A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

Jiang, Yongyao; Li, Yun; Yang, Chaowei; Hu, Fei; Armstrong, E. M.; Huang, Thomas S.; Moroni, David; McGibbney, L. J.; Greguska, Frank; Finch, Christopher J.

doi:10.3390/ijgi7020062

Cited by 12 publications

(16 citation statements)

References 25 publications

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“…The Physical Oceanography Distributed Active Archive Center (PO. DAAC) serves the oceanographic community with 514 collection level datasets and massive granule level data atop Solr (Jiang et al 2018a). Elasticsearch is the fundamental component of NOAA's OneStop portal in which data providers manage data and metadata with increased discoverability and accessibility.…”

Section: Data Query Layermentioning

confidence: 99%

“…For example, an algorithm combing Latent Semantic Analysis (LSA) and a two-tier ranking was reported to build a semantic-enabled data search engine (Li et al 2014a, b). Jiang et al (2018a) developed a smart web-based data discovery engine that mines and utilizes data relevancy from metadata and user behavior data. The engine enables machine-learned ranking based on several features that can reflect users' search preferences.…”

Section: Data Query Layermentioning

confidence: 99%

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Big Data and Cloud Computing

et al. 2019

Manual of Digital Earth

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Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing emerged to provide crucial computing support to address these challenges. This chapter introduces Digital Earth data sources, analytical methods, and architecture for data analysis and describes how cloud computing supports big data processing in the context of Digital Earth. Keywords Geoscience • Spatial data infrastructure • Digital transformation • Big data architecture 9.1 Introduction Digital Earth refers to the virtual representation of the Earth we live in. It represents the Earth in the digital world from data to model. Data are collected and models are abstracted to build the digital reality. Massive amounts of data are generated from various sensors deployed to observe our home planet while building Digital Earth. The term "big data" was first presented by NASA researchers to describe the massive amount of information that exceeds the capacities of main memory, local disk, and even remote disk (Friedman 2012). According to the National Institute of Standards and Technology (NIST), "Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world" (Chang and Grady 2015). This definition refers to the bounty of digital data from various data sources in the context of Digital Earth, which focus on big data's geographical aspects of social information, Earth observation (EO), sensor observation service (SOS), cyber infrastructure (CI), social media and business information (Guo 2017; Guo et al. 2017; Yang et al. 2017a, b). Digital Earth data are collected from satellites,

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Section: Data Query Layermentioning

confidence: 99%

Section: Data Query Layermentioning

confidence: 99%

Big Data and Cloud Computing

et al. 2019

Manual of Digital Earth

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“…Accordingly, query-user, query-data, and terminology-data co-occurrence matrices are constructed from user logs and metadata for similarity calculation. The Latent Semantic Analysis (LSA) [41] is applied to these co-occurrence matrixes to discover hidden semantic relations among vocabularies that consist of queries and terminologies. The independent vocabulary similarity scores derived from the three matrixes validate each other and become more credible if integrated while using borda voting to a final score ranging from 0 (i.e., no relation) to 1 (i.e., identical).…”

Section: Query Expansionmentioning

confidence: 99%

A Query Understanding Framework for Earth Data Discovery

Jiang

Goldstein

et al. 2020

Applied Sciences

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One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent.

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“…Ocean databases store marine and environmental data from different disciplines and sources. They are valuable data management solutions, inasmuch as accessing, combining, verifying and analyzing these multi‐disciplinary datasets present a significant challenge for scientists and managers (Jian et al ). The first ocean database, Crubase, contains cruise data of up to 320 measured variables from 3000 to 4000 oceanographic stations (Vladimirov ; Vladimirov and Miroshnichenko ).…”

Section: Ocean Databases: An Overviewmentioning

confidence: 99%

Design, construction, and application of a regional ocean database: A case study in Jiaozhou Bay, China

Yuan

Jalón‐Rojas

Wang

et al. 2019

Limnology & Ocean Methods

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Access to geophysical data poses a challenge in Oceanography and Marine Sciences. Ocean databases are an efficient solution to storing, standardizing, validating, and sharing marine and environmental data. However, ocean databases of regional seas and coastal systems are still rare. In this article, we describe a regional ocean database for Jiaozhou Bay, a coastal system affected by increasing anthropogenic activities, such as land reclamation, a cross‐bay bridge construction, and land‐based pollutant discharges. Understanding the impact of human activities on Jiaozhou Bay requires a comprehensive range of research on biogeochemical and physical changes, and on the current environmental state of the bay. The Jiaozhou Bay database described here aims to inform this key research and also to serve as a reference for the future development of regional ocean databases. It includes three kinds of variables: on‐site, satellite, and modeling data. The database structure is unified and incorporates file names, header information (including metadata), measured values and data‐quality flags, which facilitate data exchange and use. We propose a system of data‐quality flags, based on 13 checks that encompass quality control of the different kinds of variables (e.g., satellite 2D data and on‐site profile data). Three examples, one for each type of data, illustrate the use and value of the Jiaozhou Bay database in better understanding the oceanographic characteristics of this bay and the impact of intensive anthropogenic activities on its marine environment.

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A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

Cited by 12 publications

References 25 publications

Big Data and Cloud Computing

Big Data and Cloud Computing

A Query Understanding Framework for Earth Data Discovery

Design, construction, and application of a regional ocean database: A case study in Jiaozhou Bay, China

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