Corporate social responsibility: issues for future development in the low-fares airlines sector

Research reportFor reasons beyond our control, access to this report will resume on 1 December 2012 - please direct any queries to [email protected] practitioner-focused review of the research that emphasises the issues and opportunities managers might face should they decide to develop their CSR further. Main findings from the research are that sustainable development, corporate social responsibility (CSR), and aviation policies have only limited linkages, especially on the UK regional level. Within the sector, low-fares airlines do have socially-responsible initiatives, mostly centred on environmentally-sensitive technological investment and charitable/community support. However, low-fares airlines do not communicate their activities as widely as other sectors and full-service carriers.Research funded by the Economic and Social Research Council (ESRC) as part of its Business Engagement Scheme under the title Social Responsibility among Low-fares Airlines: Current Practices and FutureTrends (RES 185-3-0046)

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Corporate Social Responsibility

This Edited Volume Corporate Social Responsibility is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in corporate behavior. The book comprises single chapters authored by various researchers and edited by an expert in the field. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors and opening new possible research paths for further novel developments

Smart Water: Short-Term Forecasting Application in Water Utilities

The unyielding interconnection between water and energy has made demand forecasting a necessity for water utilities. Electricity prices driven by the time of use has impelled water utilities towards short-term water demand forecasting. The progressive new Smart Water Grid platform has helped water utilities in utilizing their Water Distribution Networks. This two-way platform has provided developers and decision makers with robust models that rely on consumer feedback. Among these models is the water demand forecasting models. Multitudinous demand forecasting methods have been developed but none have utilized model implementation practicality. Utilities differ in size, capacity, and interest. While small size utilities focus on model simplicity, larger utilities prioritize model accuracy. This work focuses on a water utility located in Essex County, Ontario, Canada. This study presents three papers that focus on investigation and evaluation of short-term water demand forecasting techniques. The first paper compares water usage between two crops (tomatoes and bell peppers) in an effort to evaluate a crop to crop forecast technique that relies on one crops watering data in order to produce forecasts for another crop, The second paper examines the effect of model type, input type, and data size on model performance and computational load. The third paper proposes a new methodology where model performance is not sacrificed for model simplification

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Data mining Twitter for cancer, diabetes, and asthma insights

Twitter may be a data resource to support healthcare research. Literature is still limited related to the potential of Twitter data as it relates to healthcare. The purpose of this study was to contrast the processes by which a large collection of unstructured disease-related tweets could be converted into structured data to be further analyzed. This was done with the objective of gaining insights into the content and behavioral patterns associated with disease-specific communications on Twitter. Twelve months of Twitter data related to cancer, diabetes, and asthma were collected to form a baseline dataset containing over 34 million tweets. As Twitter data in its raw form would have been difficult to manage, three separate data reduction methods were contrasted to identify a method to generate analysis files, maximizing classification precision and data retention. Each of the disease files were then run through a CHAID (chi-square automatic interaction detector) analysis to demonstrate how user behavior insights vary by disease. Chi-square Automatic Interaction Detector (CHAID) was a technique created by Gordon V. Kass in 1980. CHAID is a tool used to discover the relationship between variables. This study followed the standard CRISP-DM data mining approach and demonstrates how the practice of mining Twitter data fits into this six-stage iterative framework. The study produced insights that provide a new lens into the potential Twitter data has as a valuable healthcare data source as well as the nuances involved in working with the data

public expenditure review (PER)

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High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications