S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. T. Lu, "Spatial databases: Accomplishments and research needs," IEEE Trans. Knowledge Data Eng., vol. 11, Jan./Feb. 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....University of Minnesota, Minneapolis, MN 55455, USA. Email: fchawla,shekhar,wuwg cs.umn.edu Department of Environmental Sciences, Ericyes University, Kayseri, Turkey. Email:uozesmi erciyes.edu.tr 1 Introduction Widespread use of spatial databases [Guting, 1994, S. Shekhar and S. Chawla, 2000, Shekhar et al. 1999, Worboys, 1995] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns[Koperski et al. 1996, Mark, 1999, Greenman, 2000, Roddick and Spiliopoulou, 1999] Efficient tools for extracting information from geo spatial data, the focus of this work, are ....

Shekhar, S., Chawla, S., Ravada, S., A.Fetterer, X.Liu, and Lu, C. (1999). Spatial databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1).

....spatial statistics approaches, PLUMS achives comparable accuracy but at a fraction of the computational cost. Furthermore, PLUMS provides a general framework for specializing other data mining techniques for mining spatial data. 1 Introduction Widespread use of spatial databases [14, 31, 34, 38] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns[19, 24, 12, 30] Efficient tools for extracting information from geo spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial data sets. These ....

S. Shekhar, S. Chawla, S. Ravada, A.Fetterer, X.Liu, and C.T. Lu. Spatial databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1), Jan-Feb 1999.

....or of the United States Government. 1 1 Introduction In many application domains, images comprise the majority of acquired data. The management of large volumes of digital images has generated additional interest in methods and tools for real time archiving and retrieval of images by content [1, 2, 3]. Several approaches to the problem of content based image management have been proposed and some have been implemented on research prototypes and commercial systems: In the Virage system [4] image content is given primarily in terms of properties of color and texture. The QBIC system of IBM [5] ....

S. Shekhar, S. Chawla an S. Ravada, A. Fetterer, X. Liu, and C.-T. Lu. Spatial Databases - Accomplishments and Research Needs. IEEE Trans. on Knowledge and Data Engineering, 11(1):45--55, Jan./Feb. 1999. 14

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S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. T. Lu, "Spatial databases: Accomplishments and research needs," IEEE Trans. Knowledge Data Eng., vol. 11, Jan./Feb. 1999.

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S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. t. Lu. Spatial databases--accomplishments and research needs. IEEE Trans. Knowledge and Data Eng.,, 11(1), 1999.

....in # different ways. The SQL aggregate functions and the group by operators only produce one out of # aggregates at a time. A data cube [12] operator computes all # aggregates in one shot. Spatial data warehouses contain geographic data, e.g. satellite images, aerial photographs [23, 13, 20, 24], in addition to non spatial data. Examples of spatial datawarehouses include the US Census dataset [11] Earth Observation System archives of satellite imagery [29] Sequoia 2000 [27] and highway traffic measurement archives. The research on spatial data warehouses has focused on casestudies [7, ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. Lu. Spatial databases: Accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering (TKDE), 11(1):45--55, 1999.

....to be computed for remaining pairs. Using experimental studies with Earth science datasets, we show that the filter and refine approach can save a large fraction of the computational cost, particularly when the minimal correlation threshold is high. 1 Introduction Spatio temporal data mining [14, 16, 15, 17, 13, 7] is important in many application domains such as epidemiology, ecology, climatology, or census statistics, where datasets which are spatio temporal in nature are routinely collected. The development of e#cient tools [1, 4, 8, 10, 11] to explore these datasets, the focus of this work, is crucial ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial databases: Accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

....new processing strategies for correlation based similarity range queries and similarity joins. We provide a preliminary evaluation of the proposed strategies using algebraic cost models and experimental studies with Earth science datasets. 1 Introduction Analysis of spatio temporal datasets [17, 19, 20, 11] collected by satellites, sensor nets, retailers, mobile device servers, and medical instruments on a daily basis is important for many application domains such as epidemiology, ecology, climatology, and census statistics. The development of ecient tools [2, 6, 12] to explore these datasets, the ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE TKDE, 11(1), 1999.

....new processing strategies for correlation based similarity range queries and similarity joins. We provide a preliminary evaluation of the proposed strategies using algebraic cost models and experimental studies with Earth science datasets. 1 Introduction Analysis of spatio temporal datasets [17, 19, 20, 11] collected by satellites, sensor nets, retailers, mobile device servers, and medical instruments on a daily basis is important for many application domains such as epidemiology, ecology, climatology, and census statistics. The development of e#cient tools [2, 6, 12] to explore these datasets, the ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE TKDE, 11(1), 1999.

....in 2 different ways. The SQL aggregate functions and the group by operators only produce one out of 2 aggregates at a time. A data cube [12] operator computes all 2 aggregates in one shot. Spatial data warehouses contain geographic data, e.g. satellite images, aerial photographs [23, 13, 20, 24], in addition to non spatial data. Examples of spatial datawarehouses include the US Census dataset [11] Earth Observation System archives of satellite imagery [29] Sequoia 2000 [27] and highway traffic measurement archives. The research on spatial data warehouses has focused on casestudies [7, ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. Lu. Spatial databases: Accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering (TKDE), 11(1):45--55, 1999.

