Diego Seco

Clustering-based similarity search in metric spaces with sparse spatial centers

Metric spaces are a very active research field which offers efficient methods for indexing and searching by similarity in large data sets. In this paper we present a new clustering-based method for similarity search called SSSTree. Its main characteristic is that the centers of each cluster are selected using Sparse Spatial Selection (SSS), a technique initially developed for the selection of pivots. SSS is able to adapt the set of selected points (pivots or cluster centers) to the intrinsic dimensionality of the space. Using SSS, the number of clusters in each node of the tree depends on the complexity of the subspace it represents. The space partition in each node will be made depending on that complexity, improving thus the performance of the search operation. In this paper we present this new method and provide experimental results showing that SSSTree performs better than previously proposed indexes.

Space efficient wavelet tree construction

Wavelet trees are one of the main building blocks in many space efficient data structures. In this paper, we present new algorithms for constructing wavelet trees, based on in-place sorting, that use virtually no extra space. Furthermore, we implement and confirm that these algorithms are practical by comparing them to a known construction algorithm. This represents a step forward for practical space-efficient data structures, by allowing their construction on more massive data sets.

A fun application of compact data structures to indexing geographic data

The way memory hierarchy has evolved in recent decades has opened new challenges in the development of indexing structures in general and spatial access methods in particular. In this paper we propose an original approach to represent geographic data based on compact data structures used in other fields such as text or image compression. A wavelet tree-based structure allows us to represent minimum bounding rectangles solving geographic range queries in logarithmic time. A comparison with classical spatial indexes, such as the R-tree, shows that our structure can be considered as a fun, yet seriously competitive, alternative to these classical approaches.

A New Point Access Method Based on Wavelet Trees

The development of index structures that allow efficient retrieval of spatial objects has been a topic of interest in the last decades. Most of these structures have been designed for secondary memory. However, in the last years the price of memory has decreased drastically. Nowadays it is feasible to place complete spatial indexes in main memory. In this paper we focus in a subcategory of spatial indexes named Point Access Methods. These indexes are designed to solve the problem of indexing points. We present a new index structure designed for two dimensions and main memory that keeps a good trade-off between the space needed to store the index and its search efficiency. Our structure is based on a wavelet tree , which was originally designed to represent sequences, but has been successfully used as an index in areas like information retrieval or image compression.

Space-efficient representations of rectangle datasets supporting orthogonal range querying

The increasing use of geographic search engines manifests the interest of Internet users in geo-located resources and, in general, in geographic information. This has emphasized the importance of the development of efficient indexes over large geographic databases. The most common simplification of geographic objects used for indexing purposes is a two-dimensional rectangle. Furthermore, one of the primitive operations that must be supported by every geographic index structure is the orthogonal range query, which retrieves all the geographic objects that have at least one point in common with a rectangular query region. In this work, we study several space-efficient representations of rectangle datasets that can be used in the development of geographic indexes supporting orthogonal range queries.

An Ontology-Based Index to Retrieve Documents with Geographic Information

Both Geographic Information Systemsand Information Retrievalhave been very active research fields in the last decades. Lately, a new research field called Geographic Information Retrievalhas appeared from the intersection of these two fields. The main goal of this field is to define index structures and techniques to efficiently store and retrieve documents using both the text and the geographic references contained within the text. We present in this paper a new index structure that combines an inverted index, a spatial index, and an ontology-based structure. This structure improves the query capabilities of other proposals. In addition, we describe the architecture of a system for geographic information retrieval that uses this new index structure. This architecture defines a workflow for the extraction of the geographic references in the document.

On Minimum- and Maximum-Weight Minimum Spanning Trees with Neighborhoods

We study optimization problems for the Euclidean Minimum Spanning Tree (MST) problem on imprecise data. To model imprecision, we accept a set of disjoint disks in the plane as input. From each member of the set, one point must be selected, and the MST is computed over the set of selected points. We consider both minimizing and maximizing the weight of the MST over the input. The minimum weight version of the problem is known as the Minimum Spanning Tree with Neighborhoods (MSTN) problem, and the maximum weight version (max-MSTN) has not been studied previously to our knowledge. We provide deterministic and parameterized approximation algorithms for the max-MSTN problem, and a parameterized algorithm for the MSTN problem. Additionally, we present hardness of approximation proofs for both settings.

An inconsistency measure of spatial data sets with respect to topological constraints

An inconsistency measure can be used to compare the quality of different data sets and to quantify the cost of data cleaning. In traditional relational databases, inconsistency is defined in terms of constraints that use comparison operators between attributes. Inconsistency measures for traditional databases cannot be applied to spatial data sets because spatial objects are complex and the constraints are typically defined using spatial relations. This paper proposes an inconsistency measure to evaluate how dirty a spatial data set is with respect to a set of integrity constraints that define the topological relations that should hold between objects in the data set. The paper starts by reviewing different approaches to quantify the degree of inconsistency and showing that they are not suitable for the problem. Then, the inconsistency measure of a data set is defined in terms of the degree in which each spatial object in the data set violates topological constraints, and the possible representations of spatial objects are points, curves, and surfaces. Finally, an experimental evaluation demonstrates the applicability of the proposed inconsistency measure and compares it with previously existing approaches.

Efficient Wavelet Tree Construction and Querying for Multicore Architectures

Wavelet trees have become very useful to handle large data sequences efficiently. By the same token, in the last decade, multicore architectures have become ubiquitous, and parallelism in general has become extremely important in order to gain performance. This paper introduces two practical multicore algorithms for wavelet tree construction that run in O(n) time using

Parallel construction of wavelet trees on multicore architectures

\lg \sigma