225,128 research outputs found
Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs
Many problems in areas as diverse as recommendation systems, social network
analysis, semantic search, and distributed root cause analysis can be modeled
as pattern search on labeled graphs (also called "heterogeneous information
networks" or HINs). Given a large graph and a query pattern with node and edge
label constraints, a fundamental challenge is to nd the top-k matches ac-
cording to a ranking function over edge and node weights. For users, it is di
cult to select value k . We therefore propose the novel notion of an any-k
ranking algorithm: for a given time budget, re- turn as many of the top-ranked
results as possible. Then, given additional time, produce the next lower-ranked
results quickly as well. It can be stopped anytime, but may have to continues
until all results are returned. This paper focuses on acyclic patterns over
arbitrary labeled graphs. We are interested in practical algorithms that
effectively exploit (1) properties of heterogeneous networks, in particular
selective constraints on labels, and (2) that the users often explore only a
fraction of the top-ranked results. Our solution, KARPET, carefully integrates
aggressive pruning that leverages the acyclic nature of the query, and
incremental guided search. It enables us to prove strong non-trivial time and
space guarantees, which is generally considered very hard for this type of
graph search problem. Through experimental studies we show that KARPET achieves
running times in the order of milliseconds for tree patterns on large networks
with millions of nodes and edges.Comment: To appear in WWW 201
Evaluation of local orientation for texture classification
The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture orientation
in the classification process. In this paper the orientation for each pixel in the image is extracted using the
partial derivatives of the Gaussian function and the main focus of our work is centred on the evaluation of
the local dominant orientation (which is calculated by combining the magnitude and local orientation) on
the classification results. While the dominant orientation of the texture depends strongly on the observation
scale, in this paper we propose to evaluate the macro-texture by calculating the distribution of the dominant
orientations for all pixels in the image that sample the texture at micro-level. The experimental results were
conducted on standard texture databases and the results indicate that the dominant orientation calculated at
micro-level is an appropriate measure for texture description
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