Generalized Distance Metric as a Robust Similarity Measure for Mobile Object Trajectories

In this paper, we propose a novel generalized distance metric based on a model that incorporates the time axis explicitly. The proposed metric is based fundamentally on the Mahalanobis distance metric, which eliminates the correlation and scaling errors in similarity searches on trajectory databases. We propose the incorporation of a weight matrix in the proposed distance metric, which allows for easy manipulation of the degree of significance of the different spatial and or temporal dimensions

Fast Online Similarity Search for Uncertain Time Series

To achieve fast retrieval of online data, it is needed for the retrieval algorithm to increase throughput while reducing latency. Based on the traditional online processing algorithm for time series data, we propose a spatial index structure that can be updated and searched quickly in a real-time environment. At the same time, we introduce an adaptive segmentation method to divide the space corresponding to nodes. Unlike traditional retrieval algorithms, for uncertain time series, the distance threshold used for screening will dynamically change due to noise during the search process. Extensive experiments are conducted to compare the accuracy of the query results and the timeliness of the algorithm. The results show that the index structure proposed in this paper has better efficiency while maintaining a similar true positive ratio

Spatial Index for Uncertain Time Series

A search for patterns in uncertain time series is time-expensive in today\u27s large databases using the currently available methods. To accelerate the search process for uncertain time series data, in this paper, we explore a spatial index structure, which uses uncertain information stored in minimum bounding rectangle and ameliorates the general prune/search process along the path from the root to leaves. To get a better performance, we normalize the uncertain time series using the weighted variance before the prune/hit process. Meanwhile, we add two goodness measures with respect to the variance to improve the robustness. The extensive experiments show that, compared with the primitive probabilistic similarity search algorithm, the prune/hit process of the spatial index can be more efficient and robust using the specific preprocess and variant index operations with just a little loss of accuracy

Fast and accurate NN approach for multi-event annotation of time series

technical reportSimilarity search in time-series subsequences is an important time series data mining task. Searching in time series subsequences for matches for a set of shapes is an extension of this task and is equally important. In this work we propose a simple but efficient approach for finding matches for a group of shapes or events in a given time series using a Nearest Neighbor approach. We provide various improvements of this approach including one using the GNAT data structure. We also propose a technique for finding similar shapes of widely varying temporal width. Both of these techniques for primitive shape matching allow us to more accurately and efficiently form an event representation of a time-series, leading in turn to finding complex events which are composites of primitive events. We demonstrate the robustness of our approaches in detecting complex shapes even in the presence of ?don?t care? symbols. We evaluate the success of our approach in detecting both primitive and complex shapes using a data set from the Fluid Dynamics domain. We also show a speedup of up to 5 times over a na?ve nearest neighbor approach

Similarity searching in sequence databases under time warping.

Wong, Siu Fung.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-84).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 2 --- Preliminary --- p.6Chapter 2.1 --- Dynamic Time Warping (DTW) --- p.6Chapter 2.2 --- Spatial Indexing --- p.10Chapter 2.3 --- Relevance Feedback --- p.11Chapter 3 --- Literature Review --- p.13Chapter 3.1 --- Searching Sequences under Euclidean Metric --- p.13Chapter 3.2 --- Searching Sequences under Dynamic Time Warping Metric --- p.17Chapter 4 --- Subsequence Matching under Time Warping --- p.21Chapter 4.1 --- Subsequence Matching --- p.22Chapter 4.1.1 --- Sequential Search --- p.22Chapter 4.1.2 --- Indexing Scheme --- p.23Chapter 4.2 --- Lower Bound Technique --- p.25Chapter 4.2.1 --- Properties of Lower Bound Technique --- p.26Chapter 4.2.2 --- Existing Lower Bound Functions --- p.27Chapter 4.3 --- Point-Based indexing --- p.28Chapter 4.3.1 --- Lower Bound for subsequences matching --- p.28Chapter 4.3.2 --- Algorithm --- p.35Chapter 4.4 --- Rectangle-Based indexing --- p.37Chapter 4.4.1 --- Lower Bound for subsequences matching --- p.37Chapter 4.4.2 --- Algorithm --- p.41Chapter 4.5 --- Experimental Results --- p.43Chapter 4.5.1 --- Candidate ratio vs Width of warping window --- p.44Chapter 4.5.2 --- CPU time vs Number of subsequences --- p.45Chapter 4.5.3 --- CPU time vs Width of warping window --- p.46Chapter 4.5.4 --- CPU time vs Threshold --- p.46Chapter 4.6 --- Summary --- p.47Chapter 5 --- Relevance Feedback under Time Warping --- p.49Chapter 5.1 --- Integrating Relevance Feedback with DTW --- p.49Chapter 5.2 --- Query Reformulation --- p.53Chapter 5.2.1 --- Constraint Updating --- p.53Chapter 5.2.2 --- Weight Updating --- p.55Chapter 5.2.3 --- Overall Strategy --- p.58Chapter 5.3 --- Experiments and Evaluation --- p.59Chapter 5.3.1 --- Effectiveness of the strategy --- p.61Chapter 5.3.2 --- Efficiency of the strategy --- p.63Chapter 5.3.3 --- Usability --- p.64Chapter 5.4 --- Summary --- p.71Chapter 6 --- Conclusion --- p.72Chapter A --- Deduction of Data Bounding Hyper-rectangle --- p.74Chapter B --- Proof of Theorem2 --- p.76Bibliography --- p.77Publications --- p.8

COOPERATIVE QUERY ANSWERING FOR APPROXIMATE ANSWERS WITH NEARNESS MEASURE IN HIERARCHICAL STRUCTURE INFORMATION SYSTEMS

Cooperative query answering for approximate answers has been utilized in various problem domains. Many challenges in manufacturing information retrieval, such as: classifying parts into families in group technology implementation, choosing the closest alternatives or substitutions for an out-of-stock part, or finding similar existing parts for rapid prototyping, could be alleviated using the concept of cooperative query answering. Most cooperative query answering techniques proposed by researchers so far concentrate on simple queries or single table information retrieval. Query relaxations in searching for approximate answers are mostly limited to attribute value substitutions. Many hierarchical structure information systems, such as manufacturing information systems, store their data in multiple tables that are connected to each other using hierarchical relationships - "aggregation", "generalization/specialization", "classification", and "category". Due to the nature of hierarchical structure information systems, information retrieval in such domains usually involves nested or jointed queries. In addition, searching for approximate answers in hierarchical structure databases not only considers attribute value substitutions, but also must take into account attribute or relation substitutions (i.e., WIDTH to DIAMETER, HOLE to GROOVE). For example, shape transformations of parts or features are possible and commonly practiced. A bar could be transformed to a rod. Such characteristics of hierarchical information systems, simple query or single-relation query relaxation techniques used in most cooperative query answering systems are not adequate. In this research, we proposed techniques for neighbor knowledge constructions, and complex query relaxations. We enhanced the original Pattern-based Knowledge Induction (PKI) and Distribution Sensitive Clustering (DISC) so that they can be used in neighbor hierarchy constructions at both tuple and attribute levels. We developed a cooperative query answering model to facilitate the approximate answer searching for complex queries. Our cooperative query answering model is comprised of algorithms for determining the causes of null answer, expanding qualified tuple set, expanding intersected tuple set, and relaxing multiple condition simultaneously. To calculate the semantic nearness between exact-match answers and approximate answers, we also proposed a nearness measuring function, called "Block Nearness", that is appropriate for the query relaxation methods proposed in this research