Query Optimization and Execution for Multi-Dimensional OLAP

Online Analytical Processing (OLAP) is a database paradigm that supports the rich analysis of multi-dimensional data. While current OLAP tools are primarily constructed as extensions to conventional relational databases, the unique modeling and processing requirements of OLAP systems often make for a relatively awkward fit with RDBM systems in general, and their embedded string-based query languages in particular. In this thesis, we discuss the design, implementation, and evaluation of a robust multi-dimensional OLAP server. In fact, we focus on several distinct but related themes. To begin, we investigate the integration of an open source embedded storage engine with our own OLAP-specific indexing and access methods. We then present a comprehensive OLAP query algebra that ultimately allows developers to create expressive OLAP queries in native client languages such as Java. By utilizing a formal algebraic model, we are able to support an intuitive Object Oriented query API, as well as a powerful query optimization and execution engine. The thesis describes both the optimization methodology and the related algorithms for the efficient execution of the associated query plans. The end result of our research is a comprehensive OLAP DBMS prototype that clearly demonstrates new opportunities for improving the accessibility, functionality, and performance of current OLAP database management systems

A Framework for Real-time Analysis in OLAP Systems

OLAP systems are designed to quickly answer multi-dimensional queries against large data warehouse systems. Constructing data cubes and their associated indexes is time consuming and computationally expensive, and for this reason, data cubes are only refreshed periodically. Increasingly, organizations are demanding for both historical and predictive analysis based on the most current data. This trend has also placed the requirement on OLAP systems to merge updates at a much faster rate than before. In this thesis, we proposes a framework for OLAP systems that enables updates to be merged with data cubes in soft real-time. We apply a strategy of local partitioning of the data cube, and maintain a ``hot'' partition for each materialized view to merge update data. We augment this strategy by applying multi-core processing using the OpenMP library to accelerate data cube construction and query resolution. Experiments using a data cube with 10,000,000 tuples and an update set of 100,000 tuples show that our framework achieves a 99% performance improvement updating the data cube, a 76% performance increase when constructing a new data cube, and a 72% performance increase when resolving a range query against a data cube with 1,000,000 tuples

Native Language OLAP Query Execution

Online Analytical Processing (OLAP) applications are widely used in the components of contemporary Decision Support systems. However, existing OLAP query languages are neither efficient nor intuitive for developers. In particular, Microsoft’s Multidimensional Expressions language (MDX), the de-facto standard for OLAP, is essentially a string-based extension to SQL that hinders code refactoring, limits compile-time checking, and provides no object-oriented functionality whatsoever. In this thesis, we present Native language OLAP query eXecution, or NOX, a framework that provides responsive and intuitive query facilities. To this end, we exploit the underlying OLAP conceptual data model and provide a clean integration between the server and the client language. NOX queries are object-oriented and support inheritance, refactoring and compile-time checking. Underlying this functionality is a domain specific algebra and language grammar that are used to transparently convert client side queries written in the native development language into algebraic operations understood by the server. In our prototype of NOX, JAVA is used as the native language. We provide client side libraries that define an API for programmers to use for writing OLAP queries. We investigate the design of NOX through a series of real world query examples. Specifically, we explore the following: fundamental SELECTION and PROJECTION, set operations, hierarchies, parametrization and query inheritance. We compare NOX queries to MDX and show the intuitiveness and robustness of NOX. We also investigate NOX expressiveness with respect to MDX from an algebraic point of view by demonstrating the correspondence of the two approaches in terms of SELECTION and PROJECTION operations. We believe the practical benefit of NOX-style query processing is significant. In short, it largely reduces OLAP database access to the manipulation of client side, in-memory data object

A UML framework for OLAP conceptual modeling

Data warehouses are used by organizations around the world to store huge volumes of historical data. Ultimately, the purpose of the warehouse is to allow decision makers to assess both the history and, more importantly, the future of the organization. In practice, the capacity to make meaningful decisions is further supported through the use of Online Analytical Processing (OLAP) applications that provide more sophisticated representations of the warehouse data. In order to do this, OLAP systems rely on a multidimensional conceptual data model that represents the core elements of the data warehouse, as well as the relationships between them. Currently, there is no definitive conceptual model for this kind of environment. It is therefore quite difficult for data warehouse designers to express the kinds of complex analytical requirements which arise in real-world situations. In this thesis, we propose a robust and flexible conceptual model that can be used to represent multi-dimensional OLAP domains. Specifically, we present a profile extension of the Unified Modeling Language (UML) that consists of a set of stereotypes, constraints and tagged values that elegantly represent multi-dimensional properties at the conceptual level. We also make use of the Object Constraint Language (OCL) to ensure the correctness and completeness of the specification, thereby avoiding an arbitrary use of the basic components. Furthermore, we demonstrate how the new OLAP profile is utilized in MagicDraw, one of the leading UML development tools. The end result is an OLAP Modeling Environment (OME) that should significantly reduce development time, as well as improving the quality of the analytical interface for the end user

Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8

Calibración de un algoritmo de detección de anomalías marítimas basado en la fusión de datos satelitales

La fusión de diferentes fuentes de datos aporta una ayuda significativa en el proceso de toma de decisiones. El presente artículo describe el desarrollo de una plataforma que permite detectar anomalías marítimas por medio de la fusión de datos del Sistema de Información Automática (AIS) para seguimiento de buques y de imágenes satelitales de Radares de Apertura Sintética (SAR). Estas anomalías son presentadas al operador como un conjunto de detecciones que requieren ser monitoreadas para descubrir su naturaleza. El proceso de detección se lleva adelante primero identificando objetos dentro de las imágenes SAR a través de la aplicación de algoritmos CFAR, y luego correlacionando los objetos detectados con los datos reportados mediante el sistema AIS. En este trabajo reportamos las pruebas realizadas con diferentes configuraciones de los parámetros para los algoritmos de detección y asociación, analizamos la respuesta de la plataforma y reportamos la combinación de parámetros que reporta mejores resultados para las imágenes utilizadas. Este es un primer paso en nuestro objetivo futuro de desarrollar un sistema que ajuste los parámetros en forma dinámica dependiendo de las imágenes disponibles.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computación

Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI

Il lavoro 4.0

The research in this volume is based on the link between the Fourth Industrial Revolution and work. On this level, there are several questions. What is the nature of work 4.0? What is the relationship between the technological revolution and employment? What are the labour rights in the era of new business models? Can innovation be implemented without overcoming the twentieth-century subordination and the new forms of freedom and work responsibility? Do digitalisation and new forms of business organization change working relationships and favour new forms of collaboration and conflict? Are training, quality and freedom at work more important than wages? How do company planning and design relate to technology and work? Does digitalisation push knowledge-based economy to determine new forms of work? What projects need to be implemented by the involved parties (starting with the workers) for these transformations to be a step forward in working conditions and industrial relations? These and many other questions are the basis of the essays collected in this volume, born from the collaboration between authors of different backgrounds and experience: academics, journalists, entrepreneurs, managers, operators, trade unionists and trade union representatives