25,831 research outputs found
Enabling Adaptive Grid Scheduling and Resource Management
Wider adoption of the Grid concept has led to an increasing amount of federated
computational, storage and visualisation resources being available to scientists and
researchers. Distributed and heterogeneous nature of these resources renders most of the
legacy cluster monitoring and management approaches inappropriate, and poses new
challenges in workflow scheduling on such systems. Effective resource utilisation monitoring
and highly granular yet adaptive measurements are prerequisites for a more efficient Grid
scheduler. We present a suite of measurement applications able to monitor per-process
resource utilisation, and a customisable tool for emulating observed utilisation models. We
also outline our future work on a predictive and probabilistic Grid scheduler. The research is
undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising
Grid Resource Management) in cooperation with BT
Indexing Metric Spaces for Exact Similarity Search
With the continued digitalization of societal processes, we are seeing an
explosion in available data. This is referred to as big data. In a research
setting, three aspects of the data are often viewed as the main sources of
challenges when attempting to enable value creation from big data: volume,
velocity and variety. Many studies address volume or velocity, while much fewer
studies concern the variety. Metric space is ideal for addressing variety
because it can accommodate any type of data as long as its associated distance
notion satisfies the triangle inequality. To accelerate search in metric space,
a collection of indexing techniques for metric data have been proposed.
However, existing surveys each offers only a narrow coverage, and no
comprehensive empirical study of those techniques exists. We offer a survey of
all the existing metric indexes that can support exact similarity search, by i)
summarizing all the existing partitioning, pruning and validation techniques
used for metric indexes, ii) providing the time and storage complexity analysis
on the index construction, and iii) report on a comprehensive empirical
comparison of their similarity query processing performance. Here, empirical
comparisons are used to evaluate the index performance during search as it is
hard to see the complexity analysis differences on the similarity query
processing and the query performance depends on the pruning and validation
abilities related to the data distribution. This article aims at revealing
different strengths and weaknesses of different indexing techniques in order to
offer guidance on selecting an appropriate indexing technique for a given
setting, and directing the future research for metric indexes
An Efficient Algorithm for Bulk-Loading xBR+ -trees
A major part of the interface to a database is made up of the queries that can be addressed to this database and answered (processed) in an efficient way, contributing to the quality of the developed software. Efficiently processed spatial queries constitute a fundamental part of the interface to spatial databases due to the wide area of applications that may address such queries, like geographical information systems (GIS), location-based services, computer visualization, automated mapping, facilities management, etc. Another important capability of the interface to a spatial database is to offer the creation of efficient index structures to speed up spatial query processing. The xBR + -tree is a balanced disk-resident quadtree-based index structure for point data, which is very efficient for processing such queries. Bulk-loading refers to the process of creating an index from scratch, when the dataset to be indexed is available beforehand, instead of creating the index gradually (and more slowly), when the dataset elements are inserted one-by-one. In this paper, we present an algorithm for bulk-loading xBR + -trees for big datasets residing on disk, using a limited amount of main memory. The resulting tree is not only built fast, but exhibits high performance in processing a broad range of spatial queries, where one or two datasets are involved. To justify these characteristics, using real and artificial datasets of various cardinalities, first, we present an experimental comparison of this algorithm vs. a previous version of the same algorithm and STR, a popular algorithm of bulk-loading R-trees, regarding tree creation time and the characteristics of the trees created, and second, we experimentally compare the query efficiency of bulk-loaded xBR + -trees vs. bulk-loaded R-trees, regarding I/O and execution time. Thus, this paper contributes to the implementation of spatial database interfaces and the efficient storage organization for big spatial data management
Large Spatial Database Indexing with aX-tree
Spatial databases are optimized for the management of data stored based on their geometric space. Researchers through high degree scalability have proposed several spatial indexing structures towards this effect. Among these indexing structures is the X-tree. The existing X-trees and its variants are designed for dynamic environment, with the capability for handling insertions and deletions. Notwithstanding, the X-tree degrades on retrieval performance as dimensionality increases and brings about poor worst-case performance than sequential scan. We propose a new X-tree packing techniques for static spatial databases which performs better in space utilization through cautious packing. This new improved structure yields two basic advantage: It reduces the space overhead of the index and produces a better response time, because the aX-tree has a higher fan-out and so the tree always ends up shorter. New model for super-node construction and effective method for optimal packing using an improved str bulk-loading technique is proposed. The study reveals that proposed system performs better than many existing spatial indexing structure
QUASII: QUery-Aware Spatial Incremental Index.
