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 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

Similarity Search and Analysis Techniques for Uncertain Time Series Data

Emerging applications, such as wireless sensor networks and location-based services, require the ability to analyze large quantities of uncertain time series, where the exact value at each timestamp is unavailable or unknown. Traditional similarity search techniques used for standard time series are not always effective for uncertain time series data analysis. This motivates our work in this dissertation. We investigate new, efficient solution techniques for similarity search and analysis of both uncertain time series models, i.e., PDF-based uncertain time series (having probability density function) and multiset-based uncertain time series (having multiset of observed values) in general, as well as correlation queries in particular. In our research, we first formalize the notion of normalization. This notion is used to introduce the idea of correlation for uncertain time series data. We model uncertain correlation as a random variable that is a basis to develop techniques for similarity search and analysis of uncertain time series. We consider a class of probabilistic, threshold-based correlation queries over such data. Moreover, we propose a few query optimization and query quality improvement techniques. Finally, we demonstrate experimentally how the proposed techniques can improve similarity search in uncertain time series. We believe that our results provide a theoretical baseline for uncertain time series management and analysis tools that will be required to support many existing and emerging applications