Resources

Marketing and Data Science: Together the Future is Ours

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data

Internet Giants as Quasi-Governmental Actors and the Limits of Contractual Consent

Although the government’s data-mining program relied heavily on information and technology that the government received from private companies, relatively little of the public outrage generated by Edward Snowden’s revelations was directed at those private companies. We argue that the mystique of the Internet giants and the myth of contractual consent combine to mute criticisms that otherwise might be directed at the real data-mining masterminds. As a result, consumers are deemed to have consented to the use of their private information in ways that they would not agree to had they known the purposes to which their information would be put and the entities – including the federal government – with whom their information would be shared. We also call into question the distinction between governmental actors and private actors in this realm, as the Internet giants increasingly exploit contractual mechanisms to operate with quasi-governmental powers in their relations with consumers. As regulators and policymakers focus on how to better protect consumer data, we propose that solutions that rely upon consumer permission adopt a more exacting and limited concept of the consent required before private entities may collect or make use of consumer’s information where such uses touch upon privacy interests