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Published in Jens Jacobsen, Tilman Schlenker, Lisa Edwards, Implementing a Digital Asset Management System, 2012
Jens Jacobsen, Tilman Schlenker, Lisa Edwards
If you search for information in text documents or databases, full text search is an option. It requires a lot of expertise to compile search phrases that find the needed item and don’t bring up too many irrelevant results. The greater the amount of texts, the more difficult searching becomes with full text search.
Cloud-based storage and computing for remote sensing big data: a technical review
Published in International Journal of Digital Earth, 2022
Chen Xu, Xiaoping Du, Xiangtao Fan, Gregory Giuliani, Zhongyang Hu, Wei Wang, Jie Liu, Teng Wang, Zhenzhen Yan, Junjie Zhu, Tianyang Jiang, Huadong Guo
NoSQL can store unstructured data such as heterogeneous remote sensing metadata (Guo and Onstein 2020). For RSBD data storage systems, data and metadata are often rarely modified after they are input into the database. Therefore, compared to RDBMS, NoSQL’s lack of support for ACID is acceptable for RSBD management systems. Search engine is a type of NoSQL that supports full-text search such as Solr and Elastic Search. Search engine NoSQL builds inverted indexes in memory to achieve high performance and a robust full-text index. Fan et al. (2017) stored the metadata of remote sensing in SolrCloud and implemented a full-text search. Their process supports advanced functions such as fuzzy queries and has good adaptability for the complex structures of remote sensing metadata. However, it is costly to implement such storage systems using search engine NoSQL. Wide-column and document NoSQL are also used for metadata storage. For example, Earth Engine adopted the Big Table storage system (Gorelick et al. 2017). Wang et al. (2019) and Cheng et al. (2020) used MongoDB to store both raster data and metadata to achieve integrated data/metadata storage.
Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge
Published in International Journal of Digital Earth, 2020
Thomas Esch, Hubert Asamer, Felix Bachofer, Jakub Balhar, Martin Boettcher, Enguerran Boissier, Pablo d' Angelo, Caroline M. Gevaert, Andreas Hirner, Katerina Jupova, Franz Kurz, Andy Yaw Kwarteng, Emmanuel Mathot, Mattia Marconcini, Alessandro Marin, Annekatrin Metz-Marconcini, Fabrizio Pacini, Marc Paganini, Hans Permana, Tomas Soukup, Soner Uereyen, Christopher Small, Vaclav Svaton, Julian Nils Zeidler
U-TEP uses an EO catalogue to quickly access metadata related to the major EO satellite missions such as the European Sentinel fleet or the US Landsat program. Metadata are continuously harvested and ingested into the catalogue in order to follow-up the ongoing missions and provide up-to-date data to the users. The catalogue is powered by a state-of-the-art computer science technology based on Apache Lucene (Lucene 2018) and providing a distributed, multitenant-capable full-text search engine. The catalogue, associated to the data storage, is also used to publish the user’s data products – e.g. in form of a direct upload by the user or an automatic retrieval from a remotely executed job. An important aspect of the catalogue is the authorization scheme applied to users and catalogue elements in order to define the access to and visibility of datasets, products and applications. Here the user/provider can chose between the three different indexes private (only visible to the individual user), shared (only visible to a defined community of users) and public (visible to all users and communities).
Intelligent evaluation of test suites for developing efficient and reliable software
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Masoud Mohammadian, Zafer Javed
Lucene is a search engine library developed by Apache [27] in Java language. It performs full-text search, and has been widely used in other applications, tools and web-sites as an underlying search engine [28]. Its source code consists of 331 files and 66,702 lines of code. The source code of the Searcher component has 112 files containing 18,388 lines of code.