1,155 research outputs found
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
SPARSITY HANDLING AND DATA EXPLOSION IN OLAP SYSTEMS
A common problem with OnLine Analytical Processing (OLAP) databases is data explosion - data size multiplies, when it is loaded from the source data into multidimensional cubes. Data explosion is not an issue for small databases, but can be serious problems with large databases. In this paper we discuss the sparsity and data explosion phenomenon in multidimensional data model, which lie at the core of OLAP systems. Our researches over five companies with different branch of business confirm the observations that in reality most of the cubes are extremely sparse. We also consider a different method that relational and multidimensional severs applies to reduce the data explosion and sparsity problems as compression and indexes techniques, partitioning, preliminary aggregations
The FZ Strategy to Compress the Bitmap Index for Data Warehouses
Data warehouses contain data consolidated from several operational databases and provide the historical, and summarized data which is more appropriate for analysis than detail, individual records. Fast response time is essential for on-line decision support. A bitmap index could reach this goal in read-mostly environments. For the data with high cardinality in data warehouses, a bitmap index consists of a lot of bitmap vectors, and the size of the bitmap index could be much larger than the capacity of the disk. The WAH strategy has been presented to solve the storage overhead. However, when the bit density and clustering factor of 1\u27s increase, the bit strings of the WAH strategy become less compressible. Therefore, in this paper, we propose the FZ strategy which compresses each bitmap vector to reduce the size of the storage space and provide efficient bitwise operations without decompressing these bitmap vectors. From our performance simulation, the FZ strategy could reduce the storage space more than the WAH strategy
Compressed Video Action Recognition
Training robust deep video representations has proven to be much more
challenging than learning deep image representations. This is in part due to
the enormous size of raw video streams and the high temporal redundancy; the
true and interesting signal is often drowned in too much irrelevant data.
Motivated by that the superfluous information can be reduced by up to two
orders of magnitude by video compression (using H.264, HEVC, etc.), we propose
to train a deep network directly on the compressed video.
This representation has a higher information density, and we found the
training to be easier. In addition, the signals in a compressed video provide
free, albeit noisy, motion information. We propose novel techniques to use them
effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times
faster than ResNet-152. On the task of action recognition, our approach
outperforms all the other methods on the UCF-101, HMDB-51, and Charades
dataset.Comment: CVPR 2018 (Selected for spotlight presentation
Representation and Exploitation of Event Sequences
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
The Ten Commandments, the thirty best smartphones in the market and
the five most wanted people by the FBI. Our life is ruled by sequences:
thought sequences, number sequences, event sequences. . . a history book
is nothing more than a compilation of events and our favorite film is
just a sequence of scenes. All of them have something in common, it
is possible to acquire relevant information from them. Frequently, by
accumulating some data from the elements of each sequence we may
access hidden information (e.g. the passengers transported by a bus
on a journey is the sum of the passengers who got on in the sequence
of stops made); other times, reordering the elements by any of their
characteristics facilitates the access to the elements of interest (e.g. the
publication of books in 2019 can be ordered chronologically, by author,
by literary genre or even by a combination of characteristics); but it
will always be sought to store them in the smallest space possible.
Thus, this thesis proposes technological solutions for the storage
and subsequent processing of events, focusing specifically on three
fundamental aspects that can be found in any application that needs
to manage them: compressed and dynamic storage, aggregation
or accumulation of elements of the sequence and element sequence
reordering by their different characteristics or dimensions.
The first contribution of this work is a compact structure for the
dynamic compression of event sequences. This structure allows any
sequence to be compressed in a single pass, that is, it is capable of
compressing in real time as elements arrive. This contribution is
a milestone in the world of compression since, to date, this is the
first proposal for a variable-to-variable dynamic compressor for general purpose.
Regarding aggregation, a data warehouse-like proposal is presented
capable of storing information on any characteristic of the events in a
sequence in an aggregated, compact and accessible way. Following the
philosophy of current data warehouses, we avoid repeating cumulative
operations and speed up aggregate queries by preprocessing the
information and keeping it in this separate structure.
Finally, this thesis addresses the problem of indexing event sequences
considering their different characteristics and possible reorderings. A new
approach for simultaneously keeping the elements of a sequence ordered
by different characteristics is presented through compact structures.
Thus, it is possible to consult the information and perform operations
on the elements of the sequence using any possible rearrangement in a
simple and efficient way.[Resumen]
Los diez mandamientos, los treinta mejores móviles del mercado y las
cinco personas más buscadas por el FBI. Nuestra vida está gobernada
por secuencias: secuencias de pensamientos, secuencias de números,
secuencias de eventos. . . un libro de historia no es más que una sucesión
de eventos y nuestra película favorita no es sino una secuencia de
escenas. Todas ellas tienen algo en común, de todas podemos extraer
información relevante. A veces, al acumular algún dato de los elementos
de cada secuencia accedemos a información oculta (p. ej. los viajeros
transportados por un autobús en un trayecto es la suma de los pasajeros
que se subieron en la secuencia de paradas realizadas); otras veces, la
reordenación de los elementos por alguna de sus características facilita
el acceso a los elementos de interés (p. ej. la publicación de obras
literarias en 2019 puede ordenarse cronológicamente, por autor, por
género literario o incluso por una combinación de características); pero
siempre se buscará almacenarlas en el espacio más reducido posible sin
renunciar a su contenido.
Por ello, esta tesis propone soluciones tecnológicas para el almacenamiento
y posterior procesamiento de secuencias, centrándose
concretamente en tres aspectos fundamentales que se pueden encontrar
en cualquier aplicación que precise gestionarlas: el almacenamiento
comprimido y dinámico, la agregación o acumulación de algún dato
sobre los elementos de la secuencia y la reordenación de los elementos
de la secuencia por sus diferentes características o dimensiones.
La primera contribución de este trabajo es una estructura compacta
para la compresión dinámica de secuencias. Esta estructura permite
comprimir cualquier secuencia en una sola pasada, es decir, es capaz de comprimir en tiempo real a medida que llegan los elementos de la
secuencia. Esta aportación es un hito en el mundo de la compresión ya
que, hasta la fecha, es la primera propuesta de un compresor dinámico
“variable to variable” de carácter general.
En cuanto a la agregación, se presenta una propuesta de almacén
de datos capaz de guardar la información acumulada sobre alguna
característica de los eventos de la secuencia de modo compacto y
fácilmente accesible. Siguiendo la filosofía de los actuales almacenes de
datos, el objetivo es evitar repetir operaciones de acumulación y agilizar
las consultas agregadas mediante el preprocesado de la información
manteniéndola en esta estructura.
Por último, esta tesis aborda el problema de la indexación de
secuencias de eventos considerando sus diferentes características y
posibles reordenaciones. Se presenta una nueva forma de mantener
simultáneamente ordenados los elementos de una secuencia por diferentes
características a través de estructuras compactas. Así se permite
consultar la información y realizar operaciones sobre los elementos
de la secuencia usando cualquier posible ordenación de una manera
sencilla y eficiente
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
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