Total Survey Error in Practice: Improving Quality in the Era of Big Data.
While mobile phones or cell phones have been a challenge for telephone survey researchers for some time, the Internet or Web capabilities of mobile phones have begun to receive attention in the last few years. There are a number of ways that Internet-enabled smartphones can affect survey data collection, and the implications of these for various sources of errors are only now being fully explored. There are three broad approaches to the opportunities and challenges posed by mobile Web.
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complexity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.
The proceedings of the 11th International Conference on Service-Oriented Computing (ICSOC 2013), held in Berlin, Germany, December 2–5, 2013, contain high-quality research papers that represent the latest results, ideas, and positions in the field of service-oriented computing. Since the first meeting more than ten years ago, ICSOC has grown to become the premier international forum for academics, industry researchers, and practitioners to share, report, and discuss their ground-breaking work. ICSOC 2013 continued along this tradition, in particular focusing on emerging trends at the intersection between service-oriented, cloud computing, and big data.
Full texts of third international conference on data analytics are presented.
The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
In 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the momentIn 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the moment
Companies are increasingly paying close attention to the IP portfolio, which is a key competitive advantage, so patents and patent applications, as well as analysis and identification of future trends, become one of the important and strategic components of a business strategy. We argue that the problems of identifying and predicting trends or entities, as well as the search for technical features, can be solved with the help of easily accessible Big Data technologies, machine learning and predictive analytics, thereby offering an effective plan for development and progress. The purpose of this study is twofold, the first is an identification of technological trends, the second is an identification of application areas and/or that are most promising in terms of technology development and investment. The research was based on methods of clustering, processing of large text files and search queries in patent databases. The suggested approach is considered on the basis of experimental data in the field of moving connected UAVs and passive acoustic ecology control.
The article is dedicated to the analysis of Big Data perspective in jurisprudence. It is proved that Big Data have to be used as the explanatory and predictable tool. The author describes issues concerning Big Data application in legal research. The problems are technical (data access, technical imperfections, data verification) and informative (interpretation of data and correlations). It is concluded that there is the necessity to enhance Big Data investigations taking into account the abovementioned limits.
This book contains the proceedings of the 4th International Conference on Computer Supported Education (CSEDU 2012) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and technically co-sponsored by SPEE (Portuguese Society for Engineering Education), IGIP (International Society for Engineering Education), ROLE (Responsive Open Learning Environments) and IFIP TC3 (International Federation for Information Processing - Technical Committee 3 - ICT and Education).
CSEDU has become an annual meeting place for presenting and discussing learning paradigms, best practices and case studies that concern innovative computer-supported learning strategies, institutional policies on technology-enhanced learning including learning from distance, supported by technology. The Web is currently a preferred medium for distance learning and the learning practice in this context is usually referred to as e-learning or technology-enhanced learning. CSEDU 2012 is expected to give an overview of the state of the art in technology-enhanced learning and to also outline upcoming trends and promote discussions about the education potential of new learning technologies in the academic and corporate world.
This conference brings together researchers and practitioners interested in methodologies and applications related to the education field. It has five main topic areas, covering different aspects of Computer Supported Education, including "Information Technologies Supporting Learning", "Learning/Teaching Methodologies and Assessment", "Social Context and Learning Environments", "Domain Applications and Case Studies" and "Ubiquitous Learning". We believe the proceedings, demonstrate new and innovative solutions, and highlight technical problems in each field that are challenging and worthwhile.
CSEDU 2012 received 243 paper submissions from 58 countries in all continents. A double-blind review process was enforced, with the help of the 297 experts who are members of the conference program committee, all of them internationally recognized in one of the main conference topic areas. Only 29 papers were selected to be published and presented as full papers, i.e. completed work (10 pages in proceedings / 30' oral presentations). 73 papers, describing work-in-progress, were selected as short papers for 20' oral presentation. Furthermore 37 papers were presented as posters. The full-paper acceptance ratio was thus 12%, and the total oral paper acceptance ratio was less than 42%. These ratios denote a high level of quality, which we intend to maintain and reinforce in the next edition of this conference.
The high quality of the CSEDU 2012 programme is enhanced by three keynote lectures, delivered by distinguished guests who are renowned experts in their fields, including (alphabetically): Joseph Trimmer (Ball State University, United States), David Kaufman (Simon Fraser University, Canada) and Hugh Davis (University of Southampton, United Kingdom).
For the fourth edition of the conference we extended and ensured appropriate indexing of the proceedings of CSEDU including DBLP, INSPEC, EI and Thomson Reuters Conference Proceedings Citation Index. Besides the proceedings edited by SciTePress, a short list of papers presented at the conference will be selected for publication of extended and revised versions in the Journal of Education and Information Technologies. Furthermore, all presented papers will soon be available at the SciTePress digital library.
The conference is complemented with two special sessions, focusing on specialized aspects of computer supported education; namely, a Special Session on Enhancing Student Engagement in e-Learning (ESEeL 2012) and a Special Session on Serious Games on Computer Science Learning (SGoCSL 2012).
Building an interesting and successful program for the conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research and development efforts are recorded here. Secondly, we thank the members of the program committee and additional reviewers for their diligence and expert reviewing. We also wish to include here a word of appreciation for the excellent organization provided by the conference secretariat, from INSTICC, who have smoothly and efficiently prepared the most appropriate environment for a productive meeting and scientific networking. Last but not least, we thank the invited speakers for their invaluable contribution and for taking the time to synthesize and deliver their talks.