On answering why-not questions in reverse skyline queries

Islam, Md. Saiful; Zhou, Rui; Liu, Chengfei

doi:10.1109/icde.2013.6544890

Cited by 58 publications

(51 citation statements)

References 17 publications

(45 reference statements)

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“…Due to the importance of its use in various fields of application, research in the reverse skyline has gained many attention in the database research community such as in [3], [10]- [15]. All of the proposed reverse skyline variations above only consider about reverse skyline for zero dimensional data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…12 shows an illustration of window query's minimum and maximum value in one dimension. Assume min and max distance for g, q, and g are (10,15), (20,25), and (30,35). For (15)(16)(17)(18)(19)(20), so that min(w k (g)) and max(w k (g)) are 5 (10 + (-5)) and 20 (same value as min(d k (q))).…”

Section: ) Window Querymentioning

confidence: 99%

“…For example, for 2 facility type, or 2-dimensional, RECT has (f 1min, f 2min) as bottom-left coordinate and (f 1max, f 2max) as top-right coordinate. Our query object RECT 3,2 has bottom-left coordinate (15,10) and top-right coordinate (29,25). Using RECT object, we build R-tree of Minmax table.…”

Section: B Reverse Area Skyline (Rasky) Algorithmmentioning

confidence: 99%

“…Fig. 7 is a partial Minmax table that records min and max distance to each facility type for grid (0, 9), (1,15), and (0, 19) in Fig. 2 Table After completing Minmax table, in the second step GASky finds area skyline using area skyline dominance rule.…”

Section: Area Skyline Querymentioning

confidence: 99%

“…In the example, h 2 = (35, 25) is transformed to h 2 = (35, 55), h 4 = (10, 20) is transformed to h 4 = (20, 60), and so forth. We, then, compute skyline query for the transformed data objects as dynamic skyline query for (15,40), which retrieves {h 4 , h 6 , q, h 7 }. Since q is in the dynamic skyline h 5 , h 5 is a reverse skyline of q.…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

Reverse Area Skyline in a Map

Annisa¹,

Zaman²,

Morimoto³

2017

ijacsa

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Abstract-Skyline query retrieves a set of data objects, each of which is not dominated by another object. On the other hand, given a query object q, "reverse" skyline query retrieves a set of points that are "dynamic" skyline of q. If q is a given preference of a user, "dynamic" skyline query retrieves a set of points that are not dominated by another point with respect to q. Intuitively,"reverse" skyline query of q retrieves a set of points that are as preferable as q. Area skyline query is a method for selecting good areas, each of which is near to desirable facilities such as stations, warehouses, promising customers' house, etc. and is far from undesirable facilities such as competitors' shops, noise sources, etc. In this paper, we applied reverse skyline concept to area skyline query and proposed Reverse Area Skyline algorithm. Analogically, given an area g, reverse area skyline query selects areas, each of which are as preferable as g. Assume that a real estate company wants to sell an area. Reverse area skyline query must be useful for such company to consider effective real estate developments so that the area attracts many buyers. Reverse area skyline query can also be used for selecting promising buyers of the area.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) Window Querymentioning

confidence: 99%

Section: B Reverse Area Skyline (Rasky) Algorithmmentioning

confidence: 99%

Section: Area Skyline Querymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Reverse Area Skyline in a Map

Annisa¹,

Zaman²,

Morimoto³

2017

ijacsa

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Retrieving Text-Based Surrounding Objects in Spatial Databases

Shen

Islam

Taniar

et al. 2019

Advanced Information Networking and Applications

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Retrieval of textually relevant non-dominated surrounding data objects has many potential applications in spatial databases such as textually relevant nearby point-of-interest retrieval surrounding a user. This paper presents a novel query, called textually-relevant direction-based spatial skyline (TDSS), for retrieving textually relevant non-dominated surrounding data objects in spatial databases. The paper also presents efficient algorithms for processing TDSS queries in spatial databases by designing novel data pruning techniques using keyword inverted index and R-Tree data indexing scheme. The effectiveness and efficiency of the proposed algorithms are demonstrated by conducting extensive experiments.

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Why-Not Questions & Explanations for Collaborative Filtering

Stratigi

Tzompanaki

Stefanidis

2020

Web Information Systems Engineering – WISE 2020

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Throughout our digital lives, we are getting recommendations for about almost everything we do, buy or consume. However, it is often the case that recommenders cannot locate the best data items to suggest. To deal with this shortcoming, they provide explanations for the reasons specific items are suggested. In this work, we focus on explanations for items that do not appear in the recommendations they way we expect them to, expressed in why-not questions, to aid the system engineer improve the recommender. That is, instead of offering explanations on every item proposed by the system, we allow the developer give feedback about items that were not proposed. We consider here the most traditional category of recommenders, i.e., the collaborative filtering one, and propose ways for providing explanations for why-not questions. We provide a detailed taxonomy of why-not questions on recommenders, and model-specific explanations based on the inherent parameters of the recommender. Finally, we propose an algorithm for producing explanations for the proposed why-not questions.

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On answering why-not questions in reverse skyline queries

Cited by 58 publications

References 17 publications

Reverse Area Skyline in a Map

Reverse Area Skyline in a Map

Retrieving Text-Based Surrounding Objects in Spatial Databases

Why-Not Questions & Explanations for Collaborative Filtering

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