Narrow Results

We present a new data structure to support orthogonal range max queries on a datacube. For a d-dimensional datacube with size n in each dimension where d≤c₃log log n/log(log* n), our structure requires query time and O((c₂n)^d) storage where c₁, c₂ and c₃ are constants independent of d and n; and log* n is the minimum number of repeated logarithms it takes to reduce the value n to at most 2. Hence our data structure is asymptotically optimal when d is constant independent of n.

On-line Analytical Processing (OLAP) has become one of the most powerful and prominent technologies for knowledge discovery in VLDB (Very Large Database) environments. Central to the OLAP paradigm is the data cube, a multi dimensional hierarchy of aggregate values that provides a rich analytical model for decision support. Various sequential algorithms for the efficient generation of the data cube have appeared in the literature. However, given the size of contemporary data warehousing repositories, multi-processor solutions are crucial for the massive computational demands of current and future OLAP systems.

In this paper we discuss the development of MCMD-CUBE, a new parallel data cube construction method for multi-core processors with multiple disks. We present experimental results for a Sandy Bridge multi-core processor with four parallel disks. Our experiments indicate that MCMD-CUBE achieves very close to linear speedup. A critical part of our MCMD-CUBE method is parallel sorting. We developed a new parallel sorting method termed MCMD-SORT for multi-core processors with multiple disks which outperforms other previous methods.

Recently, growing privacy concerns have received more and more attention and it becomes a significant topic on how to preserve private-sensitive information from being violated in distributed cooperative computation. In this paper, we first propose a novel-general privacy-preserving online analytical processing model based on secure multiparty computation. Then, based on the new model, two schemes to privacy-preserving count aggregate query over both horizontally partitioned data and vertically partitioned data are proposed. Additionally, we also propose several efficient subprotocols that serve as the basic secure buildings. Furthermore, we analyze correctness, security, communication cost, and computation complexity of our proposed protocols, and show that the new schemes are secure, having good linear complexity and that the query results are exactly accurate.

Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

On-line Analytic Processing (OLAP) is a powerful technique especially for solving business problems in terms of a multidimensional model. Traditional OLAP models (such as ROLAP and MOLAP) are not sufficient to answer queries for complex applications. In this research, we extend the traditional multidimensional model and OLAP operations with an object relational multidimensional model. We also discuss how to implement the object relational multidimensional model on top of a relational database.

The special needs of the OLAP technology were the main cause of the use of a multidimensional view of the data. Crisp models are not suitable to model complex or non well defined domains. They also fail to integrate data from semi/non-structured sources (e.g. Internet) or with incompatibilities in their schemata. In these situations, as a result of the modelling and/or integration, imprecision appears. So, we need a model able to manage imprecision in the structures and data. If we want to use expert's knowledge in the analysis, we have to keep in mind that expert users are more comfortable when they use linguistic expressions instead of exact values. In this paper we present an extension of a fuzzy multidimensional model to support the use of linguistic labels in the definition of the hierarchies and the OLAP system that implements this model.

Querying data stream data continuously has been addressed mostly as transactional queries with some attempts at analytical processing. But, in most of the proposals a single query is executed for a given window of data. In this paper, we propose to continuously execute multiple related OLAP queries (CMOLAP) for the data chosen from a data stream. The chosen data defines the context. The context data is temporarily stored in the form of a multidimensional cube to perform OLAP operations. Three sets of operations are defined. The first converts the data in a stream to a context, the second allows altering the context and the third set is analytical which operates on the context and produces an output stream. More than one related analytic operation can be performed for the data in a context. The sequence of operations, referred to as context queries, is continuously executed for a time-based window. As a result it is possible to do enhanced related analysis of data. We have also developed a GUI interface where the queries can be expressed in a user friendly manner.

Online analytical processing (OLAP) queries normally incur enormous processing overheads due to the huge size of data warehouses. This results in unacceptable response times. Parallel processing using a cluster of workstations has of late emerged as a practical solution to many compute and data intensive problems. In this article, we present parallel algorithms for some of the OLAP operators. We have implemented these parallel solutions for a data warehouse implemented on Oracle hosted in a cluster of workstations. Our performance studies show that encouraging speedups are achieved.

As a result of today's competitive business environment, companies have been trying to improve the utilization of funds effectively in their budgets for information technology investments. These companies retrieve more information with the same set of resources by means of business intelligence methods. According to Rubin (Chabrow, 2004) IT budgets are not simply declining or levelling off, rather, companies are shifting from a pure cost-cut mode to a model that emphasises agility and efficiency. Tremendous daily growth of the company data requires more funds and investment for establishing the technologies and infrastructure necessary for gathering fast and crucial information that supports the decision making process. This necessity gave birth to various business intelligence methods, which mainly aim to process mass amount of collected data from their existing application, and represent it in a way with which companies can apply to their daily competitive decisions.

This application primarily concerns the implementation of business intelligence for a retail business company. The aim is to implement built-in business intelligence solutions of the Microsoft SQL Server that holds the commercial information of the company for the past three years. The customer company has already been using Microsoft products. The key items used for analyzing data are sales, momentary inventory and logistics information.

The application can be grouped in five main areas: Building the data warehouse, constructing OLAP cubes, applying data mining algorithms on OLAP cubes, representing the results in reports with reporting services, and implementation.

This paper focuses attention on the problem of supporting multidimensional big data analytics tools over Big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm.

The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data. This paper goes over commercial OLAP software available as well as OLAP techniques such as “slice and dice” and “drill down and roll up.” It discusses various techniques and metrics used to evaluate how accurate a ML algorithm is.

Online Analytical Processing (OLAP) is an effective approach to analyzing various complex business problems, and graph is considered as a common scheme to represent the business datasets. Network analysis is a broad analytics scheme for exploring the connectivity and deriving useful analytics results. However, network analysis for graph-based OLAP presents a set of more specific analytics methods by utilizing graph model, network property, and OLAP principles. In this paper, we present a comprehensive survey on network analysis conducted on graph model for the purpose of OLAP, and we summarize the current research focus, paradigms, and the future needs on the target technology.

Online Analytical Processing (OLAP) is an effective approach to analyzing various complex business problems, and graph is considered as a common scheme to represent the business datasets. Network analysis is a broad analytics scheme for exploring the connectivity and deriving useful analytics results. However, network analysis for graph-based OLAP presents a set of more specific analytics methods by utilizing graph model, network property, and OLAP principles. In this paper, we present a comprehensive survey on network analysis conducted on graph model for the purpose of OLAP, and we summarize the current research focus, paradigms, and the future needs on the target technology.

The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data. This paper goes over commercial OLAP software available as well as OLAP techniques such as “slice and dice” and “drill down and roll up.” It discusses various techniques and metrics used to evaluate how accurate a ML algorithm is.