677 research outputs found

    Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

    Full text link
    Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou

    Clustering-Based Materialized View Selection in Data Warehouses

    Full text link
    Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited

    CubiST: A New Algorithm for Improving the Performance of Ad-hoc OLAP Queries

    Get PDF
    Being able to efficiently answer arbitrary OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes has been a continued, major concern in data warehousing. In this paper, we introduce a new data structure, called Statistics Tree (ST), together with an efficient algorithm called CubiST, for evaluating ad-hoc OLAP queries on top of a relational data warehouse. We are focusing on a class of queries called cube queries, which generalize the data cube operator. CubiST represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the familiar view lattice to compute and materialize new views from existing views in some heuristic fashion. CubiST is the first OLAP algorithm that needs only one scan over the detailed data set and can efficiently answer any cube query without additional I/O when the ST fits into memory. We have implemented CubiST and our experiments have demonstrated significant improvements in performance and scalability over existing ROLAP/MOLAP approaches

    improving query performance using distributed computing

    Get PDF
    Data warehouses are used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP) where short response times are essential for on-line decision support. One of the most important requirements of a data warehouse server is the query performance. The principal aspect from the user perspective is how quickly the server processes a given query: “the data warehouse must be fast”. The main focus of our research is finding adequate solutions to improve query response time of typical OLAP queries and improve scalability using a distributed computation environment that takes advantage of characteristics specific to the OLAP context. Our proposal provides very good performance and scalability even on huge data warehouses

    CubiST++: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees

    Get PDF
    We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed CubiST++ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, CubiST++ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select and materialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems

    Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

    Get PDF
    Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations

    Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

    Get PDF
    Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations
    • …
    corecore