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High Performance Remote Sensing Data Processing in a Cloud Computing Environment
Published in Lizhe Wang, Jining Yan, Yan Ma, Cloud Computing in Remote Sensing, 2019
Lizhe Wang, Jining Yan, Yan Ma
With the proliferation of data, the amount of metadata and catalogs would be bound to soar up. Not surprisingly, there would be millions of data entries in the metadata table of HBase. Actually, these enormous data entries in key-value fashion are normally serialized into thousands of files. Therefore, the on-line retrieval of metadata at extreme volume would definitely be a disaster. Actually, most of the key-value database are indexed by keyword of the data entry. There are several common indexing data structures, including Hash-based indexing, R-tree indexing, B-tree indexing and so on. The indexing trees are normally used for local indexing inside a key-value storage, while the Hash mechanism is used for locating the data nodes. However, in this scenario, a global indexing tree would not be a wise choice, since the cost of building and maintenance could be unprecedentedly huge.
Web IR: Information Retrieval on the Web
Published in Akshi Kumar, Web Technology, 2018
The role of the indexing process is to build data structures that enable searching. The query process makes use of these data structures to produce a ranked list of documents for a user’s query. Query processing takes the user’s query and, depending on the application, the context, and other inputs, builds a better query and submits the enhanced query to the search engine on the user’s behalf and displays the ranked results. Thus, a query process comprises a user interaction module and a ranking module (Figure 6.3). The user interaction module supports creation and refinement of a query and displays the results. The ranking module uses the query and indexes (generated during the indexing process) to generate a ranked list of documents.
Industry 4.0
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
Indexing is a data structure technique to productively recover records from the database documents dependent on specific properties on which the indexing has been completed. The video indexing system encourages users to have productive access, search, and perusing capacities to the ideal mixed media (sound video) content. The video indexing and retrieval system gives the user the ability to index and recover the video data in an effective way. In this chapter, a machine learning-based approach for indexing videos was discussed. This approach analyzes the various technique of machine learning algorithms.
DAPR-tree: a distributed spatial data indexing scheme with data access patterns to support Digital Earth initiatives
Published in International Journal of Digital Earth, 2020
Jizhe Xia, Sicheng Huang, Shaobiao Zhang, Xiaoming Li, Jianrong Lyu, Wenqun Xiu, Wei Tu
Data indexing is one of the most widely used mechanisms to provide fast data access against a large dataset by constituting a better logical data organization. Typically, a spatial index significantly improves spatial data accessing performance by leveraging spatial relationships (e.g. topology) among data (Bereuter and Weibel 2013). A spatial index fast supports a variety of operators such as range queries, spatial queries, and trajectory queries. Efficient implementation of these basic operators is essential for accessing required data records from a big dataset so that the ‘Big Value' can be fast explored. Therefore, a variety of indexing schemes have been proposed and widely used in various scientific models, database management systems, and National Spatial Data Infrastructure (NSDI) (Gaede and Günther 1998; Guttman 1984; Beckmann et al. 1990; Ji et al. 2014; Mehrotra, Majhi, and Gupta 2010; Feng et al. 2018).
A procedural footprint enhancement of global topographic surface with multiple levels of detail
Published in International Journal of Digital Earth, 2020
Mapping the footprint. Indexing methods can be used as a hash function. Based on the nearest-neighbor (NN) principle, the function assigns an arbitrary point to the unique index cell c that contains it. The mapping of a footprint employs the hashing capability to associate each point p with l cells of the applied indexing method; one for each LOD. It is denoted by function κ and reads: