872 resultados para Spatial Database Systems
Resumo:
The proliferation of inexpensive workstations and networks has created a new era in distributed computing. At the same time, non-traditional applications such as computer-aided design (CAD), computer-aided software engineering (CASE), geographic-information systems (GIS), and office-information systems (OIS) have placed increased demands for high-performance transaction processing on database systems. The combination of these factors gives rise to significant challenges in the design of modern database systems. In this thesis, we propose novel techniques whose aim is to improve the performance and scalability of these new database systems. These techniques exploit client resources through client-based transaction management. Client-based transaction management is realized by providing logging facilities locally even when data is shared in a global environment. This thesis presents several recovery algorithms which utilize client disks for storing recovery related information (i.e., log records). Our algorithms work with both coarse and fine-granularity locking and they do not require the merging of client logs at any time. Moreover, our algorithms support fine-granularity locking with multiple clients permitted to concurrently update different portions of the same database page. The database state is recovered correctly when there is a complex crash as well as when the updates performed by different clients on a page are not present on the disk version of the page, even though some of the updating transactions have committed. This thesis also presents the implementation of the proposed algorithms in a memory-mapped storage manager as well as a detailed performance study of these algorithms using the OO1 database benchmark. The performance results show that client-based logging is superior to traditional server-based logging. This is because client-based logging is an effective way to reduce dependencies on server CPU and disk resources and, thus, prevents the server from becoming a performance bottleneck as quickly when the number of clients accessing the database increases.
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This report summarizes the technical presentations and discussions that took place during RTDB'96: the First International Workshop on Real-Time Databases, which was held on March 7 and 8, 1996 in Newport Beach, California. The main goals of this project were to (1) review recent advances in real-time database systems research, (2) to promote interaction among real-time database researchers and practitioners, and (3) to evaluate the maturity and directions of real-time database technology.
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This paper presents a framework for a telecommunications interface which allows data from sensors embedded in Smart Grid applications to reliably archive data in an appropriate time-series database. The challenge in doing so is two-fold, firstly the various formats in which sensor data is represented, secondly the problems of telecoms reliability. A prototype of the authors' framework is detailed which showcases the main features of the framework in a case study featuring Phasor Measurement Units (PMU) as the application. Useful analysis of PMU data is achieved whenever data from multiple locations can be compared on a common time axis. The prototype developed highlights its reliability, extensibility and adoptability; features which are largely deferred from industry standards for data representation to proprietary database solutions. The open source framework presented provides link reliability for any type of Smart Grid sensor and is interoperable with existing proprietary database systems, and open database systems. The features of the authors' framework allow for researchers and developers to focus on the core of their real-time or historical analysis applications, rather than having to spend time interfacing with complex protocols.
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Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation.
On Implementing Joins, Aggregates and Universal Quantifier in Temporal Databases using SQL Standards
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A feasible way of implementing a temporal database is by mapping temporal data model onto a conventional data model followed by a commercial database management system. Even though extensions were proposed to standard SQL for supporting temporal databases, such proposals have not yet come across standardization processes. This paper attempts to implement database operators such as aggregates and universal quantifier for temporal databases, implemented on top of relational database systems, using currently available SQL standards.
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Die Auszeichnungssprache XML dient zur Annotation von Dokumenten und hat sich als Standard-Datenaustauschformat durchgesetzt. Dabei entsteht der Bedarf, XML-Dokumente nicht nur als reine Textdateien zu speichern und zu transferieren, sondern sie auch persistent in besser strukturierter Form abzulegen. Dies kann unter anderem in speziellen XML- oder relationalen Datenbanken geschehen. Relationale Datenbanken setzen dazu bisher auf zwei grundsätzlich verschiedene Verfahren: Die XML-Dokumente werden entweder unverändert als binäre oder Zeichenkettenobjekte gespeichert oder aber aufgespalten, sodass sie in herkömmlichen relationalen Tabellen normalisiert abgelegt werden können (so genanntes „Flachklopfen“ oder „Schreddern“ der hierarchischen Struktur). Diese Dissertation verfolgt einen neuen Ansatz, der einen Mittelweg zwischen den bisherigen Lösungen darstellt und die Möglichkeiten des weiterentwickelten SQL-Standards aufgreift. SQL:2003 definiert komplexe Struktur- und Kollektionstypen (Tupel, Felder, Listen, Mengen, Multimengen), die es erlauben, XML-Dokumente derart auf relationale Strukturen abzubilden, dass der hierarchische Aufbau erhalten bleibt. Dies bietet zwei Vorteile: Einerseits stehen bewährte Technologien, die aus dem Bereich der relationalen Datenbanken stammen, uneingeschränkt zur Verfügung. Andererseits lässt sich mit Hilfe der SQL:2003-Typen die inhärente Baumstruktur der XML-Dokumente bewahren, sodass es nicht erforderlich ist, diese im Bedarfsfall durch aufwendige Joins aus den meist normalisierten und auf mehrere Tabellen verteilten Tupeln zusammenzusetzen. In dieser Arbeit werden zunächst grundsätzliche Fragen zu passenden, effizienten Abbildungsformen von XML-Dokumenten auf SQL:2003-konforme Datentypen geklärt. Darauf aufbauend wird ein geeignetes, umkehrbares Umsetzungsverfahren entwickelt, das im Rahmen einer prototypischen Applikation implementiert und analysiert wird. Beim Entwurf des Abbildungsverfahrens wird besonderer Wert auf die Einsatzmöglichkeit in Verbindung mit einem existierenden, ausgereiften relationalen Datenbankmanagementsystem (DBMS) gelegt. Da die Unterstützung von SQL:2003 in den kommerziellen DBMS bisher nur unvollständig ist, muss untersucht werden, inwieweit sich die einzelnen Systeme für das zu implementierende Abbildungsverfahren eignen. Dabei stellt sich heraus, dass unter den betrachteten Produkten das DBMS IBM Informix die beste Unterstützung für komplexe Struktur- und Kollektionstypen bietet. Um die Leistungsfähigkeit des Verfahrens besser beurteilen zu können, nimmt die Arbeit Untersuchungen des nötigen Zeitbedarfs und des erforderlichen Arbeits- und Datenbankspeichers der Implementierung vor und bewertet die Ergebnisse.
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Building software for Web 2.0 and the Social Media world is non-trivial. It requires understanding how to create infrastructure that will survive at Web scale, meaning that it may have to deal with tens of millions of individual items of data, and cope with hits from hundreds of thousands of users every minute. It also requires you to build tools that will be part of a much larger ecosystem of software and application families. In this lecture we will look at how traditional relational database systems have tried to cope with the scale of Web 2.0, and explore the NoSQL movement that seeks to simplify data-storage and create ultra-swift data systems at the expense of immediate consistency. We will also look at the range of APIs, libraries and interoperability standards that are trying to make sense of the Social Media world, and ask what trends we might be seeing emerge.
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Some examples from the book. Connolly, T. M. and C. E. Begg (2005). Database systems : a practical approach to design, implementation, and management. Harlow, Essex, England ; New York, Addison-Wesley.
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Background: Sexual risk behaviors associated with poor information on sexuality have contributed to major public health problems in the area of sexual and reproductive health in teenagers and young adults in Colombia. Objective: To measure the perception of changes in sexual and reproductive risk behavior after the use of a teleconsultation service via mobile devices in a sample of young adults. Methods: A before and after observational study was designed, where a mobile application to inquire about sexual and reproductive health was developed. The perception of changes in sexual and reproductive health risk behaviors in a sample of young adults after the use of the application was measured using the validated survey “Family Health International (FHI) – Behavioral Surveillance Survey (BSS) – Survey for Adults between 15 to 40 Years”. Non-probabilistic convenience recruitment was undertaken through the study´s web page. Participants answered the survey online before and after the use of the mobile application for a six month period (intervention). For the inferential analysis, data was divided into three groups (dichotomous data, discrete quantitative data, and ordinal data), to compare the results of the questions between the first and the second survey. For all tests, a confidence interval of 95% was established. For dichotomous data, the Chi-squared test was used. For quantitative data, we used the Student’s t-test, and for ordinal data, the Mann-Whitney-Wilcoxon test. Results: A total of 257 subjects were registered in the study and met the selection criteria. The pre-intervention survey was answered by 232 subjects, and 127 completely answered the post-intervention survey, of which 54.3% did not use the application, leaving an effective population of 58 subjects for analysis. 53% (n=31) were female, and 47% (n=27) were male. The mean age was 21 years, ranging between 18 and 40 years. The differences between the answers on the first and the second survey were not statistically significant. The main risk behaviors identified in the population were homosexual relations, non-use of condoms, sexual relations with non-regular and commercial partners, the use of psychoactive substances, and ignorance about the symptoms of sexually transmitted diseases and HIV transmission. Conclusions: Although there were no differences between the pre- and post-intervention results, the study revealed different risk behaviors among the participating subjects. These findings highlight the importance of promoting educational strategies on this matter and the importance of providing patients with easily accessible tools with reliable health information.
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Changes in mature forest cover amount, composition, and configuration can be of significant consequence to wildlife populations. The response of wildlife to forest patterns is of concern to forest managers because it lies at the heart of such competing approaches to forest planning as aggregated vs. dispersed harvest block layouts. In this study, we developed a species assessment framework to evaluate the outcomes of forest management scenarios on biodiversity conservation objectives. Scenarios were assessed in the context of a broad range of forest structures and patterns that would be expected to occur under natural disturbance and succession processes. Spatial habitat models were used to predict the effects of varying degrees of mature forest cover amount, composition, and configuration on habitat occupancy for a set of 13 focal songbird species. We used a spatially explicit harvest scheduling program to model forest management options and simulate future forest conditions resulting from alternative forest management scenarios, and used a process-based fire-simulation model to simulate future forest conditions resulting from natural wildfire disturbance. Spatial pattern signatures were derived for both habitat occupancy and forest conditions, and these were placed in the context of the simulated range of natural variation. Strategic policy analyses were set in the context of current Ontario forest management policies. This included use of sequential time-restricted harvest blocks (created for Woodland caribou (Rangifer tarandus) conservation) and delayed harvest areas (created for American marten (Martes americana atrata) conservation). This approach increased the realism of the analysis, but reduced the generality of interpretations. We found that forest management options that create linear strips of old forest deviate the most from simulated natural patterns, and had the greatest negative effects on habitat occupancy, whereas policy options that specify deferment and timing of harvest for large blocks helped ensure the stable presence of an intact mature forest matrix over time. The management scenario that focused on maintaining compositional targets best supported biodiversity objectives by providing the composition patterns required by the 13 focal species, but this scenario may be improved by adding some broad-scale spatial objectives to better maintain large blocks of interior forest habitat through time.
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The acquisition and update of Geographic Information System (GIS) data are typically carried out using aerial or satellite imagery. Since new roads are usually linked to georeferenced pre-existing road network, the extraction of pre-existing road segments may provide good hypotheses for the updating process. This paper addresses the problem of extracting georeferenced roads from images and formulating hypotheses for the presence of new road segments. Our approach proceeds in three steps. First, salient points are identified and measured along roads from a map or GIS database by an operator or an automatic tool. These salient points are then projected onto the image-space and errors inherent in this process are calculated. In the second step, the georeferenced roads are extracted from the image using a dynamic programming (DP) algorithm. The projected salient points and corresponding error estimates are used as input for this extraction process. Finally, the road center axes extracted in the previous step are analyzed to identify potential new segments attached to the extracted, pre-existing one. This analysis is performed using a combination of edge-based and correlation-based algorithms. In this paper we present our approach and early implementation results.
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The present work begins with a review of the literature on bit selection methods for oil well drilling. A proposal for the structure and organization of a drilling database and a knowledge base, is described. Previous studies formed the principal elements in the process of selection of drills for proposed drilling. The procedure was implemented as a computer system for the selection of tricone bits. A drilling bit database for three different Brazilian sedimentary basins was obtained for several wells drilled, and knowledge was collected from drilling engineers from different fields both electronically and also by means of interviews. It can be concluded that the selection process showed good results based on tests, which were carried out.
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This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported. © 2003 Elsevier Ltd. All rights reserved.