Citation: Poudel, M.; Sarode, R.P.;
Watanobe, Y.; Mozgovoy, M.; Bhalla,
S. A Survey of Big Data Archives in
Time-Domain Astronomy. Appl. Sci.
2022, 12, 6202. https://doi.org/
10.3390/app12126202
Academic Editors: Sławomir
Nowaczyk, Rita P. Ribeiro and
Grzegorz Nalepa
Received: 15 May 2022
Accepted: 15 June 2022
Published: 18 June 2022
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Article
A Survey of Big Data Archives in Time-Domain Astronomy
Manoj Poudel * , Rashmi P. Sarode * , Yutaka Watanobe, Maxim Mozgovoy and Subhash Bhalla
Graduate Department of Computer and Information Systems, The University of Aizu,
Aizu-Wakamatsu 965-8580, Japan; yutaka@u-aizu.ac.jp (Y.W.); mozgovoy@u-aizu.ac.jp (M.M.);
bhalla.subhash@gmail.com (S.B.)
* Correspondence: pmanoj0091@gmail.com (M.P.); rashmipsarode@gmail.com (R.P.S.)
Abstract:
The rise of big data has resulted in the proliferation of numerous heterogeneous data stores.
Even though multiple models are used for integrating these data, combining such huge amounts of
data into a single model remains challenging. There is a need in the database management archives to
manage such huge volumes of data without any particular structure which comes from unconnected
and unrelated sources. These data are growing in size and thus demand special attention. The speed
with which these data are growing as well as the varied data types involved and stored in scientific
archives is posing further challenges. Astronomy is also increasingly becoming a science which is
now based on a lot of data processing and involves assorted data. These data are now stored in
domain-specific archives. Many astronomical studies are producing large-scale archives of data and
these archives are then published in the form of data repositories. These mainly consist of images and
text without any structure in addition to data with some structure such as relations with key values.
When the archives are published as remote data repositories, it is challenging work to organize the
data against their increased diversity and to meet the information demands of users. To address
this problem, polystore systems present a new model of data integration and have been proposed to
access unrelated data repositories using an uniform single query language. This article highlights the
polystore system for integrating large-scale heterogeneous data in the astronomy domain.
Keywords: big data; data integretion; astronomy; polystore
1. Introduction
Due to the abundance of data sources, the amount of data available for analysis is
increasing rapidly, and there has been a great deal of research into how to manage heteroge-
neous data. Big data refers to large or complex information that cannot be processed using
traditional methods. For a long time, people have been storing and accessing huge amounts
of data for analytics [
1
]. In today’s world, big data, comprising both structured as well as
unstructured data, is available. Databases and spreadsheets that have been used in the past
include structured data that are often numerical. An unstructured data set is a collection
of unrelated pieces of information that does not follow a predetermined structure. It is
common practice to store and process large amounts of data using specialized computer
databases and applications [2].
The Semantic Web, often known as the Web of Data, is based on the concept of
Linked Data [
3
]. The Semantic Web’s emphasis on Linked Data’s best practices for creating
meaningful links between resources will benefit humans and robots. It is a collection of
design concepts for sharing machine-readable and cross-referenced data [
4
]. Linked Data
is a collection of best practices for publishing and interconnecting structured data on the
Internet. Uniform Resource Identifiers (URIs, a common means of identifying concepts
or entities), Hypertext Transfer Protocol (HTTP, a protocol used to retrieve resources or
descriptions of resources, which is fundamental and universal) and Resource Description
Framework (RDF, a data model based on graphs to organize data describing things in the
world by structuring and linking it) are some technologies that support Linked Data [
5
].
Appl. Sci. 2022, 12, 6202. https://doi.org/10.3390/app12126202 https://www.mdpi.com/journal/applsci
