Performance comparison of point and spatial access methods

In the past few years a large number of multidimensional point access methods, also called multiattribute index structures, has been suggested, all of them claiming good performance. Since no performance comparison of these structures under arbitrary (strongly correlated nonuniform, short "ugly") data distributions and under various types of queries has been performed, database researchers and designers were hesitant to use any of these new point access methods. As shown in a recent paper, such point access methods are not only important in traditional database applications. In new applications such as CAD/CIM and geographic or environmental information systems, access methods for spatial objects are needed. As recently shown such access methods are based on point access methods in terms of functionality and performance. Our performance comparison naturally consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in part I I spatial access methods for rectangles will be compared. In part I we present a survey and classification of existing point access methods. Then we carefully select the following four methods for implementation and performance comparison under seven different data files (distributions) and various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is robust under ugly data and queries. In part I I we compare spatial access methods for rectangles. After presenting a survey and classification of existing spatial access methods we carefully selected the following four methods for implementation and performance comparison under six different data files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree. This comparison is a first step towards a standardized testbed or benchmark. We offer our data and query files to each designer of a new point or spatial access method such that he can run his implementation in our testbed

Clozapine and olanzapine, but not haloperidol, suppress serotonin efflux in the medial prefrontal cortex elicited by phencyclidine and ketamine

N-methyl-D-aspartate (NMDA) receptor antagonists such as phencyclidine (PCP) and ketamine can evoke psychotic symptoms in normal individuals and schizophrenic patients. Here, we have examined the effects of PCP (5 mg/kg) and ketamine (25 mg/kg) on the efflux of serotonin (5-HT) in the medial prefrontal cortex (mPFC) and their possible blockade by the antipsychotics, clozapine, olanzapine and haloperidol, as well as ritanserin (5-HT2A/2C receptor antagonist) and prazosin (alpha1-adrenoceptor antagonist). The systemic administration, but not the local perfusion, of the two NMDA receptor antagonists markedly increased the efflux of 5-HT in the mPFC. The atypical antipsychotics clozapine (1 mg/kg) and olanzapine (1 mg/kg), and prazosin (0.3 mg/kg), but not the classical antipsychotic haloperidol (1 mg/kg), reversed the PCP- and ketamine-induced increase in 5-HT efflux. Ritanserin (5 mg/kg) was able to reverse only the effect of PCP. These findings indicate that an increased serotonergic transmission in the mPFC is a functional consequence of NMDA receptor hypofunction and this effect is blocked by atypical antipsychotic drugs.Peer reviewe

A general scheme for some deterministically parsable grammars and their strong equivalents

In the past years there have been many attempts to fill in the gap between the classes of LL(k) and LR(k) grammars with new classes of deterministically parsable grammars. Almost always the introduction of a new class was accompanied by a parsing method and/or a grammatical transformation fitting the following scheme. If parsers were at the centre of the investigation the new method used to be designed to possess certain advantages with respect to already existing ones. As far as transformations were concerned the intention was to produce methods of transforming grammars into "more easily" parsable ones. The problem of finding classes of context-free grammars which can be transformed to LL(k) grammars has received much attention. Parsing strategies and associated classes of grammars generating LL(k) languages have been extensively studied. An equally interesting class of grammars is the class of strict deterministic grammars, a subclass of the LR(O) grammars with elegant theoretical properties. Generalizations of this concept have been introduced by Friede and Pittl. The purpose of this paper is to show how the above mentioned classes of grammars can be dealt with within a general framework

Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels

Many graph datasets are labelled with discrete and numeric attributes. Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. We explore the relationship between graph structure and the distribution of attribute values and propose an outlier-detection step, which is used as a constraint during substructure discovery. By pruning anomalous vertices and edges, more weight is given to the most descriptive substructures. Our method is applicable to multi-dimensional numeric attributes; we outline how it can be extended for high-dimensional data. We support our findings with experiments on transaction graphs and single large graphs from the domains of physical building security and digital forensics, measuring the effect on runtime, memory requirements and coverage of discovered patterns, relative to the unconstrained approach