OPEN—Enabling Non-expert Users to Extract, Integrate, and Analyze Open Data

Government initiatives for more transparency and participation have lead to an increasing amount of structured data on the web in recent years. Many of these datasets have great potential. For example, a situational analysis and meaningful visualization of the data can assist in pointing out social or economic issues and raising people’s awareness. Unfortunately, the ad-hoc analysis of this so-called Open Data can prove very complex and time-consuming, partly due to a lack of efficient system support.On the one hand, search functionality is required to identify relevant datasets. Common document retrieval techniques used in web search, however, are not optimized for Open Data and do not address the semantic ambiguity inherent in it. On the other hand, semantic integration is necessary to perform analysis tasks across multiple datasets. To do so in an ad-hoc fashion, however, requires more flexibility and easier integration than most data integration systems provide. It is apparent that an optimal management system for Open Data must combine aspects from both classic approaches. In this article, we propose OPEN, a novel concept for the management and situational analysis of Open Data within a single system. In our approach, we extend a classic database management system, adding support for the identification and dynamic integration of public datasets. As most web users lack the experience and training required to formulate structured queries in a DBMS, we add support for non-expert users to our system, for example though keyword queries. Furthermore, we address the challenge of indexing Open Data

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Challenges of Internet of Things and Big Data Integration

The Internet of Things anticipates the conjunction of physical gadgets to the In-ternet and their access to wireless sensor data which makes it expedient to restrain the physical world. Big Data convergence has put multifarious new opportunities ahead of business ventures to get into a new market or enhance their operations in the current market. considering the existing techniques and technologies, it is probably safe to say that the best solution is to use big data tools to provide an analytical solution to the Internet of Things. Based on the current technology deployment and adoption trends, it is envisioned that the Internet of Things is the technology of the future, while to-day's real-world devices can provide real and valuable analytics, and people in the real world use many IoT devices. Despite all the advertisements that companies offer in connection with the Internet of Things, you as a liable consumer, have the right to be suspicious about IoT advertise-ments. The primary question is: What is the promise of the Internet of things con-cerning reality and what are the prospects for the future.Comment: Proceedings of the International Conference on International Conference on Emerging Technologies in Computing 2018 (iCETiC '18), 23rd -24th August, 2018, at London Metropolitan University, London, UK, Published by Springer-Verla

QGIS AND OPEN DATA CUBE APPLICATIONS FOR LOCAL CLIMATE ZONES ANALYSIS LEVERAGING PRISMA HYPERSPECTRAL SATELLITE DATA

Climate change poses a significant threat to humans and biodiversity, impacting various aspects of livelihoods, infrastructure, and ecosystems. Understanding climate change and its interaction with the environment is crucial for achieving Sustainable Development Goals. Local Climate Zones (LCZ) play a key role in comprehending climate change by categorizing urban areas also based on their thermal characteristics. This study presents prototype open-source software tools developed to integrate ground and satellite data for LCZ analysis in the Metropolitan City of Milan (Northern Italy). These tools consist of a QGIS plugin to access and preprocess ground-based meteorological sensor data and a client-server platform, based on the Open Data Cube and Docker technologies, for the exploitation of multispectral and hyperspectral satellite data in LCZ mapping and analysis. The tools’ architecture, data retrieval methods, and analysis capabilities are described in detail. The QGIS plugin facilitates the access and preprocessing of ground-based sensor data within the user-friendly QGIS environment. The platform enables seamless ground-sensor and satellite data management and analysis, using Jupyter Notebooks as an interface to support programmatic operations on the data. The proposed tools provide a framework for studying climate change and its local impacts on urban environments, with the potential of empowering users to effectively analyze and mitigate its effects

Object-oriented Tools for Distributed Computing

Distributed computing systems are proliferating, owing to the availability of powerful, affordable microcomputers and inexpensive communication networks. A critical problem in developing such systems is getting application programs to interact with one another across a computer network. Remote interprogram connectivity is particularly challenging across heterogeneous environments, where applications run on different kinds of computers and operating systems. NetWorks! (trademark) is an innovative software product that provides an object-oriented messaging solution to these problems. This paper describes the design and functionality of NetWorks! and illustrates how it is being used to build complex distributed applications for NASA and in the commercial sector