Related papers: Data Science for Geographic Information Systems
The discipline of data science emerged to combine statistical methods with computing. At Aalto University, Finland, we have taken first steps to bring educational data science as a part of daily operations of Management Information…
This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observations, from a…
Digital engineering practices offer significant yet underutilized potential for improving information assurance and system lifecycle management. This paper examines how capabilities like model-based engineering, digital threads, and…
In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In…
Background: Introduced in 2010, the sub-discipline of gerontologic biostatistics (GBS) was conceptualized to address the specific challenges in analyzing data from research studies involving older adults. However, the evolving technological…
This paper tries to throw light in the usage of data structures in the field of information retrieval. Information retrieval is an area of study which is gaining momentum as the need and urge for sharing and exploring information is growing…
Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with Global Positioning…
Non-relational databases are the common means of data storage in the Cloud, and optimizing the data access is of paramount importance into determining the overall Cloud system performance. In this paper, we present GAIA, a novel model for…
Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e. platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent…
Our society has been computerised and globalised due to emergence and spread of information and communication technology (ICT). This enables us to investigate our own socio-economic systems based on large amounts of data on human…
Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate (e.g. archived meteorological variables such as temperature, wind, water, etc.)…
Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data.…
In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved…
Volunteered geographic information (VGI), one of the most important types of user-generated web content, has been emerging as a new phenomenon. VGI is contributed by numerous volunteers and supported by web 2.0 technologies. This chapter…
The rapid growth of solar data is driving changes in the typical workflow and algorithmic approach to solar data analysis. We present recently deployed tools to aid this evolution and layout the path for future development. The majority of…
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the resulting quality heavily dependent on their experience and…
Keeping pace with rapidly evolving technology is a key challenge in teaching statistics. To equip students with essential skills for the modern workplace, educators must integrate relevant technologies into the statistical curriculum where…
Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the…
Public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and…
This paper focuses on the application of Spatial Data mining Techniques to efficiently manage the challenges faced by peripheral rural areas in analyzing and predicting market scenario and better manage their economy. Spatial data mining is…