1,825 research outputs found
Query processing of geometric objects with free form boundarie sin spatial databases
The increasing demand for the use of database systems as an integrating
factor in CAD/CAM applications has necessitated the development of database
systems with appropriate modelling and retrieval capabilities. One essential
problem is the treatment of geometric data which has led to the development of
spatial databases. Unfortunately, most proposals only deal with simple geometric
objects like multidimensional points and rectangles. On the other hand, there has
been a rapid development in the field of representing geometric objects with free
form curves or surfaces, initiated by engineering applications such as mechanical
engineering, aviation or astronautics. Therefore, we propose a concept for the realization
of spatial retrieval operations on geometric objects with free form
boundaries, such as B-spline or Bezier curves, which can easily be integrated in
a database management system. The key concept is the encapsulation of geometric
operations in a so-called query processor. First, this enables the definition of
an interface allowing the integration into the data model and the definition of the
query language of a database system for complex objects. Second, the approach
allows the use of an arbitrary representation of the geometric objects. After a
short description of the query processor, we propose some representations for free
form objects determined by B-spline or Bezier curves. The goal of efficient query
processing in a database environment is achieved using a combination of decomposition
techniques and spatial access methods. Finally, we present some experimental
results indicating that the performance of decomposition techniques is
clearly superior to traditional query processing strategies for geometric objects
with free form boundaries
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
6 Access Methods and Query Processing Techniques
The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som
Natural language querying for video databases
Cataloged from PDF version of article.The video databases have become popular in various areas due to the recent advances in technology. Video archive systems need user-friendly interfaces to retrieve video frames. In this paper, a user interface based on natural language processing (NLP) to a video database system is described. The video database is based on a content-based spatio-temporal video data model. The data model is focused on the semantic content which includes objects, activities, and spatial properties of objects. Spatio-temporal relationships between video objects and also trajectories of moving objects can be queried with this data model. In this video database system, a natural language interface enables flexible querying. The queries, which are given as English sentences, are parsed using link parser. The semantic representations of the queries are extracted from their syntactic structures using information extraction techniques. The extracted semantic representations are used to call the related parts of the underlying video database system to return the results of the queries. Not only exact matches but similar objects and activities are also returned from the database with the help of the conceptual ontology module. This module is implemented using a distance-based method of semantic similarity search on the semantic domain-independent ontology, WordNet. (C) 2008 Elsevier Inc. All rights reserved
NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA
Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002
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