D. Barbara, W. DuMouchel, C. Faloutsos, P. Hass, J. M. Hellerstein, Y. Ioannidis, H. Jagadish, T. Johnson, R. Ng, V. Poosala, K. Ross, and K. Sevcik. The new jersey data reduction report. Bulletin of the Technical Committee on Data Engineering, 20(4):3--45, December 1997. Home/Search Document Not in Database Summary Related Articles Check |
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Managing Large Multidimensional Datasets Inside A Database System - Chakrabarti (2001) (Correct)
....forms of aggregate queries. Besides the type of queries supported, another crucial aspect of an approximate query processing technique is the employed data reduction mechanism; that is, the method used to obtain synopses of the data on which the approximate query execution engine can then operate [9]. The methods explored in this context include sampling and, more recently, histograms and wavelets. Sampling based techniques are based on the use of random samples as synopses for large data sets. Sample synopses can be either precomputed and incrementally maintained (e.g. 1, 51] or they ....
D. Barbara, W. DuMouchel, C. Faloutsos, P.J. Haas, J.M. Hellerstein, Y. Ioannidis, H.V. Jagadish, T. Johnson, R. Ng, V. Poosala, K.A. Ross, and K.C. Sevcik. "The New Jersey Data Reduction Report". IEEE Data Engineering Bulletin, 20(4):3--45, December 1997. (Special Issue on Data Reduction Techniques).
Managing Large Multidimensional Datasets Inside A Database System - Chakrabarti (2001) (Correct)
....high dimensional feature spaces (HDFS) it must be used in conjunction with a dimensionality reduction technique in order to exploit the correlations in data and hence achieve further scalability. This approach is commonly used in both multimedia retrieval ( 43, 103, 76, 142] and data mining ([47, 8, 49]) applications. The idea is to first reduce the dimensionality of the data and then index the reduced space using a multidimensional index structure [43] Most of the information in the dataset is condensed to a few dimensions (the first few principal components (PCs) by using principal component ....
D. Barbara, W. DuMouchel, C. Faloutsos, P. Haas, J. Hellerstein, Y. Ionnidis, H. Jagadish, T. Johnson, R. Ng, V. Poosala, K. Ross, and K. Sevcik. The new jersey data reduction report. Data Engineering, 20(4), 1997.
Approximate Join Processing Over Data Streams - Das, Gehrke, Riedewald (2003) (Correct)
.... to keep all relevant tuples in memory (and frequent access to hard disk will be too slow when arrival rates are high) In order to deal with resource limitations in a graceful way, returning approximate query answers instead of exact answers has emerged as a promising approach to save resources [4]. In data stream processing systems, one way of approximating query answers is to shed load, for example, by dropping tuples before they naturally expire (i.e. leave the window) or even before they reach the operator. The current state of the art consists of two main approaches. The first relies ....
D. Barbara, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. E. Ioannidis, H. V. Jagadish, T. Johnson, R. T. Ng, V. Poosala, K. A. Ross, and K. C. Sevcik. The New Jersey data reduction report. IEEE Data Engineering Bulletin, 20(4):3--45, 1997.
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....clusters or outliers, others might not. To date, one has no way of knowing that, unless one resorts to executing the appropriate algorithms on the data. A powerful paradigm to improve the efficiency of a given data analysis task is to reduce the size of the dataset (often called Data Reduction [4]) while keeping the accuracy loss as small as possible. The difficulty lies in designing the appropriate approximation technique for the specific task and datasets. Sampling [48, 36] is a well recognized and widely used statistical technique. In the context of clustering in databases, both ....
D. Barbara, C. Faloutsos, J. Hellerstein, Y. Ioannidis, H. V. Jagadish, T. Johnson, R. Ng, V. Poosala, K. Ross, K.C. Sevcik. The New Jersey Data Reduction Report. In Data Engineering Bulletin, 9f1996
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....e.g. the sum of sales. In contrast, our work achieves a reduction in data volume, while still allowing all dimensions to remain in the warehouse, by offering mechanisms for selectively aggregating data to higher levels. Previous work has also dealt with general techniques for data reduction [2] such as wavelets, sampling, and aggregation. These types of work differ in focus from ours, which offers rule based specifications for when data reduction, in our case using aggregation, should take place, allowing the reduction process to be automatic and transparent to the user. The aggregation ....
D. Barbara, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. Ioannidis, H. V. Jagadish, T. Johnson, R. Ng, V. Poosala, K. A. Ross, and K. C. Sevcik. The New Jersey Data Reduction Report. IEEE Data Engineering Bulletin, 20(4):3--42, 1997.
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D. Barbara, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. E. Ioannidis, H. Jagadish, T. Johnson, R. T. Ng, V. Poosala, K. A. Ross, and K. C. Sevcik. The New Jersey data reduction report. Data Engineering Bulletin, 20(4):3--45, 1997.
TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks - Madden, Franklin.. (2002) (187 citations) Self-citation (Hellerstein) (Correct)
....state records are proportional in size to some (perhaps statistical) property of the data values in the partition. Many approximate aggregates proposed recently in the database literature are content sensitive. Examples of such aggregates include fixed width histograms, wavelets, and so on; see [3] for an overview of such functions. In summary, we have classified aggregates according to their state requirements, tolerance of loss, duplicate sensitivity, and monotonicity. We will refer back to this classification throughout the text, as these properties will determine the applicability of ....
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