Agrawal, R., Lin, K. I., Sawhney, H. S., and Swim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings 21st VLDB. Home/Search Document Not in Database Summary Related Articles Check |
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Database Systems Supporting Next Decade's Applications - Böhm (Correct)
....commercial DBMS. Therefore, the information infrastructure of most enterprises is based on products such as Oracle or Informix. In recent years, an increasing number of applications has emerged processing large amounts of complex, applicationspecific data objects [Jag 91, GM 93, FBF 94, FRM 94, ALSS 95, KSF 96] In application domains such as multimedia, medical imaging, molecular biology, computer aided design, marketing and purchasing assistance, etc. a high efficiency of query processing is crucial due to the immense and even increasing size of current databases. The search in such ....
....that match the query person with a probability of at least 10 . determine the person that matches the query person with maximum probability. Technical Analysis of Share Price One of the classical applications of similarity search and data mining is clearly the analysis of time sequences [ALSS 95] such as share price analysis. Various similarity measures have been proposed. For practical analysis, however, quite different concepts are used, such as indicators, i.e. mathematical formulas derived from the time sequence that generate trading signals (buy, sell) Another concept for the ....
Agrawal R., Lin K., Sawhney H., Shim K.: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases, Proc. of the 21st Int. Conf. on Very Large Databases, 1995, pp. 490-501.
Probabilistic Discovery of Time Series Motifs - Chiu, Keogh, Lonardi (2003) (Correct)
....1000 1500 2000 2500 Figure 1: Above) An example of a motif that occurs three times in a complex and noisy industrial dataset. Below) a zoom in reveals just how similar the three occurrences are to each other There exists a vast body of work on efficiently locating known patterns in time series [1, 6, 12, 23, 35, 36, 37]. Here, however, we must be able to discover motifs without any prior knowledge about the regularities of the data under study. The obvious, nested loop, brute force approach to motif discovery would require a number of comparisons quadratic in the length of the database. Optimizations based on ....
....mine noisy datasets. Figure 3 also shows that allowing small don t care subsections (that is, sections which are ignored by the distance function) allows much more intuitive results to be obtained. We note that the utility of allowing don t care sections in time series has been documented before [1, 22], and it is a cornerstone of text and Biosequences data mining [3, 24, 25, 28, 30, 34] The previous example illustrates the dangers of mining in the presence of noise. Indeed, this single spike might be best taken care of with a simple smoothing algorithm. More generally, however, we may have a ....
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Agrawal, R., Lin, K. I., Sawhney, H. S. & Shim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In proceedings of the 21 st Int'l Conference on Very Large Databases. Zurich, Switzerland, Sept. pp 490-50.
Stardust: Fast Stream Indexing using Incremental Wavelet.. - Bulut, Singh (Correct)
....patterns, to process user queries in a fast and an accurate manner, and to compute statistics on data streams in real time. 1.1 Related work There has been a substantial body of work on similarity search in sequence databases. Various high di mensional index structures have been proposed in [2, 3, 11, 16, 27, 29, 33, 37] to achieve fast query response time and a good quality of answers. Theoretical methods have been developed for com paring data streams under various Lp distances [12] for clustering and computing the k median [22, 32] and for computing aggregates over data streams [14, 18] Various ....
R. Agrawal, K. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in presence of noise, scaling, and translation in time-series databases. In VLDB, pages 490-501, 1995.
Efficient Storage, Retrieval and Indexing of Time Series Data - Chortaras (Correct)
....a given sequence. What exactly does similar mean depends on the application, and the definition of similarity may vary. This type of queries can be very useful when analysing financial or scientific data, for example for prediction and cluster ing purposes. Some examples of such queries are [1] [2], 8] 1. Determine products with similar selling patterns. 2. Find stocks whose stock prices move similarly. 3. Find cases in the past that resemble last year s sales pattern of a certain product. 4. Find portions of seismic waves that are not similar to spot geological irregularities. The ....
....that accounts for scaling and shifting is presented in [11] where it is proposed that the time sequences be first normalized before applying the distance met tic. Several other, more flexible definitions of similarity have been proposed in order to account for example, for the presence of noise [2] and time warping [31] In [24] a gen eral landmark similarity is introduced, which is invariant to shifting, uniform and non uniform amplitude scaling, uniform time scaling and time warping. a) b) c) Figure 2.1: Trancbrmations on time sequences: a) 4mplitude shine5 (b) Unirm amplitude ....
R. Agrawal, K.-I. Lin, H.S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In The V7DB Journal, 1995.
Supporting User Interaction for the Exploratory Mining of.. - Mah (Correct)
....sequences. Mannila, Toivonen, and Verkamo did vork in the same field, but concentrated more on finding frequent episodes (sub sequences) in specified time vindovs vithin sequences [15] They proposed an algorithm to find similar sub sequences betveen sequences of data in time series databases [2]. In this model, the sequences in question can be scaled or translated before similar sub sequences are found. Sequential data mining can have many applications, especially in the financial area, vhere it is usually used to find similar grovth patterns in companies, stocks, or product sales. 2.1 ....
R. Agrawal, K. Lin, H. Sawhney and K. Shim. Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. In Proc. 21st VLDB, pages 490-501, 1995.
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Agrawal, R., Lin, K. I., Sawhney, H. S., and Swim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings 21st VLDB.
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R. Agrawal, K.-I. Lin, H.S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In VLDB'95.
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R. Agarwal, K. Lin, H. Sawhney and K. Shim. Fast similarity search in the presence of noise, scaling and translation in time-series databases. Proc. 21st VLDB conf, 1995.
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R. Agarwal, K. Lin, H. Sawhney and K. Shim. Fast similarity search in the presence of noise, scaling and translation in time-series databases, Proc. VLDB conference, 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of 21th International Conference on Very Large Data Bases (VLDB'95), 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. VLDB Conference, pages 490-501, 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of the 21st International Conference on Very Large Data Bases, pages 490--501, Zurich, Switzerland, 1995.
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Agrawal, R., Lin, K.I., Sawhney, H.S., & Shim, K. Fast similarity search in the presence of noise, scaling, and translation in times-series databases. In VLDB, September. 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series database. In Proc. of the VLDB Conf., Zurich, Switzerland, 1995.
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R. Agrawal, K. Lin, H. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time--series databases. In 21st VLDB, pg. 490--501,1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of the 21 st International Conference on Very Large Databases (VLDB), Zurich, Switzerland, September 1995.
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Agrawal R., Lin K.-I., Sawhney H., Shim K.: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. Proc. 21th Int. Conf. on Very Large Databases (VLDB 95), pages 490-501, 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In The VLDB Journal, pages 490-501, 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. In Proceedings of the 21st VLDB Conference, pages 490--501, Zurich, Switzerland, September 1995.
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R. Agrawal, K. Lin, H. Sawhney, K. Shim. Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. Proc. 21st VLDB Conf., pp. 490-501, 1995.
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# R. Agrawal, K. Lin, H. S. Sawhney, and K. Shim. Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. In Proc. of the 21st Int. Conf. on Very Large Databases, Zurich, Switzerland, September 1995.
Paper Reference No.: 192 - Authors Contact Author (2003) (Correct)
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R. Agrawal, K. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in presence of noise, scaling, and translation in time-series databases. In VLDB, pages 490--501, 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proc. the 21st Int'l Conf. on Very Large Data Bases, pages 490-501, 1995.
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R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In The VLDB Journal, pages 490--501, 1995.
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Agrawal, R., Lin, K.-L., Sawhney, H. S., and Shim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in timeseries databases. In Proc. of the 21st Int. Conf. on Very Large Databases, Zurich, Switzerland.
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