With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes
Efficient bulk-loading methods for temporal and multidimensional index structures
Nahezu alle naturwissenschaftlichen Bereiche profitieren von neuesten Analyse- und Verarbeitungsmethoden fĂĽr groĂźe Datenmengen. Diese Verfahren setzten eine effiziente Verarbeitung von geo- und zeitbezogenen Daten voraus, da die Zeit und die Position wichtige Attribute vieler Daten
sind. Die effiziente Anfrageverarbeitung wird insbesondere durch den Einsatz von Indexstrukturen
ermöglicht. Im Fokus dieser Arbeit liegen zwei Indexstrukturen: Multiversion B-Baum
(MVBT) und R-Baum. Die erste Struktur wird fĂĽr die Verwaltung von zeitbehafteten Daten,
die zweite fĂĽr die Indexierung von mehrdimensionalen Rechteckdaten eingesetzt.
Ständig- und schnellwachsendes Datenvolumen stellt eine große Herausforderung an die Informatik
dar. Der Aufbau und das Aktualisieren von Indexen mit herkömmlichen Methoden (Datensatz
fĂĽr Datensatz) ist nicht mehr effizient. Um zeitnahe und kosteneffiziente Datenverarbeitung
zu ermöglichen, werden Verfahren zum schnellen Laden von Indexstrukturen dringend benötigt.
Im ersten Teil der Arbeit widmen wir uns der Frage, ob es ein Verfahren fĂĽr das Laden von MVBT
existiert, das die gleiche I/O-Komplexität wie das externe Sortieren besitz. Bis jetzt blieb diese
Frage unbeantwortet. In dieser Arbeit haben wir eine neue Kostruktionsmethode entwickelt und
haben gezeigt, dass diese gleiche Zeitkomplexität wie das externe Sortieren besitzt. Dabei haben
wir zwei algorithmische Techniken eingesetzt: Gewichts-Balancierung und Puffer-Bäume. Unsere
Experimenten zeigen, dass das Resultat nicht nur theoretischer Bedeutung ist.
Im zweiten Teil der Arbeit beschäftigen wir uns mit der Frage, ob und wie statistische Informationen
über Geo-Anfragen ausgenutzt werden können, um die Anfrageperformanz von R-Bäumen zu
verbessern. Unsere neue Methode verwendet Informationen wie Seitenverhältnis und Seitenlängen
eines repräsentativen Anfragerechtecks, um einen guten R-Baum bezüglich eines häufig eingesetzten
Kostenmodells aufzubauen. Falls diese Informationen nicht verfĂĽgbar sind, optimieren
wir R-Bäume bezüglich der Summe der Volumina von minimal umgebenden Rechtecken der Blattknoten.
Da das Problem des Aufbaus von optimalen R-Bäumen bezüglich dieses Kostenmaßes
NP-hart ist, führen wir zunächst das Problem auf ein eindimensionales Partitionierungsproblem
zurück, indem wir die Daten bezüglich optimierte raumfüllende Kurven sortieren. Dann lösen
wir dieses Problem durch Einsatz vom dynamischen Programmieren. Die I/O-Komplexität des
Verfahrens ist gleich der von externem Sortieren, da die I/O-Laufzeit der Methode durch die
Laufzeit des Sortierens dominiert wird.
Im letzten Teil der Arbeit haben wir die entwickelten Partitionierungsvefahren fĂĽr den Aufbau
von Geo-Histogrammen eingesetzt, da diese ähnlich zu R-Bäumen eine disjunkte Partitionierung
des Raums erzeugen. Ergebnisse von intensiven Experimenten zeigen, dass sich unter Verwendung
von neuen Partitionierungstechniken sowohl R-Bäume mit besserer Anfrageperformanz als
auch Geo-Histogrammen mit besserer Schätzqualität im Vergleich zu Konkurrenzverfahren generieren
lassen
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