VDDA: automatic visualization-driven data aggregation in relational databases (original) (raw)

Faster visual analytics through pixel-perfect aggregation

Proceedings of the VLDB Endowment, 2014

State-of-the-art visual data analysis tools ignore bandwidth limitations. They fetch millions of records of high-volume time series data from an underlying RDBMS to eventually draw only a few thousand pixels on the screen.

M4: A visualization-oriented time series data aggregation

Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of time series data disregard the semantics of visualizations and result in visualization errors.

Extreme visualization: squeezing a billion records into a million pixels

2008

ABSTRACT Database searches are usually performed with query languages and form fill in templates, with results displayed in tabular lists. However, excitement is building around dynamic queries sliders and other graphical selectors for query specification, with results displayed by information visualization techniques. These filtering techniques have proven to be effective for many tasks in which visual presentations enable discovery of relationships, clusters, outliers, gaps, and other patterns.

Mr. Plotter: Unifying Data Reduction Techniques in Storage and Visualization Systems

ArXiv, 2021

As the rate of data collection continues to grow rapidly, developing visualization tools that scale to immense data sets is a serious and ever-increasing challenge. Existing approaches generally seek to decouple storage and visualization systems, performing just-in-time data reduction to transparently avoid overloading the visualizer. We present a new architecture in which the visualizer and data store are tightly coupled. Unlike systems that read raw data from storage, the performance of our system scales linearly with the size of the final visualization, essentially independent of the size of the data. Thus, it scales to massive data sets while supporting interactive performance (sub-100 ms query latency). This enables a new class of visualization clients that automatically manage data, quickly and transparently requesting data from the underlying database without requiring the user to explicitly initiate queries. It lays a groundwork for supporting truly interactive exploration o...

DEVise: Integrated Querying and Visualization of Large Datasets

Sigmod, 1997

DEVise is a data exploration system that allows users to ez-sily develop, browse, and share visual presentations of large tabular dataaets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework is being implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups. Our emphasis is on developing an intuitive yet powerful set of querying and visualization primitives that can be easily combhed to develop a rich set of visual presentations that integrate data from a wide range of application domains. While DEVise is a powerful visualization tool, its greatest strengths are the ability to interactively explore a visual presentation of the data at any level of detail (including retrieving individual data records), and the ability to seamlessly query and combine data from a variety of local and remote sources. In this paper, we present the DEVise framework, describe the current tool, and report on our experience in applying it to several real applications.

DEVise: integrated querying and visual exploration of large datasets

Sigmod Record, 1997

DEVise is a data exploration system that allows users to ez-sily develop, browse, and share visual presentations of large tabular dataaets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework is being implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups.

Query-Driven Visualization of Large Data Sets

VIS 05. IEEE Visualization, 2005., 2005

We present a practical and general-purpose approach to large and complex visual data analysis where visualization processing, rendering and subsequent human interpretation is constrained to the subset of data deemed interesting by the user. In many scientific data analysis applications, "interesting" data can be defined by compound Boolean range queries of the form (temperature > 1000) AND (70 < pressure < 90). As data sizes grow larger, a central challenge is to answer such queries as efficiently as possible. Prior work in the visualization community has focused on answering range queries for scalar fields within the context of accelerating the search phase of isosurface algorithms. In contrast, our work describes an approach that leverages state-of-the-art indexing technology from the scientific data management community called "bitmap indexing." Our implementation, which we call "DEX" (short for dextrous data explorer), uses bitmap indexing to efficiently answer multivariate, multidimensional data queries to provide input to a visualization pipeline. We present an analysis overview and benchmark results that show bitmap indexing offers significant storage and performance improvements when compared to previous approaches for accelerating the search phase of isosurface algorithms. More importantly, since bitmap indexing supports complex multidimensional, multivariate range queries, it is more generally applicable to scientific data visualization and analysis problems. In addition to benchmark performance and analysis, we apply DEX to a typical scientific visualization problem encountered in combustion simulation data analysis.

Visualizing the structure of large relational databases

IEEE Software, 1996

Large relational databases, such as those associated with legacy software systems, can be difficult to engineer and extend. This visualization system abandons the use of entityrelationship diagrams in favor of a ZD, multiview, CQIO~ representation of the database structure. It can easily display databases with more than 1,000 relations. A large database is inherently complex and may become even harder to understand as it ages. Database structures may degrade because of changing requirements, evolving technology, unplanned enhancements, and staff turnover. After years of modification, the scope and size of the database may be far beyond what its originators envisioned. The initial model may become almost unrecognizable, and even its designers may no longer understand the overall structure. Visuahzation systems based on the use of network graphs and entityrelationship diagrams' can help somewhat by graphically representing a database's structure. The entity-relationship diagram explicitly shows the entities (or objects) of interest and the relationshps, or associations, among them. Using this diagram, systems engineers can design a database as a collection of relations that represent the entiQes in the problem domain and the associations among them.