Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Mixed reality participants in smart meeting rooms and smart home enviroments

Human–computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments

Generating collaborative systems for digital libraries: A model-driven approach

This is an open access article shared under a Creative Commons Attribution 3.0 Licence (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2010 The Authors.The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework

Advanced Visualizations Tools for CERN Institutional Data

Project Specification: The aim of this openlab summer student project is to provide intuitive and powerful visualisation tools for key institutional data about CERN, including budgets and contracts. The project will be done in collaboration with the Open Knowledge Foundation under the framework of CERN's open data policy regarding scientific results from LHC. The student will use the model-view-controller web development framework with Flask/HTML5/jQuery/TwitterBootstrap technologies for the user interface and SQLAlchemy ORM for database persistence. Abstract: CERN’s Open Access Policy says that “all results of its experimental and theoretical work shall be published or otherwise made generally available”. Following that, CERN has reached a collaboration agreement with the Open Knowledge Foundation in order for CERN to publish and visualize institutional data. As part of this collaboration, we will develop a module for showing this data in a graphical way in the CERN side and a tool in the Open Knowledge Foundation site for automatizing the input of data

Geoweb 2.0 for Participatory Urban Design: Affordances and Critical Success Factors

In this paper, we discuss the affordances of open-source Geoweb 2.0 platforms to support the participatory design of urban projects in real-world practices.We first introduce the two open-source platforms used in our study for testing purposes. Then, based on evidence from five different field studies we identify five affordances of these platforms: conversations on alternative urban projects, citizen consultation, design empowerment, design studio learning and design research. We elaborate on these in detail and identify a key set of success factors for the facilitation of better practices in the future

Advancing Strategy: How to Lead Change in Corporate Societal Engagement

Implementing a strategy may be even harder than developing it. This learning brief is intended for corporate foundation and CSR leaders who have completed an initial strategy refresh process and who seek effecitve practices and tools to advance this strategy. In our experience advising more than 100 multinational companie, effective leaders facilitate structured, data-informed decisions and enable important organizational improvements to achieve their strategic objectives. Specifically, advancing strategy in corporate societal engagement typically requires leading change in two major areas of the overall portfolio: designing a signative initiative and transforming local giving

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment