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Geometry and geospatial data on the web
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
Anna Wagner, Mathias Bonduel, Jeroen Werbrouck, Kris McGlinn
GeoJSON is an encoding based on the JavaScript Object Notation (JSON), developed to support web developers, with the intention of providing a (relatively) simple method to define a geospatial geometry. The basis of an object and the data is given by key-value pairs, and other objects can also form the value for a key, thus enabling nested structures. GeoJSON focuses on a restricted set of features, similar to the OGC simple 2D features, e.g. Point, LineString and Polygon. It also only supports one CRS, WGS 84. GeoJSON does not set out to represent semantics though, and if these are required, it is possible through the use of JSON-LD17.
Image Processing and Acquisition using Python
Published in Ravishankar Chityala, Sridevi Pudipeddi, Image Processing and Acquisition using Python, 2020
Ravishankar Chityala, Sridevi Pudipeddi
Dictionaries store key-value pairs. A dictionary is created by enclosing a key-value pair inside {}. >>> a = { 'lang':'python' 'ver': '3.6.6' }
H-Map-Based Technique for Mining High Average Utility Itemset
Published in IETE Journal of Research, 2022
M. S. Bhuvaneswari, N. Balaganesh, K. Muneeswaran
The time complexity of the proposed algorithm is analysed in terms of the basic operation carried out in the High Average Utility Itemset Mining. The key-value pair that represents the RUB and stores the MUT, SMUT, RIU, MIU, and UBRIU values has a substantial advantage over the list and tree structure that was previously employed. The key-value pairs are stored in the hash table. For item lookup and insertion, key-value pairs are more efficient than a list and tree structure. Because insertion and lookup are the most common operations/basic operation in High Average Utility Itemset Mining, the hash map requires only O(1) time compared to the list, which takes O(n), and the tree data structure, which takes O(log(n)). As the time taken for look-up is reduced, the overall execution time of the proposed approach is dependent only on the number of transactions and the number of itemsets explored. The worst-case time complexity of the proposed methodology is O(|RUB|*φ*log(n)), where |RUB| is the number of transactions in RUB, φ is the average transaction length and log(n) is the time for processing promising itemsets.