496 research outputs found
10381 Summary and Abstracts Collection -- Robust Query Processing
Dagstuhl seminar 10381 on robust query processing (held 19.09.10 -
24.09.10) brought together a diverse set of researchers and practitioners
with a broad range of expertise for the purpose of fostering discussion
and collaboration regarding causes, opportunities, and solutions for
achieving robust query processing.
The seminar strove to build a unified view across
the loosely-coupled system components responsible for
the various stages of database query processing.
Participants were chosen for their experience with database
query processing and, where possible, their prior work in academic
research or in product development towards robustness in database query
processing.
In order to pave the way to motivate, measure, and protect future advances
in robust query processing, seminar 10381 focused on developing tests
for measuring the robustness of query processing.
In these proceedings, we first review the seminar topics, goals,
and results, then present abstracts or notes of some of the seminar break-out
sessions.
We also include, as an appendix,
the robust query processing reading list that
was collected and distributed to participants before the seminar began,
as well as summaries of a few of those papers that were
contributed by some participants
Ordering selection operators under partial ignorance
Optimising queries in real-world situations under imperfect conditions is still a problem that has not been fully solved. We consider finding the optimal order in which to execute a given set of selection operators under partial ignorance of their selectivities. The selectivities are modelled as intervals rather than exact values and we apply a concept from decision theory, the minimisation of the maximum regret, as a measure of optimality. The associated decision problem turns out to be NP-hard, which renders a brute-force approach to solving it impractical. Nevertheless, by investigating properties of the problem and identifying special cases which can be solved in polynomial time, we gain insight that we use to develop a novel heuristic for solving the general problem. We also evaluate minmax regret query optimisation experimentally, showing that it outperforms a currently employed strategy of optimisers that uses mean values for uncertain parameters
Analisis dan Perancangan Database Menggunakan Model Konseptual Data Warehouse Sistem Manajemen Transaksi Toko Online Haransaf
Penelitian ini bertujuan untuk merancang dan menganalisis kebutuhan basis data toko online Haransaf menggunakan konseptual data warehouse. Analisis kebutuhan basis data ini mengambil subjek penelitian toko haransaf yang bergerak di penjualan barang berupa baju khusus wanita. Owner toko dalam wawancara kesulitan dalam pembukuan barang pembelian stock dan pembukuan penjualan barang. Buku catatan dan file excel pembukuan yang sering tercampur dengan file lain sehingga sulit mencari data transaksi yang telah berlalu, oleh karna itu dibutuhkanlah sebuah sistem pengelolaan yang dapat medata transaksi yang ada di toko Haransaf mengenai pembelian dan penjualan barang. Langkah perancangan sistem manajemen toko ini melalui analisis perancangan basis data Melalui manajemen sitem tersebut pengguna khususnya owner toko dapat memperoleh kemudahan dalam mencari informasi, menyimpan informasi dan membuang informasi. Dalam perancangan database ini menggunakan model konseptual data warehouse yang menghasilkan star schema. atribut tabel yang didapat diperlukan pengkodean khusus untuk id owner, id karyawan, id produk, id costumer dan id supplier serta terdapat perbedaan antara skema star pembelian dan penjualan dalam hasil analisis. Perancangan basis data warehouse dapat dihasilkan data yang terstruktur dan terintegrasi sehingga bisa menjadi masukan bagi pemilik toko dalam proses pengambilan keputusa
Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications
MapReduce is a popular programming paradigm for developing large-scale,
data-intensive computation. Many frameworks that implement this paradigm have
recently been developed. To leverage these frameworks, however, developers must
become familiar with their APIs and rewrite existing code. Casper is a new tool
that automatically translates sequential Java programs into the MapReduce
paradigm. Casper identifies potential code fragments to rewrite and translates
them in two steps: (1) Casper uses program synthesis to search for a program
summary (i.e., a functional specification) of each code fragment. The summary
is expressed using a high-level intermediate language resembling the MapReduce
paradigm and verified to be semantically equivalent to the original using a
theorem prover. (2) Casper generates executable code from the summary, using
either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically
converting real-world, sequential Java benchmarks to MapReduce. The resulting
benchmarks perform up to 48.2x faster compared to the original.Comment: 12 pages, additional 4 pages of references and appendi
Allocation Strategies for Data-Oriented Architectures
Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms.
Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods
A scalable analysis framework for large-scale RDF data
With the growth of the Semantic Web, the availability of RDF datasets from multiple domains
as Linked Data has taken the corpora of this web to a terabyte-scale, and challenges
modern knowledge storage and discovery techniques. Research and engineering on RDF
data management systems is a very active area with many standalone systems being introduced.
However, as the size of RDF data increases, such single-machine approaches meet
performance bottlenecks, in terms of both data loading and querying, due to the limited
parallelism inherent to symmetric multi-threaded systems and the limited available system
I/O and system memory. Although several approaches for distributed RDF data processing
have been proposed, along with clustered versions of more traditional approaches, their
techniques are limited by the trade-off they exploit between loading complexity and query
efficiency in the presence of big RDF data. This thesis then, introduces a scalable analysis
framework for processing large-scale RDF data, which focuses on various techniques to
reduce inter-machine communication, computation and load-imbalancing so as to achieve
fast data loading and querying on distributed infrastructures.
