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On Similarity Queries for TimeSeries Data: Constraint Specification and Implementation
, 1995
"... Constraints are a natural mechanism for the specification of similarity queries on timeseries data. However, to realize the expressive power of constraint programming in this context, one must provide the matching implementation technology for efficient indexing of very large data sets. In this pap ..."
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Cited by 106 (4 self)
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Constraints are a natural mechanism for the specification of similarity queries on timeseries data. However, to realize the expressive power of constraint programming in this context, one must provide the matching implementation technology for efficient indexing of very large data sets. In this paper, we formalize the intuitive notions of exact and approximate similarity between timeseries patterns and data. Our definition of similarity extends the distance metric used in [2, 7] with invariance under a group of transformations. Our main observation is that the resulting, more expressive, set of constraint queries can be supported by a new indexing technique, which preserves all the desirable properties of the indexing scheme proposed in [2, 7].
Similarity Search in Time Series Data Sets
, 1997
"... Similarity search on timeseries data sets is of growing importance in data mining. With the increasing amount of data of timeseries in many applications, from financial to scientific, it is important to study the methods of retrieving similarity patterns efficiently and user friendly for business ..."
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Cited by 10 (0 self)
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Similarity search on timeseries data sets is of growing importance in data mining. With the increasing amount of data of timeseries in many applications, from financial to scientific, it is important to study the methods of retrieving similarity patterns efficiently and user friendly for business decision making. The thesis proposes methods of efficient retrieval of all objects in the timeseries database with a shape similar to a search template. The search template can be either a shape or a sequence of data. Two search modules, subsequence search and whole sequence search, are designed and implemented. We study a set of linear transformations that can be used as the basis for similarity queries on timeseries data, and design an innovative representation technique which abstracts the shape notion so that the user can interactively query and answer the multilevel similarity patterns. The wavelet analysis technique and the OLAP technique used in knowledge discovery and data warehou...
Image registration and object recognition using affine invariants and convex hulls
 IEEE Transactions on Image Processing
, 1999
"... Abstract — This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations, and under possible occlusion and/or appearance of new objects. It presents a set of local ..."
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Cited by 9 (0 self)
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Abstract — This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations, and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering v feature points on the test image with x feature points of the reference image is of order y@x 2 vA vA. vA The method has been tested on real indoor and outdoor images and performs well. Index Terms—Affine invariants, affine transformations, alignment, convex hull, occlusion, perspective, registration, weak. I.
Flexible and Efficient Similarity Querying for Timeseries Data
, 2003
"... We present a flexible and efficient method for similarity querying in timeseries databases. A sequence S is considered similar to a query sequence Q if the sequences match (up to some tolerance ε) after S is appropriately shifted and scaled. Our method allows the user... ..."
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Cited by 1 (0 self)
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We present a flexible and efficient method for similarity querying in timeseries databases. A sequence S is considered similar to a query sequence Q if the sequences match (up to some tolerance &epsilon;) after S is appropriately shifted and scaled. Our method allows the user...
Classical geometrical approach to circle . . .
, 2003
"... After a review of the circle fitting issue, we recall a relatively unknown method derived from a classical geometric result. We propose an improvement of this technique by reweighting the data, iterating the procedure, and choosing at every step as the new inversion point the one diametrically oppo ..."
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After a review of the circle fitting issue, we recall a relatively unknown method derived from a classical geometric result. We propose an improvement of this technique by reweighting the data, iterating the procedure, and choosing at every step as the new inversion point the one diametrically opposite to the previous inversion point.
1.1 Approximate Matching of TimeSeries Data
"... Abstract. Constraints are a natural mechanism for the speci cation of similarity queries on timeseries data. However, to realize the expressive power of constraint programming in this context, one must provide the matching implementation technology for e cient indexing of very large data sets. In t ..."
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Abstract. Constraints are a natural mechanism for the speci cation of similarity queries on timeseries data. However, to realize the expressive power of constraint programming in this context, one must provide the matching implementation technology for e cient indexing of very large data sets. In this paper, we formalize the intuitive notions of exact and approximate similarity between timeseries patterns and data. Our de nition of similarity extends the distance metric used in [2, 7] with invariance under a group of transformations. Our main observation is that the resulting, more expressive, set of constraint queries can be supported by a new indexing technique, which preserves all the desirable properties of the indexing scheme proposed in [2, 7].
Constraint Query Algebras
 Constraints Journal
, 1997
"... Pages : : : University Microfilms Agreement (signed): : : Survey of Earned Doctorates : : : Career Plans Questionnaire : : : Constraint Query Algebras by Dina Q Goldin B.A. Yale University, 1985 M.S. Brown University, 1987 Thesis Submitted in partial fulfillment of the requirements for the ..."
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Pages : : : University Microfilms Agreement (signed): : : Survey of Earned Doctorates : : : Career Plans Questionnaire : : : Constraint Query Algebras by Dina Q Goldin B.A. Yale University, 1985 M.S. Brown University, 1987 Thesis Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in the Department of Computer Science at Brown University Copyright by Dina Q Goldin 1997 This dissertation by Dina Q Goldin is accepted in its present form by the Department of Computer Science as satisfying the dissertation requirement for the degree of Doctor of Philosophy.
Normalization of Life Science Data for Shapebased Similarity Querying
"... In this paper, we focus on shapebased similarity querying over life science data, and discuss the key role played by normalization in this context. Our treatment of normalization is based on the semantics of similarity transformations, which are mathematical groups. We make an explicit association ..."
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In this paper, we focus on shapebased similarity querying over life science data, and discuss the key role played by normalization in this context. Our treatment of normalization is based on the semantics of similarity transformations, which are mathematical groups. We make an explicit association between normalization and equivalence classes over data, contaning data items with the same shape. Normalization obtains (a) the normal form of each data item, which serves as a representative of its equivalence class, together with (b) the normalization parameters that allow us to map the normal form back to the original item. Normalizationbased notions of similarity distance are also treated.
Bounded similarityquerying for timeseries data �
, 2003
"... We define the problem of bounded similarity querying in timeseries databases, which generalizes earlier notions of similarityquerying. Given a (sub)sequence S, a querysequence Q, lower and upper bounds on shifting and scaling parameters, and a tolerance, S is considered boundedly similar to Q if S ..."
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We define the problem of bounded similarity querying in timeseries databases, which generalizes earlier notions of similarityquerying. Given a (sub)sequence S, a querysequence Q, lower and upper bounds on shifting and scaling parameters, and a tolerance, S is considered boundedly similar to Q if S can be shifted and scaled within the specified bounds to produce a modified sequence S ′ whose distance from Q is within.Weusesimilarity transformation to formalize the notion of bounded similarity. We then describe a framework that supports the resulting set of queries; it is based on a fingerprint method that normalizes the data and saves the normalization parameters. For offline data, we provide an indexing method with a single index structure and search technique for handling all the special cases of bounded similarityquerying. Experimental investigations find the performance of our method to be competitive with earlier, less general approaches.