Environmental information systems

data types have been shown to greatly enhance data security and to facilitate application programming. They can adapt to user requirements in a flexible manner by encapsulating data structures and operators of arbitrary complexity. Disadvantages of this approach include the duality of the connected programming paradigms: one always has to switch back and forth between the database-internal mode, which typically involves a nonprocedural language such as SQL, and the external procedures, which are usually written in a procedural programming language. Furthermore, the internal structure of the data type is lost for the outside application; there is no way to retrieve any structural information from the ADT. This is a problem in particular for the database query optimizer. Without special accommodation, it is impossible for the optimizer to obtain any information about the complexity of the ADT operators that are included in a given query [GG95]. 3.2.4 Spatial Query Languages As we saw in the previous sections, any serious attempt to manage spatial data in a relational database framework requires some significant extensions at the logical and the physical level. These kinds of extension need to be supported at the query language level as well. Besides an ability to deal with spatial data types and operators, this involves in particular concepts to support the interactive working mode that is typical for many GIS/EIS applications. Pointing to objects or drawing on the screen with the mouse are typical examples of these dynamic interactions. Further extensions at the user interface level include [Voi95]: the graphical display of query results, including legends and labels; the display of unrequested context to improve readability; and the possibility of stepwise refinement of the display (logical zooming). For many years, the database market has been dominated by a single query language: the Structured Query Language SQL. There has been a long discussion in the literature as to whether SQL is suitable for querying spatial

[1]  John L. McCarthy,et al.  Metadata Management for Large Statistical Databases , 1982, VLDB.

[2]  Thies Wittig,et al.  An Adaptable Architecture for River Quality Monitoring , 1995 .

[3]  Michael Stonebraker,et al.  The SEQUOIA 2000 Project , 1993, SSD.

[4]  Michael Stonebraker,et al.  Readings in Database Systems , 1988 .

[5]  H. Sagan.  Space-filling curves , 1994 .

[6]  Witold Litwin,et al.  Linear Hashing: A new Algorithm for Files and Tables Addressing , 1980, ICOD.

[7]  Keune Hartmut,et al.  Harmonization of environmental measurement , 1991 .

[8]  Robert B McMaster,et al.  Automated Line Generalization , 1987 .

[9]  Frederick W. Stoss.  Environment online: The greening of databases , 1991 .

[10]  Wolf-Fritz Riekert.  Extracting area objects from raster image data , 1993, IEEE Computer Graphics and Applications.

[11]  M. J. van Kreveld,et al.  Maintaining range trees in secondary memory. Part I: Partitions , 1987 .

[12]  Markus Schneider,et al.  Spatial Data Types for Database Systems , 1997, Lecture Notes in Computer Science.

[13]  Per-Åke Larson,et al.  Linear Hashing with Partial Expansions , 1980, VLDB.

[14]  Agn Es Voisard.  Mapgets: a Tool for Visualizing and Querying Geographic Information , 1995 .

[15]  Susan G. Stafford,et al.  Integration of scientific information management and environmental research , 1994 .

[16]  Stuart E. Madnick,et al.  A Metadata Approach to Resolving Semantic Conflicts , 2011, VLDB.

[17]  J. T. Robinson,et al.  The K-D-B-tree: a search structure for large multidimensional dynamic indexes , 1981, SIGMOD '81.

[18]  Bernd-Uwe Pagel,et al.  Towards an analysis of range query performance in spatial data structures , 1993, PODS '93.

[19]  Bernd-Uwe Pagel,et al.  The Transformation Technique for Spatial Objects Revisited , 1993, SSD.

[20]  Hans-Peter Kriegel,et al.  An Efficient Map Overlay Algorithm Based on Spatial Access Methods and Computational Geometry , 2000 .

[21]  Jack A. Orenstein.  Multidimensional Tries Used for Associative Searching , 1982, Inf. Process. Lett..

