Optimization of postprocessing for subsequence matching. Embeddingbased subsequence matching in time series databases 17. To reduce the number of subsequences to be compared with a query sequence, in this section, we suggest the aligned subsequence matching. Since all subsequences may potentially be discords, any algorithm will eventually have to extract all of them. Pattern matching free download as powerpoint presentation. A timeseries is a sequence of real num bers, representing values at specific time points.
Fast fuzzy subsequence matching algorithms on timeseries. It is crucial to exploit and analyze the time series data efficiently. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by. An approach for fast subsequence matching through kmp algorithm. Table 1 shows a list of notations used in the paper.
Section 3 presents the mdmwpdistance and the ranked subsequence matching algorithms based on the distance. Given best paper award also crossreferenced as umiacstr931. We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given query pattern within a specified tolerance. Abstract fast subsequence matching in timeseries databases. Historical, temporal 29 and spatiotemporal 5 databaeea. Mining motifs in massive time series databases computer science. Clustering methodology for time series mining a time series is a sequence of real data, representing the measurements of a real variable at time intervals. Download englishus transcript pdf so, the topic today is dynamic programming. In this work, we pose the new problem of finding the sequence that isleast. Similar subsequence search in time series databases springerlink. Either of those, even though we now incorporate those.
Sequence matching in time series databases is one of the most important data mining applications. Subsequence matching, which consists of index searching and postprocessing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a timeseries database. Efficient processing of subsequence matching with the euclidean metric in timeseries databases. Yangsae moon, jinho kim, fast normalizationtransformed subsequence matching in timeseries databases, ieice transactions on information and systems, v. Fast subsequence matching in timeseries databases 1994. First international conference on knowledge discovery and data mining. Proceedings of the 2010 acm sigmod international conference on. The idea is to map each data sequence into a small set of. Trend similarity and prediction in timeseries databases. Approximate embeddingbased subsequence matching of time series.
The complexity of the dtw algorithm scales linearly with the length of the query and also scales linearly with the size of the database i. The following work is related, in different respects. Using multiple indexes for efficient subsequence matching. Finding matching subsequences in time series data is an important problem. We have introduced an embeddingbased framework for subsequence matching in time series databases that improves the efficiency of processing subsequence matching queries under the dynamic time warping dtw distance measure. Subsequence matching in large time series databases has at tracted a lot of interest. Pdf ranked subsequence matching in timeseries databases. Fast correlation coefficient estimation algorithm for hbase. Subsequence matching on structured time series data. Text and dna strings can be viewed as ldimensional sequences. Time series analysis is a sufficiently wellknown task. Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from timeseries databases.
We present an efficient indexing method to locate 1dimeneional subsequences witbin a collection of sequences, such that the subsequences match a given query pattern within a specified tolerance. We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a. Apparatus and method for similarity searches using hyperrectangle based multidimensional data segmentation us20050114331a1 en. The method reduces false alarms and improves performance by searching the index using the individual points that represent. A fast and robust method for pattern matching in time. Time series subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time series database. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space. Optimization of postprocessing for subsequence matching in. Visualizing and discovering nontrivial patterns in large time. Approximate embeddingbased subsequence matching of. Discovering knowledge from time series databases appears as a complex and multidimensional data process. For example, consider a time series x xtjt 1n 1 where t is the time index and n is the number of observations.
Subsequence matching in large databases of time series and. Time series classification based on the longest common subsequence similarity and ensemble learning 1guancheng guo, 2kuosi huang, and 1. Existing work on similar sequence matching has focused on either whole matching or range subsequence matching. Subsequence matching, which consists of index searching and postprocessing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time series database. Acm sigkdd knowledge discovery in databases home page. All common subsequences hui wang school of computing and mathematics university of ulster, northern ireland, uk h. Ranked subsequence matching in timeseries databases. Supporting the linear detrending in subsequence matching is a challenging problem due to a huge number of possible subsequences. Citeseerx fast subsequence matching in timeseries databases.
A subsequence matching method in timeseries databases, reduces the number of points stored in the multidimensional index and can store individual points directly in the index by dividing the data sequence into disjoint windows using duality in constructing windows. We present an e cient indexing method to locate 1 dimensional subsequences within a collection of sequences, such that the subsequences match a given. Efficient processing of subsequence matching with the. The follo wing w ork is related, in di eren t resp ects. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. Similar sequence patterns can be discovered from time series databases 1,2,7,15,16,21. This paper addresses a performance issue of timeseries subsequence matching. We develop a fast ranked subsequence matching solution for timeseriesdatabases using distances asthe ranking method. Lnai 4571 efficient subsequence matching using the longest. One state of the art measure is the longest common subsequence. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Several methods have been proposed in order to provide algorithms for efficient query. Shinichi morishitas papers at the university of tokyo.
