. Ranking Spatial Data by Quality Preferences
Abstract:
A spatial preference query ranks objects based on the qualities
of features in their spatial neighborhood. For example, using a real estate
agency database of flats for lease, a customer may want to rank the flats with
respect to the appropriateness of their location, defined after aggregating the
qualities of other features (e.g., restaurants, cafes, hospital, market, etc.)
within their spatial neighborhood. Such a neighborhood concept can be specified
by the user via different functions. It can be an explicit circular region
within a given distance from the flat. Another intuitive definition is to
assign higher weights to the features based on their proximity to the flat. In
this paper, we formally define spatial preference queries and propose
appropriate indexing techniques and search algorithms for them. Extensive
evaluation of our methods on both real and synthetic data reveals that an
optimized branch-and-bound solution is efficient and robust with respect to
different parameters.
Existing System:
To
our knowledge, there is no existing efficient solution for processing the top-k spatial preference query.Object ranking
is a popular retrieval task in various applications. In relational databases, we
rank tuples using an aggregate score function on their attribute values. For
example, a real estate agency maintains a database that contains information of
flats available for rent. A potential customer wishes to view the top-10 flats with the largest sizes and lowest
prices. In this case, the score of each flat is expressed by the sum of two
qualities: size and price, after normalization to the domain (e.g., 1 means the
largest size and the lowest price). In spatial databases, ranking is often
associated to nearest neighbor (NN) retrieval. Given a query location, we are
interested in retrieving the set of nearest objects to it that qualify a
condition (e.g., restaurants). Assuming that the set of interesting objects is
indexed by an R-tree , we can apply distance bounds and traverse the index in a
branch-and-bound fashion to obtain the answer.
Proposed System:
We Propose (i)
spatial ranking, which orders the objects according to their distance from a
reference point, and (ii) non-spatial ranking, which orders the objects by an
aggregate function on their non-spatial values. Our top- k spatial preference query integrates
these two types of ranking in an intuitive way. As indicated by our examples,
this new query has a wide range of applications in service recommendation and
decision support systems. To our knowledge, there is no existing efficient
solution for processing the top-k spatial preference query. A brute-force approach (to be
elaborated in Section 3.2) for evaluating it is to compute the scores of all
objects in D and select the
top-k ones. This
method, however, is expected to be very expensive for large input datasets.
Module Description:
- Spatial Ranking
- Non-Spatial ranking
- Neighbor (NN) Retrieval
- Spatial Query Evaluation on R-trees
Spatial Ranking
spatial ranking, which orders the objects according to their
distance from a reference point.
Non-Spatial Ranking:
Non-spatial ranking, which orders the objects by an
aggregate function on their non-spatial values. Our top- k spatial preference query integrates
these two types of ranking in an intuitive way. As indicated by our examples,
this new query has a wide range of applications in service recommendation and
decision support systems. To our knowledge, there is no existing efficient
solution for processing the top-k spatial preference query.
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