The Spatial Nearest Neighbor Skyline Queries

📝 Abstract

User preference queries are very important in spatial databases. With the help of these queries, one can found best location among points saved in database. In many situation users evaluate quality of a location with its distance from its nearest neighbor among a special set of points. There has been less attention about evaluating a location with its distance to nearest neighbors in spatial user preference queries. This problem has application in many domains such as service recommendation systems and investment planning. Related works in this field are based on top-k queries. The problem with top-k queries is that user must set weights for attributes and a function for aggregating them. This is hard for him in most cases. In this paper a new type of user preference queries called spatial nearest neighbor skyline queries will be introduced in which user has some sets of points as query parameters. For each point in database attributes are its distances to the nearest neighbors from each set of query points. By separating this query as a subset of dynamic skyline queries N2S2 algorithm is provided for computing it. This algorithm has good performance compared with the general branch and bound algorithm for skyline queries.

💡 Analysis

User preference queries are very important in spatial databases. With the help of these queries, one can found best location among points saved in database. In many situation users evaluate quality of a location with its distance from its nearest neighbor among a special set of points. There has been less attention about evaluating a location with its distance to nearest neighbors in spatial user preference queries. This problem has application in many domains such as service recommendation systems and investment planning. Related works in this field are based on top-k queries. The problem with top-k queries is that user must set weights for attributes and a function for aggregating them. This is hard for him in most cases. In this paper a new type of user preference queries called spatial nearest neighbor skyline queries will be introduced in which user has some sets of points as query parameters. For each point in database attributes are its distances to the nearest neighbors from each set of query points. By separating this query as a subset of dynamic skyline queries N2S2 algorithm is provided for computing it. This algorithm has good performance compared with the general branch and bound algorithm for skyline queries.

📄 Content

International Journal of Database Management Systems ( IJDMS ) Vol.3, No.4, November 2011 DOI: 10.5121/ijdms.2011.3406 65

THE SPATIAL NEAREST NEIGHBOR SKYLINE QUERIES Nasrin Mazaheri Soudani1 and Ahmad Baraani Dastgerdi2 1Department of Computer Engineering, Isfahan University, Isfahan, Iran nasrinmazaheri@eng.ui.ir 2Department of Computer Engineering, Isfahan University, Isfahan, Iran ahmadb@eng.ui.ir

ABSTRACT User preference queries are very important in spatial databases. With the help of these queries, one can found best location among points saved in database. In many situation users evaluate quality of a location with its distance from its nearest neighbor among a special set of points. There has been less attention about evaluating a location with its distance to nearest neighbors in spatial user preference queries. This problem has application in many domains such as service recommendation systems and investment planning. Related works in this field are based on top-k queries. The problem with top-k queries is that user must set weights for attributes and a function for aggregating them. This is hard for him in most cases. In this paper a new type of user preference queries called spatial nearest neighbor skyline queries will be introduced in which user has some sets of points as query parameters. For each point in database attributes are its distances to the nearest neighbors from each set of query points. By separating this query as a subset of dynamic skyline queries N2S2 algorithm is provided for computing it. This algorithm has good performance compared with the general branch and bound algorithm for skyline queries. KEYWORDS User preference queries, nearest neighbor, skyline queries, spatial databases

1. INTRODUCTION

In many situations for decision making, users need select one or more data from database in accordance with their interest. The selected data must meet their desired constraints. For example suppose in a database about a shoreline city, information of its hotels such as cost and distance of each hotel from beach has been saved. A user wants to select a hotel with less cost and distance to beach. User hasn’t accurate asked (for example cost of hotel below 100$ and distance to beach less than 1Km is accurate asked) but wants to find a set of data that are closer to their own interests. Such constraints called soft constraints and queries about these problems called user preference queries [1].

There are two basic queries for these problems. In the first type of queries that are known to top- k, each of data attribute based on their importance to user gives a weight. The score of the data is computed by multiplying its values with the corresponding weights and aggregating them by a function. This query retrieves the k data with the highest scores [2]. In the second type of queries that are known to skyline, the set of all data that no other data dominate them are retrieved. A data dominate another if and only if for all attributes is better than or equals and for at least one International Journal of Database Management Systems ( IJDMS ) Vol.3, No.4, November 2011 66 attribute is better than the other. Indeed, in this way all data that aren’t worse than any other data in database are retrieved [3].
User preference queries are very important in spatial databases. Spatial data in addition to non- spatial data can be stored in these databases. With the help of these queries, user can find best places in database according to their interest.

For many application users evaluate quality of a location with its distance from its nearest neighbor among special set of points. For example suppose a user wants to find a hotel for rest that is near to a restaurant and a coffee shop. So he considers coffee shops and restaurants as two query point sets and evaluates hotels with their distances to nearest coffee shop and restaurant. Less attention has been about subject distance of a point to its nearest neighbors as preference of the user.

Related works in this field are in based on top-k queries. In top-k queries setting a weight for each attribute and a scoring function for aggregating attributes are hard for user. Indeed user is more willing to ask for the skyline first in order to get the “big picture” and then apply a top-k query to get more specific results. So the use of skyline query with considering the distances of points to their nearest neighbors is subject that less attention has been on it [3].

Distance of the point to its nearest neighbor is a dynamic attribute for that point. Dynamic attributes are not directly stored in database but according to data from database can be calculated. Existence algorithms for respond skyline queries such as branch and bound algorithm don’t have good performance f

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