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