Papers by Ing Fernando Lopez
Indexing Time Series Data is an interesting problem that has attracted much interest in the resea... more Indexing Time Series Data is an interesting problem that has attracted much interest in the research community for the last decade. Traditional indexing methods organize the data space using different metrics. For time series, however, there are some cases when a metric is not suited for properly assessing the similarity between sequences. For instance, to detect similarities between sequences that are locally out of phase Dynamic Time Warping (DTW) must be used. DTW is not a metric as it does not satisfy the triangular inequality. Therefore, traditional spatial access methods cannot be used without introducing false dismissals. In such cases, alternative methods for organizing and searching time series data must be proposed. In this paper we propose the use of quantization to generate small and homogeneous representations of time series. We compute upper-and lower-bounds on the DTW distance to a query sequence using this quantized representation to filter-out sequences that cannot be a best match for the query. In the proposed approach, efficient search is achieved by organizing the quantized representation of data in a linear array that can be efficiently read from disk. The computational cost of processing the query is shadowed by the IO cost required to scan the file containing the linear array and it does affect the total query cost.
The ability to model time-varying natures is essential to many database applications such as data... more The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory.
IEEE Transactions on Knowledge and Data Engineering, 2004
We have developed a new indexing strategy that helps overcome the curse of dimensionality for tim... more We have developed a new indexing strategy that helps overcome the curse of dimensionality for time series data. Our proposed approach, called Skyline Index, adopts new Skyline Bounding Regions (SBR) to approximate and represent a group of time series data according to their collective shape. Skyline bounding regions allow us to define a distance function that tightly lower bounds the distance between a query and a group of time series data. In an extensive performance study, we investigate the impact of different distance functions by various dimensionality reduction and indexing techniques on the performance of similarity search, including index pages accessed, data objects fetched, and overall query processing time. In addition, we show that, for k-nearest neighbor queries, the proposed Skyline index approach can be coupled with the state of the art dimensionality reduction techniques such as Adaptive Piecewise Constant Approximation (APCA) and improve its performance by up to a factor of 3.
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Papers by Ing Fernando Lopez