Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

https://tandfonline.com/doi/pdf/10.1080/17538947.2016.1239771

Big Data and cloud computing: innovation opportunities and challenges

Extracting and understanding urban areas of interest using geotagged photos

The recent explosive publications of big data studies have well documented the rise of big data and its ongoing prevalence. Different types of “big data” have emerged and have greatly enriched spatial information sciences and related fields in terms of breadth and granularity. Studies that were difficult to conduct in the past time due to data availability can now be carried out. However, big data brings lots of “big errors” in data quality and data usage, which cannot be used as a substitute for sound research design and solid theories. We indicated and summarized the problems faced by current big data studies with regard to data collection, processing and analysis: inauthentic data collection, information incompleteness and noise of big data, unrepresentativeness, consistency and reliability, and ethical issues. Cases of empirical studies are provided as evidences for each problem. We propose that big data research should closely follow good scientific practice to provide reliable and scientific “stories”, as well as explore and develop techniques and methods to mitigate or rectify those ‘big-errors’ brought by big data.

Rethinking big data: A review on the data quality and usage issues

Big Data has become a very popular term. It refers to the enormous amount of structured, semi-structured and unstructured data that are exponentially generated by high-performance applications in many domains: biochemistry, genetics, molecular biology, physics, astronomy, business, to mention a few. Since the literature of Big Data has increased significantly in recent years, it becomes necessary to develop an overview of the state-of-the-art in Big Data. This paper aims to provide a comprehensive review of Big Data literature of the last 4 years, to identify the main challenges, areas of application, tools and emergent trends of Big Data. To meet this objective, we have analyzed and classified 457 papers concerning Big Data. This review gives relevant information to practitioners and researchers about the main trends in research and application of Big Data in different technical domains, as well as a reference overview of Big Data tools.

A general perspective of Big Data: applications, tools, challenges and trends

https://espace.library.uq.edu.au/view/UQ:426115/UQ426115_OA.pdf

An evaluation of crowdsourced information for assessing the visitation and perceived importance of protected areas

Traditional gazetteers are built and maintained by authoritative mapping agencies. In the age of Big Data, it is possible to construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI) from the Web. In this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem to harvest crowd-sourced gazetteer entries. Using experiments based on geotagged datasets in Flickr, we find that the MapReduce-based workflow running on the spatially enabled Hadoop cluster can reduce the processing time compared with traditional desktop-based operations by an order of magnitude. We demonstrate how to use such a novel spatial-computing infrastructure to facilitate gazetteer research. In addition, we introduce a provenance-based trust model for quality assurance. This work offers new insights on enriching future gazetteers with the use of Hadoop clusters, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analytics.

Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop

Constructing gazetteers from volunteered Big Geo-Data based on Hadoop

1 Digital gazetteers play an important role in geographic information retrieval, in digital library services and in systems for spatio-temporal knowledge organization. Traditional gazetteers are built and maintained by authoritative mapping agencies. With the emergence of Web 2.0, new forms of crowd-sourced gazetteers contributed by the general public have become possible. In the age of Big Data, it is possible to enrich and construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI). However, the mining, harvesting, and geoprocessing of voluminous VGI are very computationally intensive. To address this issue, in this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem. Using experiments based on geotagged datasets in Flickr, we demonstrate the high performance of such a novel infrastructure in rapidly extracting different feature types of gazetteer entries at multiple scales. Although post-processing is still necessary for a better quality of output, this work offers new insights on enriching future gazetteers, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analysis. In addition, the conventional gazetteer model (place name, feature type, and footprint) is not sufficient to represent the characteristics of crowdsourcing. Therefore, we propose a quadruple crowd-sourced gazetteer model that extends the classic triple with the fourth element: provenance.

Yue Zhang

Papers

Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop

Constructing gazetteers from volunteered Big Geo-Data based on Hadoop

Constructing Gazetteers from Volunteered Geographic Information Based on Hadoop.