[22]  Werner Pillmann,et al.  Distributed Environmental Data Compendia , 1992, IFIP Congress.

[23]  Claudia Bauzer Medeiros,et al.  Databases for GIS , 1994, SGMD.

[24]  Franz Josef Radermacher,et al.  The Importance of Metaknowledge for Environmental Information Systems , 1991, SSD.

[25]  Hanan Samet,et al.  The Design and Analysis of Spatial Data Structures , 1989 .

[26]  Andreas Buja,et al.  Xgobi: Interactive Dynamic Graphics In The X Window System With A Link To S , 1991 .

[27]  Inge Henning,et al.  Management of Distributed and Heterogeneous Information Resources for Environmental Administrations , 1997 .

[28]  Richard R. Muntz,et al.  OASIS: an open architecture scientific information system , 1996, Proceedings RIDE '96. Sixth International Workshop on Research Issues in Data Engineering.

[29]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[30]  Alfons Kemper,et al.  An analysis of geometric modeling in database systems , 1987, CSUR.

[31]  K. S. Shea,et al.  Cartographic generalization in a digital environment: a framework for implementation in a geographic information system , 1988 .

[32]  Aris M. Ouksel.  The interpolation-based grid file , 1985, PODS '85.

[33]  Peter Widmayer,et al.  Datenstrukturen für Geodatenbanken , 1991, Entwicklungstendenzen bei Datenbanksystemen.

[34]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[35]  David J. Maguire,et al.  Geographical information systems : principles and applications , 1991 .

[36]  Hanan Samet,et al.  Applications of spatial data structures , 1989 .

[37]  H. V. Jagadish.  Spatial search with polyhedra , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[38]  Knut Scheuer.  Knowledge-based interpretation of gas chromatographic data , 1993 .

[39]  Rudolf Kruse,et al.  Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence , 1995, International Centre for Mechanical Sciences.

[40]  Jesslyn F. Brown,et al.  Development of a land-cover characteristics database for the conterminous U.S. , 1991 .

[41]  Theresa-Marie Rhyne,et al.  Going virtual with geographic information and scientific visualization , 1997 .

[42]  G. Langran.  Time in Geographic Information Systems , 1990 .

[43]  H. V. Jagadzsh.  Linear Clustering of Objects with Multiple Attributes , 1998 .

[44]  David Mckeown,et al.  The Role of Artificial Intelligence in the Integration of Remotely Sensed Data with Geographic Information Systems , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Joachim Wiesel,et al.  A federation architecture for an environmental information system incorporating GIS, the World Wide Web, and CORBA , 1996 .

[46]  Nick Roussopoulos,et al.  Direct spatial search on pictorial databases using packed R-trees , 1985, SIGMOD Conference.

[47]  Shashi Shekhar,et al.  Data models in geographic information systems , 1997, CACM.

[48]  Heinz Schweppe,et al.  Abstraction and Decomposition in Interoperable GIS , 1998, Int. J. Geogr. Inf. Sci..

[49]  Michael Stonebraker,et al.  Segment indexes: dynamic indexing techniques for multi-dimensional interval data , 1991, SIGMOD '91.

[50]  Ulrich Steger.  Umweltmanagement : Erfahrungen und Instrumente einer Umweltorientierten Unternehmensstrategie , 1993 .

[51]  Doron Rotem.  Spatial join indices , 1991, [1991] Proceedings. Seventh International Conference on Data Engineering.

[52]  Scott Morehouse.  Arc/info: a geo-relational model for spatial information , 1985 .

[53]  Hans-Peter Kriegel,et al.  Multidimensional quantile hashing is very efficient for nonuniform distributions , 1989, Inf. Sci..

[54]  Bernd-Uwe Pagel,et al.  Window query-optimal clustering of spatial objects , 1995, PODS.

[55]  Sigbert Klinke,et al.  Spatial Data Analysis in the Dynamically Linked ArcView/XGobi/XploRe Environment , 1997 .