Timeseries subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a timeseries database. Efficient subsequence matching in time series databases. Time series data mining, anomaly detection, clustering. The performance bottleneck of subsequence matching in time. Clustering similar patterns 8,22 and prediction of a very next pattern 20 have been one of main concerns in discovering knowledge. Fast subsequence matching in time series databases. Diversity each dataset of time series may have its own characteristic. For time series matching, there have been a lot of research efforts starting from agrawal et al. Pdf we present an efficient indexing method to locate 1dimensional. If the accuracy of the time series classi cation is considered, the value becomes a critical factor. Embeddingbased subsequence matching in timeseries databases. To this end, we developed viztree, a time series pattern discovery and.
Subsequence matching in such spatiotemporal data is difficult as queryrelevant motions can vary in lengths and occur arbitrarily in a very long motion. Fast subsequence matching in timese ries databases. This paper addresses a performance issue of time series subsequence matching. Interestingly, to the best of authors knowledge, there is relatively few research studies on timeseries fuzzy subsequence matching yet. Fast correlation coefficient estimation algorithm for. In this paper we define this problem the linear detrending subsequence matching and propose. Fast approximate correlation for massive timeseries data. Whole sequence matching and subsequence matching 1 introduction one of the basic problems in handling time series data is locating a pattern of interest from the long sequence of input data 1,2,7. Fast subsequence matching in timeseries databases discrete. A fast and robust method for pattern matching in time series. Similarity search in time series databases is an important research direction.
Fast subsequence matching in timeseries databases acm. Cs349 taught previously as data mining by sergey brin. Us6496817b1 subsequence matching method using duality in. Making subsequence time series clustering meaningful. Lnai 4571 efficient subsequence matching using the. Subsequence matching is a fundamental task in mining time series data. For timeseries matching, there have been a lot of research efforts starting from agrawal et al. This paper discusses optimization of postprocessing for subsequence matching. The definition of a match is rather obvious and intuitive. Proceedings of the 1994 acm sigmod international conference on. Similar sequence patterns can be discovered from timeseries databases 1,2,7,15,16,21. Ok, programming is an old word that means any tabular method for accomplishing something. Locally adaptive dimensionality reduction for indexing large time series databases.
Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time series databases. Pdf fast subsequence matching in timeseries databases. Subsequence matching method using duality in constructing windows in timeseries databases us6778981b2 en 20011017. Section 4 presents an optimization technique to boost the ranked subsequence matching algorithm as well as the windowgroup distance. Discovering knowledge from timeseries databases appears as a complex and multidimensional data process. Introduction timeseries data are of growing importance in many new database applications such as data mining and data ware housinglo. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance. Subsequence matching in time series databases is a useful technique, with applications in pattern matching, prediction, and rule discovery. Introduction the previous decade has seen hundreds of papers on time series similarity search, which is the task of finding a time series that is most similar to a particular query sequence 5. Aligned subsequence matching we assumethat sequencesconsistofa series of realnumbers. We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given. Subsequence time series clustering is used in different fields, such as ecommerce, outlier detection, speech recognition, biological systems, dna recognition, and text mining.
Existing time series similarity measures, such as dtw dynamic time warping, can accommodate certain timing errors in the query and perform with high accuracy on small databases. Dualitybased subsequence matching in timeseries databases. Fast retrieval of similar subsequences in long sequence. Internal structure within the time series data can be used to improve these tasks, and provide important insight into the problem domain. A time series is a sequence of real num bers, representing values at specific time points. Fast retrieval of similar subsequences in long sequence databases.
Fast subsequence matching in timeseries databases core. Yangsae moon, jinho kim, fast normalizationtransformed subsequence matching in time series databases, ieice transactions on information and systems, v. We have introduced an embeddingbased framework for subsequence matching in timeseries databases that improves the efficiency of processing subsequence matching queries under the dynamic time warping dtw distance measure. Each time series has its own linear trend, the directionality of a timeseries, and removing the linear trend is crucial to get the more intuitive matching results. Each timeseries has its own linear trend, the directionality of a timeseries, and removing the linear trend is crucial to get the more intuitive matching results.
Introduction time series data are of growing importance in many new database applications such as data mining and data ware housinglo. Using multiple indexes for efficient subsequence matching in. Clustering of subsequence time series remains an open issue in time series clustering. Motion capture data digitally represent human movements by sequences of body configurations in time. Measuring the similarity of time series is a key to solving these problems. Lohdualitybased subsequence matching in timeseries databases. So, youll hear about linear programming and dynamic programming. Article information, pdf download for visualizing and discovering nontrivial patterns in. Heikki mannilas papers at the university of helsinki. A subsequence matching method in time series databases, reduces the number of points stored in the multidimensional index and can store individual points directly in the index by dividing the data sequence into disjoint windows using duality in constructing windows. For example, the line charts of two datasets, 50words and adiac, are shown in figure 2.
409 1116 1088 279 1227 1111 221 11 71 638 477 179 1077 1297 683 706 101 1068 141 54 1296 1257 1343 359 95 1464 672 1458 1126