An index method for continuous moving objects

There are current, historical and future information about continuously moving spatio-temporal objects. And there are correspondingly spatio-temporal indexes for current, past and future querying. Among the various types of spatio-temporal access methods, no one can support historical and future information querying. The Time Parameterized R-tree(TPR-tree) employs the idea of parametric bounding rectangles in the R-tree. It can effectively support predictive querying to continuously moving objects. Unfortunately, TPR-tree can not used to historical querying. This paper presents a partial-persistence method in order to extend TPR-tree for querying past information of moving objects. In this method, several TPR-trees will be created for more effectively predictive querying, because TPR-tree has a time horizon limit for predictive querying. Further more, a B-tree will be used to index time dimension. Since the partial-persistence method brings about huge storage space using, this paper also discusses some methods on how to reduce storage space. Finally, this paper presents an extensive experimental study for the proposed method and gives some interesting directions for future work.     

A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and nonspatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

P systems with array objects and array rewriting rules

Array P systems were introduced by Paun Gh. which is linking the two areas of membrane computing and picture grammars. Puzzle grammars were introduced by us for generating connected picture arrays in the two-dimensional plane, motivated by the problem of tiling the plane. On the other hand, incorporating into arrays the developmental type of generation used in the well-known biologically motivated L systems, Siromoney and Siromoney proposed a very general rectangular array generating model, called extended controlled tabled L array system (ECTLAS). In this paper we introduce two variations of the array P system, called BPG array P system and parallel array P system. The former has in the regions array objects and basic puzzle grammar rules (BPG), which are a specific kind of puzzle grammar rules. In the latter, the regions have rectangular array objects and tables of context-free rules. We examine these two types of P systems for their array generative power.     

P systems with array objects and array rewriting rules

Array P systems were introduced by Paun Gh. which is linking the two areas of membrane computing and picture grammars. Puzzle grammars were introduced by us for generating connected picture arrays in the two-dimensional plane, motivated by the problem of tiling the plane. On the other hand, incorporating into arrays the developmental type of generation used in the well-known biologically motivated L systems, Siromoney and Siromoney proposed a very general rectangular array generating model, called extended controlled tabled L array system (ECTLAS). In this paper we introduce two variations of the array P system, called BPG array P system and parallel array P system. The former has in the regions array objects and basic puzzle grammar rules (BPG), which are a specific kind of puzzle grammar rules. In the latter, the regions have rectangular array objects and tables of context-free rules. We examine these two types of P systems for their array generative power.     

Indexing large moving objects from past to future with PCFI

In moving object database, the moving objects' current position must be kept in memory, also to the trajectory, in some case, as same as the future. But the current existing indexes such as SEB tree, SETI tree, 2+3R tree, 2 3RT tree and etc. can only provide the capability for past and current query, and the TPR Tree, TPR * Tree and etc. can only provide the capability for current and future query. None of them can provide a strategy for indexing the past, current and also the future information of moving objects. In this paper, we propose the past current future Index (PCFI Index) to index the past, current & future information of the moving objects. It is the combination of SETI tree and TPR * tree, the SETI liking index is used for indexing the historical trajectory segments except the front line structure, and the moving objects' current positions, velocities are indexed via the in memory frontline structure which mainly implemented with TPR * tree. Considering the large update operations on TPR tree of large population, a hash table considering cache sensitivity is also introduced. It works with the frontline part, leading a bottom up update of the tree. The performance analysis proves that the PCFI index can handle most of the query efficiently and provides a uniform solution for the trajectory query, time slice query, internal query and moving query.     

Are topological relations dependent on the shape of spatial objects?

Topology is normally considered as independent of shape of spatial objects. This may not necessarily be true in describing relations between spatial objects in GIS. In this paper, we present proof that the topological relations between spatial objects are dependent on the shape of spatial objects. That is, that the topological relations of non-convex sets cannot be deformed to the topological relations of convex sets. The significant theoretical value of this paper is on its findings that topology of spatial objects is shape dependent. This indicates that when we want to describe topological relations between spatial objects in GIS, both topology and the shape of objects need to be considered. As a result, spatial data modeling, query and analysis based on the existing understanding of topology of spatial objects may need re-assessed.     

0957 + 561 A, B: twin quasistellar objects or gravitational lens?

0957 + 561 A, B are two QSOs of mag 17 with 5.7 arc s separation at redshift 1.405. Their spectra leave little doubt that they are associated. Difficulties arise in describing them as two distinct objects and the possibility that they are two images of the same object formed by a gravitational lens is discussed.