....using space filling curves, such as Z order or Hilbert, to improve the performance of the sorting based method. However, min cut graph partitioning, the method used by AC, outperforms space filling curves in the clustering of non uniformly distributed spatial data, as shown in our previous work [37, 38]. The Sorting based heuristic clusters the nodes of the first page level relation and then loads the pages in the sorted order. The loading sequence for the example in Figure 4 is shown in Table 2. With a buffer size of seven, the sorting heuristic will load pages f1; 2; 3; 4; 5; 6g from the ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

....to be computed for remaining pairs. Using experimental studies with Earth science datasets, we show that the lter and re ne approach can save a large fraction of the computational cost, particularly when the minimal correlation threshold is high. 1 Introduction Spatio temporal data mining [14, 16, 15, 17, 13, 7] is important in many application domains such as epidemiology, ecology, climatology, or census statistics, where datasets which are spatio temporal in nature are routinely collected. The development of ecient tools [1, 4, 8, 10, 11] to explore these datasets, the focus of this work, is crucial to ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial databases: Accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45-55, 1999.

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S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial databases: Accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering(TKDE), 11(1):45-55, 1999.

....and video data types. Secondly, spatial concepts and techniques are often crucial in indexing and retrieval of image and video databases. Finally, according to several estimates, spatial data constitutes almost 80 of all digital data including multimedia data. Widespread use of spatial databases [28], is leading to an increasing interest in mining interesting and useful but implicit spatial patterns[14, 19, 10, 26] Traditional data mining algorithms[l] Department of Computer Science, University of Minnesota, Minneapolis, MN 55455, USA. Supported in part by the Army High Performance ....

S. Shekhar, S. Chawla, S. Ravada, A.Fetterer, X.Liu, and C.T. Lu. Spatial databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1), Jan-Feb 1999.

....policy of the government and no ocial endorsement should be inferred. Access to computing facilities was provided by the AHPCRC and the Minnesota Supercomputing Institute. The contact author. E mail: pusheng cs.umn.edu. Tel: 612) 626 7515 1 Introduction Analysis of spatio temporal datasets [12, 21, 22, 24, 25, 26, 29] collected by satellites, sensor nets, retailers, mobile device servers, and medical instruments on a daily basis is important for many application domains such as epidemiology, ecology, climatology, and census statistics. The development of ecient tools [3, 7, 13, 16, 18, 14] to explore these ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial databases: Accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45-55, 1999.

.... of the Department of the Army, Army Research Laboratory cooperative agreement number DAAD19 01 2 0014, the content of which does not necessarily re ect the position or the policy of the government, and no ocial endorsement should be inferred 1 Introduction Widespread use of spatial databases [10, 24, 25, 36] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns [9, 16, 20, 23, 32, 26, 28, 5, 29, 35] For example, E services are growing along with mobile computing infrastructures such as PDAs and celluar phones. Finding E services frequently located ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1), Jan-Feb 1999.

....overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future. 1 Introduction Widespread use of spatial databases [8, 21, 22, 28] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns [7, 13, 17, 20, 26] Ecient tools for extracting information from geo spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial datasets. These ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1), Jan-Feb 1999.

....queries in spatial databases refer to finding the spatial objects nearest to some given query points. NN queries are used in a wide range of applications, such as Geographic Information Systems (GIS) Computer Aided Design (CAD) computational biology, decision support, and pattern recognition [26]. NN queries in spatial databases can be classified into five major categories: simple k NN queries [2, 6, 8, 9, 16, 22, 23, 25] approximate k NN queries [3, 10, 14] reverse NN queries [21, 27] constrained k NN queries [13] and k NN join queries [17] In this paper, we focus on simple k NN ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. T. Lu. Spatial databases - accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

....queries in spatial databases refer to finding the spatial objects nearest to some given query points. NN queries are used in a wide range of applications, such as Geographic Information Systems (GIS) Computer Aided Design (CAD) computational biology, decision support, and pattern recognition [29]. NN queries in spatial databases can be classified into five major categories: simple k NN queries [2, 6, 8, 9, 17, 23, 24, 28] approximate k NN queries [3, 10, 14] reverse NN queries [22, 30] constrained k NN queries [13] and k NN join queries [18] In this paper, we focus on simple k NN ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. T. Lu. Spatial databases - accomplishments and research needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

....tank , and the third example uses a viewer based direction as its selection criterion. It is important for a spatial database management system to provide a mechanism for modeling and processing direction queries. Directional relationship is often modeled as a binary relationship between objects[2, 16, 4, 23, 1, 14, 5, 3, 17, 12]. Most of the previous work [22, 20, 13] on direction query processing has been focused on indexing mechanisms and processing strategies for queries involving absolute directions(e.g. North, East) A previous study[20] evaluated di erent indices for processing absolute direction queries, and ....

S. Shekhar, S. Ravada A.Fetterer, X.Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Trans. Knowledge and Data Eng., 11(1):45-55, 1999.

....analysis, voting irregularity, and severe weather prediction. This paper focuses on spatial outliers, i.e. observations which appear to be inconsistent with their neighborhoods. Detecting spatial outliers is useful in many applications of geographic information systems and spatial databases [22, 23]. These application domains include transportation, ecology, public safety, public health, climatology, and location based services. We model a spatial data set to be a collection of spatially referenced objects, such as houses, roads, and traffic sensors. Spatial objects have two distinct ....

S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

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S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

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S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases - Accomplishments and Research Needs. In IEEE Transactions on Knowledge and Data Engineering, Vol.11, No.1, pp.45-55, 1999

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S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and L. Chang-tien. Spatial databases - accomplishments and research needs. TKDE, 1999.

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S. Shekhar, S. Chawla, S. Ravada, et al. Spatial Databases - Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1):45--55, 1999.

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