scholarly journals A KSNA-Tree Algorithm for the Top-k Exact Keyword Search in Spatial Databases

2017 ◽  
Keyword(s):  
Spatial Databases ◽  
Keyword Search ◽  
Tree Algorithm

Keyword Search Mechanisms in Geo-Spatial Databases

2018 ◽  
pp. 176-194
Author(s):  
Priya M. ◽  
Kalpana R.
Keyword(s):  
Spatial Databases ◽  
Keyword Search ◽  
Database Systems ◽  
Index Structure ◽  
Hybrid Index ◽  
Spatial Objects ◽  
Ranking Model ◽  
Text Information ◽  
Spatial Indexes ◽  
The Given

Most web and mobile applications are based on searching the location-based objects called spatial objects. In spatial database systems, searching such objects is a challenging task since it deals with geo-spatial capabilities. Sometimes, the spatial queries are associated with text information in order to obtain the most relevant answers nearest to the given location. Such queries are called spatial textual query. Conventional spatial indexes and text indexes are not suitable for resolving such queries. Since these indexes use various approaches to perform searching, they can cause performance degradation. Effective processing of the query mainly depends on the index structure, searching algorithms, and location-based ranking. This chapter reviews the different hybrid index structures and search mechanisms to extract the spatial objects, the different ranking model it supports, and the performance characteristics.

scholarly journals Efficient NN Spatial Keyword Search Using Spatial Inverted (SI) Index

2018 ◽  
Vol 7 (2.19) ◽  
pp. 17
Author(s):  
B.A. Vishnupriya ◽  
N. Senthamarai ◽  
S. Bharathi
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Keyword Search ◽  
Spatial Information ◽  
Spatial Database ◽  
Spatial Relations ◽  
Base Station ◽  
Inverted File ◽  
File Structure ◽  
N Gram

"Spatial information mining", or learning revelation in spatial database, alludes to the illustration out of concealed information, spatial relations, or different examples that are not unequivocally put away in spatial databases. To get to the spatial database alongside the catchphrase another kind of inquiry called spatial watchword question is utilized. A spatial watchword inquiry get client area and client given catchphrases as contentions and gives web protests that are spatially and literarily material to these information. The current answers for such inquiries depend on IR2-tree that has a couple of inadequacies as space utilization and event of false hit is extremely huge when the question of the last outcome is far from the inquiry point .To beat this issue a novel file structure called Spatial Inverted file is proposed. Presently a-days use of portable is expanding enormously .In the versatile system an intermediary is set between base station and Location Based Server (LBS).This intermediary utilizes the Spatial modified file procedure to answer the SK inquiry by utilizing spatial data from the base station and printed data from the client question. The outcome from the SI record is given to two file structure in the intermediary called EVR Tree and Grid list. The Estimated Valid Region (EVR) for the present area of the client and required spatial articles are produced and come back to the client. On the off chance that the EVR is absent in the two file structure of intermediary it offer question to LBS. In the event that the client given inquiry is miss written or miss spelled it can be oversee by SI record utilizing n gram/2L Approximation file.

Keyword Search in Spatial Databases: Towards Searching by Document

2009 ◽  
Author(s):  
Dongxiang Zhang ◽  
Yeow Meng Chee ◽  
Anirban Mondal ◽  
Anthony K. H. Tung ◽  
Masaru Kitsuregawa
Keyword(s):  
Spatial Databases ◽  
Keyword Search

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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