Framework for location-aware search engine

Tabarcea, Andrei; Gali, Najlah; Fränti, Pasi

doi:10.1080/17489725.2017.1407001

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…PharmFinder) users of the prototype app were able to locate the exact pharmacy to make drug purchase. As an important functional requirement, this was met in the proposed prototype system going by users' validation, which is consistent with the assertion in [20] and [21] that L-aS accuracy criterion must be met. There are other Subjective non-functional Criteria (SnC) that showed the prototype system as a valid tool to help users transcend the experience of serendipity in their quest to locate the exact pharmacy where to find drugs.…”

Section: Results and Analysissupporting

confidence: 84%

A Mobile Based Pharmacy Store Location-aware System

Akhigbe

Ososanya

Olajubu

2022

JIOS

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This paper presents a formulated mobile-based location-awareness model that was implemented into a Location-awareness System (L-aS hereafter) for finding the pharmacy location where prescribed drugs and their prices are available for sale. The scenario that inspired the model formulation was formalized using the unified modeling language. The model was implemented within the android studio integrated development environment with the L-aS database created through SQL lite database. The system was tested using user experience based testing technique. Based on core system performance testing, the system demonstrated a normal response time, resource utilization (i.e. storage and memory usage), and data use potentials of 414.6ms, 4.964mb and 1.9116 kb/secs, and 3.0296mb respectively. Therefore, the system performed well under ordinary conditions as an android application running on small memory devices. The study concluded that the developed mobile based pharmacy store location-aware system was useful to provide information to purchase prescribed drugs especially in perplexed situation(s).

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Section: Results and Analysissupporting

confidence: 84%

A Mobile Based Pharmacy Store Location-aware System

Akhigbe

Ososanya

Olajubu

2022

JIOS

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“…Note that the traversal path is determined by the classification score of the summaries. At iteration 4, the point (1,3) corresponds to a leaf node (i.e. it does not have a summary) so the process is completed and the point is considered as a candidate solution.…”

Section: A Evaluation Of Bkskq Queriesmentioning

confidence: 99%

“…SPOI 1 (Smart POI dataset) is a good example of geo-tagged dataset. It contains more than 27 million points of interest around the world, including cafes, pubs, restaurants, or hotels, which are described using keywords about their…”

Section: Introductionmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Waleed Alsabhan . 1 https://sdi4apps.eu/spoi/ specialties or the amenities they provide. Picture datasets or collections of microposts from social-networks can be also considered geo-tagged datasets.…”

Section: Introductionmentioning

confidence: 99%

“…In other domains, such as the Web, where just a portion of the content is geo-tagged, there are some attempts to automatically detect locations from web resources [1]. Such results would enable the development of general location-aware search engines, in which data structures such as the one proposed in our work, are essential for an efficient information retrieval as an extension of ubiquitous inverted indexes.…”

Section: Introductionmentioning

confidence: 99%

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cBiK: A Space-Efficient Data Structure for Spatial Keyword Queries

et al. 2020

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A vast amount of geo-referenced data is being generated by mobile devices and other sensors increasing the importance of spatio-textual analyses on such data. Due to the large volume of data, the use of indexes to speed up the queries that facilitate such analyses is imperative. Many disk resident indexes have been proposed for different types of spatial keyword queries, but their efficiency is harmed by their high I/O costs. In this work, we propose cBiK, the first spatio-textual index that uses compact data structures to reduce the size of the structure, hence facilitating its usage in main memory. Our experimental evaluation, shows that this approach needs half the space and is more than one order of magnitude faster than a disk resident state-of-the-art index. Also, we show that our approach is competitive even in a scenario where the disk resident data structure is warmed-up to fit in main memory. INDEX TERMS Bitmap, compact data structure, kNN, spatial keyword query, spatio-textual data.

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