The first part of this thesis focuses on the study of RDF store implementation and parallel
hashing on big data processing. (1) A system-level investigation of RDF store implementation
has been conducted on the basis of a comparative analysis of runtime characteristics
of a representative set of RDF stores. The detailed time cost and system consumption is
measured for data loading and querying so as to provide insight into different triple store
implementation as well as an understanding of performance differences between different
platforms. (2) A high-level structured parallel hashing approach over distributed memory is
proposed and theoretically analyzed. The detailed performance of hashing implementations
using different lock-free strategies has been characterized through extensive experiments,
thereby allowing system developers to make a more informed choice for the implementation
of their high-performance analytical data processing systems.
The second part of this thesis proposes three main techniques for fast processing of large
RDF data within the proposed framework. (1) A very efficient parallel dictionary encoding
algorithm, to avoid unnecessary disk-space consumption and reduce computational complexity of query execution. The presented implementation has achieved notable speedups
compared to the state-of-art method and also has achieved excellent scalability. (2) Several
novel parallel join algorithms, to efficiently handle skew over large data during query processing.
The approaches have achieved good load balancing and have been demonstrated
to be faster than the state-of-art techniques in both theoretical and experimental comparisons.
(3) A two-tier dynamic indexing approach for processing SPARQL queries has been
devised which keeps loading times low and decreases or in some instances removes intermachine
data movement for subsequent queries that contain the same graph patterns. The
results demonstrate that this design can load data at least an order of magnitude faster than
a clustered store operating in RAM while remaining within an interactive range for query
processing and even outperforms current systems for various queries
Arquitectura, técnicas y modelos para posibilitar la Ciencia de Datos en el Archivo de la Misión Gaia
Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leÃda el 26/05/2017.The massive amounts of data that the world produces every day pose new challenges to modern societies in terms of how to leverage their inherent value. Social networks, instant messaging, video, smart devices and scientific missions are just mere examples of the vast number of sources generating data every second. As the world becomes more and more digitalized, new needs arise for organizing, archiving, sharing, analyzing, visualizing and protecting the ever-increasing data sets, so that we can truly develop into a data-driven economy that reduces inefficiencies and increases sustainability, creating new business opportunities on the way. Traditional approaches for harnessing data are not suitable any more as they lack the means for scaling to the larger volumes in a timely and cost efficient manner. This has somehow changed with the advent of Internet companies like Google and Facebook, which have devised new ways of tackling this issue. However, the variety and complexity of the value chains in the private sector as well as the increasing demands and constraints in which the public one operates, needs an ongoing research that can yield newer strategies for dealing with data, facilitate the integration of providers and consumers of information, and guarantee a smooth and prompt transition when adopting these cutting-edge technological advances. This thesis aims at providing novel architectures and techniques that will help perform this transition towards Big Data in massive scientific archives. It highlights the common pitfalls that must be faced when embracing it and how to overcome them, especially when the data sets, their transformation pipelines and the tools used for the analysis are already present in the organizations. Furthermore, a new perspective for facilitating a smoother transition is laid out. It involves the usage of higher-level and use case specific frameworks and models, which will naturally bridge the gap between the technological and scientific domains. This alternative will effectively widen the possibilities of scientific archives and therefore will contribute to the reduction of the time to science. The research will be applied to the European Space Agency cornerstone mission Gaia, whose final data archive will represent a tremendous discovery potential. It will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), providing unprecedented position, parallax and proper motion measurements for about one billion stars. The successful exploitation of this data archive will depend to a large degree on the ability to offer the proper architecture, i.e. infrastructure and middleware, upon which scientists will be able to do exploration and modeling with this huge data set. In consequence, the approach taken needs to enable data fusion with other scientific archives, as this will produce the synergies leading to an increment in scientific outcome, both in volume and in quality. The set of novel techniques and frameworks presented in this work addresses these issues by contextualizing them with the data products that will be generated in the Gaia mission. All these considerations have led to the foundations of the architecture that will be leveraged by the Science Enabling Applications Work Package. Last but not least, the effectiveness of the proposed solution will be demonstrated through the implementation of some ambitious statistical problems that will require significant computational capabilities, and which will use Gaia-like simulated data (the first Gaia data release has recently taken place on September 14th, 2016). These ambitious problems will be referred to as the Grand Challenge, a somewhat grandiloquent name that consists in inferring a set of parameters from a probabilistic point of view for the Initial Mass Function (IMF) and Star