Analysing the Impact of Large Data Imports in OpenStreetMap

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For this study, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given.

Human Networks and Data Science (NSF 19-608): Uncovering the "dark matter" of research

With funding from the National Science Foundation, the Center for Open Science (COS) and Indiana University will create a dynamic, distributed, and heterogeneous data source for the advancement of science of science research. This will be achieved by using, enhancing, and combining the capabilities of the Open Science Framework (OSF) and the Collaborative Archive & Data Research Environment (CADRE). With over 200,000 users (currently growing by >220 per day), many thousands of projects, registrations, and papers, millions of files stored and managed, and rich metadata tracking researcher actions, the OSF is already a very rich dataset for investigating the research lifecycle, researcher behaviors, and how those behaviors evolve in the social network. As a cross-university effort, CADRE provides an integrated data mining and collaborative environment for big bibliographic data sets. While still under development, the CADRE platform has already attracted long-term financial commitments from 10 research intensive universities with additional support from multiple infrastructure and industry partners. Connecting these efforts will catalyze transformative research of human networks in the science of science.

University Heterogeneous Data Source Integration Middleware Design Based on XML

According to the distribution, autonomy and heterogeneity of the university database, we designed the structure, main arithmetic, query distribution device, result processor and wrapper of the university heterogeneous data integration middle ware by using Java, XML and middle ware. We emphasized on introducing the designation of query distribution device, result processor and wrapper.

Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.