AS urvey on Multidimensional Methods

2000

Storing multidimensional data in databases is an important topic both in the industrial and scientific database communities. Arrays are offered as a multidimensional data structure by most programming languages. Conventional database systems, however, do not support arrays of arbitrary dimensionality and base type. RasDaMan is a DBMS integrating arrays as a first class data type offering both a declarative query language and a specialised storage structure for arrays.

ACM Transactions on Database Systems, 2005

Multi-dimensional data points can be mapped to one-dimensional space to exploit single dimensional indexing structures such as the B + -tree. In this paper we present a Generalized structure for data Mapping and query Processing (GiMP), which supports extensible mapping methods and query processing. GiMP can be easily customized to behave like many competent indexing mechanisms for multi-dimensional indexing, such as the UB-Tree, the Pyramid technique, the iMinMax, and the iDistance. Besides being an extendible indexing structure, GiMP also serves as a framework to study the characteristics of the mapping and hence the efficiency of the indexing scheme. Specifically, we introduce a metric called mapping redundancy to characterize the efficiency of a mapping method in terms of disk page accesses and analyze its behavior for point, range and kNN queries. We also address the fundamental problem of whether an efficient mapping exists and how to define such a mapping for a given data set. · mappability problem. We discuss the circumstances under which a one-to-one mapping exists and how such a mapping can be defined. In order to demonstrate the applicability of our theory, we developed an indexing scheme called the Z*-curve and implemented it on GiMP. Experiments show that for the targeting workloads, the Z*-curve is more efficient than the existing methods.

2012

Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects’ geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “steak, spaghetti, brandy” all at the same time. Currently, the best solution to such queries is based on the IR2-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR2-tree in query response time significantly, often by a factor of orders of magnitude.

2012

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial Geoinformatica

Sovremennye Informacionnye Tehnologii i IT-obrazovanie, 2018

We present a new dynamic index structure for multidimensional data. The considered index structure is based on an extended grid file concept. Strengths and weaknesses of the grid files were analyzed. Based on that analysis we proposed to strengthen the concept of grid files by considering their stripes as linear hash tables, introducing the concept of chunk and representing the grid file structure as a graph. As a result we significantly reduced the amount of disk operations. Efficient algorithms for storage and access of index directory are proposed, in order to minimize memory usage and lookup operations complexities. Estimations of complexities for these algorithms are presented. A comparison of our approach to support effective grid file structure with other known approaches is presented. This comparison shows effectiveness of suggested metadata storage environment. An estimation of directory size is presented. A prototype to support of our grid file concept has been created and...

2001

Emerging database applications require the use of new indexing structures beyond B-trees and R-trees. Examples are the k-D tree, the trie, the quadtree, and their variants. They are often proposed as supporting structures in data mining, GIS, and CAD/CAM applications. A common feature of all these indexes is that they recursively divide the space into partitions. A new extensible index structure, termed SP-GiST, is presented that supports this class of data structures, mainly the class of space partitioning unbalanced trees. Simple method implementations are provided that demonstrate how SP-GiST can behave as a k-D tree, a trie, a quadtree, or any of their variants. Issues related to clustering tree nodes into pages as well as concurrency control for SP-GiST are addressed. A dynamic minimum-height clustering technique is applied to minimize disk accesses and to make using such trees in database systems possible and efficient. A prototype implementation of SP-GiST is presented as well as performance studies of the various SP-GiST's tuning parameters.

Technologies, Techniques and Trends, 2005

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods, and optimization rules. Although available extensible indexing frameworks provide a gateway for seamless integration of spatial access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the spatial access method itself. An internal enhancement of the database kernel is usually not an option for database developers. The embedding of a custom block-oriented index structure into concurrency control, recovery services and buffer management would cause extensive implementation efforts and maintenance cost, at the risk of weakening the reliability of the entire system. The server stability can be preserved by delegating index operations to an external process, but this approach induces severe performance bottlenecks due to context switches and inter-process communication. Therefore, we present the paradigm of object-relational spatial access methods that perfectly fits to the common relational data model and is highly compatible with the extensible indexing frameworks of existing object-relational database systems allowing the user to define application-specific access methods.

1991

The applicat.ion of st~andard qurry processing al111 01)timizat,ion techniques in the conl ext. of an in t.egril.t fvl spatial dat,abase environment is discussed. In adtli-Con, some new processing and optimization strat,egies are shown to emerge from the nature of the underlying architecture used for the integration of spatial data. Other strategies are presented t,liat, RI'C applicat,iondependent,. They are related t.o tile diITf,rctlt possible Ianplementationa of spatial operat,ors wli~rc~ each one is pr+ ferrable under certain conditions. The underlying spatial database architecture that is used is called SAND (denot#ing Spatial And Non-spatial Data). SAND is a dual spatial database archit,ecture in which tile ohjeck' spat,ial information is stored in sepi3rat.e spalial da1.a structures and their non-spat#ial inrormation is stored in dat.abase relat,ions while nlaint,aitling apl)roprintc links between the spatrial and non-spatial con1poncnt.s of'eacll object. SAND provides an equal opportunit,y for hot#h the spatial and non-spatial components of the dat,a to participate in query processing and optimizabion. Aside from the application-dependent opt,imization st,rakgic>s discussed in the paper, these tprhniqlles are not Iill]ited t.o spa.tial data. They can he estcnd~tl to deal wit II multi-media databases as well.