Formation Rate (SFR) of a given set of stars (with a huge sample size), from noisy estimates of their masses and ages respectively. This will be achieved by using Hierarchical Bayesian Modeling (HBM). In principle, the HBM can incorporate stellar evolution models to infer the IMF and SFR directly, but in this first step presented in this thesis, we will start with a somewhat less ambitious goal: inferring the PDMF and PDAD. Moreover, the performance and scalability analyses carried out will also prove the suitability of the models for the large amounts of data that will be available in the Gaia data archive.Las grandes cantidades de datos que se producen en el mundo diariamente plantean nuevos retos a la sociedad en términos de cómo extraer su valor inherente. Las redes sociales, mensajerÃa instantánea, los dispositivos inteligentes y las misiones cientÃficas son meros ejemplos del gran número de fuentes generando datos en cada momento. Al mismo tiempo que el mundo se digitaliza cada vez más, aparecen nuevas necesidades para organizar, archivar, compartir, analizar, visualizar y proteger la creciente cantidad de datos, para que podamos desarrollar economÃas basadas en datos e información que sean capaces de reducir las ineficiencias e incrementar la sostenibilidad, creando nuevas oportunidades de negocio por el camino. La forma en la que se han manejado los datos tradicionalmente no es la adecuada hoy en dÃa, ya que carece de los medios para escalar a los volúmenes más grandes de datos de una forma oportuna y eficiente. Esto ha cambiado de alguna manera con la llegada de compañÃas que operan en Internet como Google o Facebook, ya que han concebido nuevas aproximaciones para abordar el problema. Sin embargo, la variedad y complejidad de las cadenas de valor en el sector privado y las crecientes demandas y limitaciones en las que el sector público opera, necesitan una investigación continua en la materia que pueda proporcionar nuevas estrategias para procesar las enormes cantidades de datos, facilitar la integración de productores y consumidores de información, y garantizar una transición rápida y fluida a la hora de adoptar estos avances tecnológicos innovadores. Esta tesis tiene como objetivo proporcionar nuevas arquitecturas y técnicas que ayudarán a realizar esta transición hacia Big Data en archivos cientÃficos masivos. La investigación destaca los escollos principales a encarar cuando se adoptan estas nuevas tecnologÃas y cómo afrontarlos, principalmente cuando los datos y las herramientas de transformación utilizadas en el análisis existen en la organización. Además, se exponen nuevas medidas para facilitar una transición más fluida. Éstas incluyen la utilización de software de alto nivel y especÃfico al caso de uso en cuestión, que haga de puente entre el dominio cientÃfico y tecnológico. Esta alternativa ampliará de una forma efectiva las posibilidades de los archivos cientÃficos y por tanto contribuirá a la reducción del tiempo necesario para generar resultados cientÃficos a partir de los datos recogidos en las misiones de astronomÃa espacial y planetaria. La investigación se aplicará a la misión de la Agencia Espacial Europea (ESA) Gaia, cuyo archivo final de datos presentará un gran potencial para el descubrimiento y hallazgo desde el punto de vista cientÃfico. La misión creará el catálogo en tres dimensiones más grande y preciso de nuestra galaxia (la VÃa Láctea), proporcionando medidas sin precedente acerca del posicionamiento, paralaje y movimiento propio de alrededor de mil millones de estrellas. Las oportunidades para la explotación exitosa de este archivo de datos dependerán en gran medida de la capacidad de ofrecer la arquitectura adecuada, es decir infraestructura y servicios, sobre la cual los cientÃficos puedan realizar la exploración y modelado con esta inmensa cantidad de datos. Por tanto, la estrategia a realizar debe ser capaz de combinar los datos con otros archivos cientÃficos, ya que esto producirá sinergias que contribuirán a un incremento en la ciencia producida, tanto en volumen como en calidad de la misma. El conjunto de técnicas e infraestructuras innovadoras presentadas en este trabajo aborda estos problemas, contextualizándolos con los productos de datos que se generarán en la misión Gaia. Todas estas consideraciones han conducido a los fundamentos de la arquitectura que se utilizará en el paquete de trabajo de aplicaciones que posibilitarán la ciencia en el archivo de la misión Gaia (Science Enabling Applications). Por último, la eficacia de la solución propuesta se demostrará a través de la implementación de dos problemas estadÃsticos que requerirán cantidades significativas de cómputo, y que usarán datos simulados en el mismo formato en el que se producirán en el archivo de la misión Gaia (la primera versión de datos recogidos por la misión está disponible desde el dÃa 14 de Septiembre de 2016). Estos ambiciosos problemas representan el Gran Reto (Grand Challenge), un nombre grandilocuente que consiste en inferir una serie de parámetros desde un punto de vista probabilÃstico para la función de masa inicial (Initial Mass Function) y la tasa de formación estelar (Star Formation Rate) dado un conjunto de estrellas (con una muestra grande), desde estimaciones con ruido de sus masas y edades respectivamente. Esto se abordará utilizando modelos jerárquicos bayesianos (Hierarchical Bayesian Modeling). Enprincipio,losmodelospropuestos pueden incorporar otros modelos de evolución estelar para inferir directamente la función de masa inicial y la tasa de formación estelar, pero en este primer paso presentado en esta tesis, empezaremos con un objetivo algo menos ambicioso: la inferencia de la función de masa y distribución de edades actual (Present-Day Mass Function y Present-Day Age Distribution respectivamente). Además, se llevará a cabo el análisis de rendimiento y escalabilidad para probar la idoneidad de la implementación de dichos modelos dadas las enormes cantidades de datos que estarán disponibles en el archivo de la misión Gaia...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu
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