Spatial databases-accomplishments and research needs

Shekhar, Shashi; Chawla, Sanjay; Ravada, Siva; Fetterer, Andrew; Li, Xuan; Lu, Chang-Tien

doi:10.1109/69.755614

Cited by 130 publications

(69 citation statements)

References 25 publications

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“…Data oriented DS tools involve no models, but enable good understanding through segmentation, slicing, dicing, drilling down, rolling-up and other operations. The proposed architectural framework of our Geo-DSS does not migrate from the general theoretical approach to decision-making, which follows the Simon's model of decision-making but at the same time it takes into account the suggestions of Frank Kriwaczek et al [15] . While one of the significant DSS architecture proposal by E.G.…”

Section: Integration Of Sparm With P-tree and T-tree Into Geo-dssmentioning

confidence: 99%

Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System

Vyas¹,

Sharma²,

Birlinghoven³

2007

J. of Computer Science

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Geographical Decision Support System (Geo-DSS) is a demanding field, since enormous amount of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although some efforts were made to combine spatial mining with Spatial Decision Support System but mostly researchers for spatial database are using a popular data mining approach-Apriori based association rule mining. There are two major limitations in existing approaches; the biggest being, that in a typical Apriori based spatial association the same records are required to be scanned again and again to find out the frequent sets. This becomes cumbersome, as spatial data is already known to be large in size. As far as sparse data is concerned, an Apriori based spatial association rule may even be considered but when there is dense data there were other approaches giving better performance. Researchers discuss only the positive spatial association rules; they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. As this approach makes computation faster, it is thus better candidate for integration into Geo-DSS architectural framework. We have tried to design a particular Decision support system using spatial positive and negative association rule with efficient PTree and T-Tree.

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Section: Integration Of Sparm With P-tree and T-tree Into Geo-dssmentioning

confidence: 99%

Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System

Vyas¹,

Sharma²,

Birlinghoven³

2007

J. of Computer Science

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“…SQL3 has facilities for the use of abstract data types (ADTs) in its speci®cation [25]. The OpenGIS consortium (OpenGIS inc. [40]) also proposed a speci®cation for incorporating``geo-spatial ADTs'' into SQL92 [46]. Many of the features used in this speci®cation were proposed earlier in the SpatialSQL speci®cation [17].…”

Section: Textual Query Languagesmentioning

confidence: 99%

“…The OpenGIS [40] have speci®ed a number of operators for the geometry class. These are summarised in table A.1, compiled by Shekar et al [46] and Clementini and DiFelice [13]. A spatial relationship that is not de®ned in the OpenGIS speci®cation [40] but has been de®ned in a number of papers [1], [12], [14], [19] is a directional relation.…”

Section: Appendixmentioning

confidence: 99%

A Filter Flow Visual Querying Language and Interface for Spatial Databases

et al. 2004

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In this paper a visual approach to querying in spatial databases is presented. A ®lter¯ow methodology is used to consistently express different types of queries in these systems. Filters are used to represent operations on the database and pictorial icons are used throughout the language for ®lters, operators and spatial relations. Different granularities of the relations are presented in a hierarchical fashion for spatial constraints. The language framework and functions are described and examples are used to demonstrate its capabilities in representing different levels of queries, including spatial joins and composite spatial joins. Here, the primary focus is on the query language itself but an overview of the implemented interface of the language is also provided.

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“…This paper specifically focuses on spatial databases, an important class of applications for the mobile devices. In general, Spatial Database Management Systems (SDBMS) [28] have found widespread adoption in numerous areas including Geographical Information Systems (GIS), Image Processing, Military Planning and Logistics, Computer Aided Design (CAD), Multimedia Systems, and Medical Database Systems. SDBMS are important for mobile computing, with several possible applications in this domain.…”

Section: Introductionmentioning

confidence: 99%

“…SDBMS design and implementation is a difficult problem [28] even on conventional systems because it has to deal with multidimensional data. Data objects have varying sizes associated with them, and spatial operations are in general much more complex than standard relational operators.…”

Section: Introductionmentioning

confidence: 99%

Energy and performance considerations in work partitioning for mobile spatial queries

Gurumurthi

Sivasubramaniam³

et al.

Proceedings International Parallel and Distributed Processing Symposium

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A seamless infrastructure for information access and data processing is the backbone for the successful development and deployment of the envisioned ubiquitous/mobile applications of the near future. This infrastructure should allow a user to access and process information from anywhere, whether it be from a desktop or on the road. Further, the user should not be able to perceive any noticeable differences in the performance across these different environments. These goals become particularly challenging when constrained by the resource availability on many mobile devices, in terms of the computational power, storage capacities, wireless connectivity and battery energy. With spatial data and location-aware applications widely recognized as being significant beneficiaries of mobile computing, this paper examines an important topic with respect to spatial query processing from the resource-constrained perspective. Specifically, when faced with the task of answering different location-based queries on spatial data from a mobile device, this paper investigates the benefits of partitioning the work between the resource-constrained mobile device (client) and a resource-rich server, that are connected by a wireless network, for energy and performance savings.This study considers two different scenarios, one where all the spatial data and associated index can fit in client memory and the other where client memory is insufficient. For each of these scenarios, several work partitioning schemes are identified. The execution of spatial queries using a packed R-tree index structure is modeled for each of these schemes using a cycle accurate performance and energy simulator, that captures the wireless communication as well. Three different spatial queries on different datasets are studied for these schemes by varying several hardware and system software parameters. The results show that the type and nature of queries has an important influence on the choice of work partitioning schemes, as does the dataset, relative speed of the mobile client and server CPUs, and the wireless network bandwidth. It is found that work partitioning is a good choice from both energy and performance perspectives in several situations, and these perspectives can have differential effects on the relative benefits of work-partitioning techniques.

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Spatial databases-accomplishments and research needs

Cited by 130 publications

References 25 publications

Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System

Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System

A Filter Flow Visual Querying Language and Interface for Spatial Databases

Energy and performance considerations in work partitioning for mobile spatial queries

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