scholarly journals Multidimensional Data Exploration by Explicitly Controlled Animation

Informatics ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 26 ◽  
Author(s):  
Johannes Kruiger ◽  
Almoctar Hassoumi ◽  
Hans-Jörg Schulz ◽  
AlexandruC Telea ◽  
Christophe Hurter
Keyword(s):  
Data Exploration ◽  
Multidimensional Data

scholarly journals The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration

2020 ◽  
Author(s):  
Alyssa Long ◽  
Alexander Glogowski ◽  
Matthew Meppiel ◽  
Lisa De Vito ◽  
Eric Engle ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Clinical Research ◽  
Architectural Design ◽  
Application Programming Interface ◽  
Lessons Learned ◽  
Data Exploration ◽  
Multidimensional Data ◽  
Server Side ◽  
Wide Range ◽  
Clinical Research Informatics

Abstract Objective Clinical research informatics tools are necessary to support comprehensive studies of infectious diseases. The National Institute of Allergy and Infectious Diseases (NIAID) developed the publicly accessible Tuberculosis Data Exploration Portal (TB DEPOT) to address the complex etiology of tuberculosis (TB). Materials and Methods TB DEPOT displays deidentified patient case data and facilitates analyses across a wide range of clinical, socioeconomic, genomic, and radiological factors. The solution is built using Amazon Web Services cloud-based infrastructure, .NET Core, Angular, Highcharts, R, PLINK, and other custom-developed services. Structured patient data, pathogen genomic variants, and medical images are integrated into the solution to allow seamless filtering across data domains. Results Researchers can use TB DEPOT to query TB patient cases, create and save patient cohorts, and execute comparative statistical analyses on demand. The tool supports user-driven data exploration and fulfills the National Institute of Health’s Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Discussion TB DEPOT is the first tool of its kind in the field of TB research to integrate multidimensional data from TB patient cases. Its scalable and flexible architectural design has accommodated growth in the data, organizations, types of data, feature requests, and usage. Use of client-side technologies over server-side technologies and prioritizing maintenance have been important lessons learned. Future directions are dynamically prioritized and key functionality is shared through an application programming interface. Conclusion This paper describes the platform development methodology, resulting functionality, benefits, and technical considerations of a clinical research informatics application to support increased understanding of TB.

Explorative Data Analysis Support for Environmental Epidemiology

2001 ◽  
pp. 186-196
Author(s):  
V. Kamp
Keyword(s):  
Data Analysis ◽  
Environmental Epidemiology ◽  
Data Representation ◽  
Database Management System ◽  
Multidimensional Analysis ◽  
Data Exploration ◽  
Multidimensional Data ◽  
Lower Saxony ◽  
Environmental Information Systems ◽  
Special Support

Applications like environmental information systems define various scenarios of multidimensional data analysis, and they require special efforts concerning data representation, storage and processing. The project CARLOS (Cancer Registry Lower-Saxony) developed the Epidemiological and Statistical Data Exploration System (CARESS) to support multidimensional analysis of health data. The system is based on an architecture that focuses on extensive interoperability between a database management system and several analysis and visualisation tools. As spatial and statistical aspects of the data play an important role, CARESS provides special support for the integration of both.

View Selection in DW and OLAP

2011 ◽  
pp. 2048-2055
Author(s):  
Alfredo Cuzzocrea
Keyword(s):  
Knowledge Discovery ◽  
Data Warehousing ◽  
Data Cube ◽  
Data Exploration ◽  
Multidimensional Data ◽  
Data Cubes ◽  
View Selection ◽  
Materialized View ◽  
Data Source ◽  
Time Required

Data Warehousing (DW) systems store materialized views, data marts and data cubes, and provide nicely data exploration and analysis interfaces via OnLine Analytical Processing (OLAP) (Gray et al., 1997) and Data Mining (DM) tools and algorithms. Also, OnLine Analytical Mining (OLAM) (Han, 1997) integrates the previous knowledge discovery methodologies and offers a meaningfully convergence between OLAP and DM, thus contributing to significantly augment the power of data exploration and analysis capabilities of knowledge workers. At the storage layer, the mentioned knowledge discovery methodologies share the problem of efficiently accessing, querying and processing multidimensional data, which in turn heavily affect the performance of knowledge discovery processes at the application layer. Due to the fact that OLAP and OLAM directly process data cubes/marts, and DM is more and more encompassing methodologies that are interested to multidimensional data, the problem of efficiently representing data cubes by means of a meaningfully selected view set is become of relevant interest for the Data Warehousing and OLAP research community. This problem is directly related to the analogous problem of efficiently computing the data cube from a given relational data source (Harinarayan et al., 1996; Agarwal et al., 1996; Sarawagi et al., 1996; Zhao et al., 1997). Given a relational data source R and a target data cube schema W, the view selection problem in OLAP deals with how to select and materialize views from R in order to compute the data cube A defined by the schema W by optimizing both the query processing time, denoted by TQ, which models the amount of time required to answer a reference query-workload on the materialized view set, and the view maintenance time, denoted by TM, which models the amount of time required to maintain the materialized view set when updates occur, under a given set of constraints I that, without any loss of generality, can be represented by a space bound constraint B limiting the overall occupancy of the views to be materialized (i.e., I = ). It has been demonstrated (Gupta, 1997; Gupta & Mumick, 2005) that this problem is NP-hard, thus heuristic schemes are necessary. Heuristics are, in turn, implemented in the vest of greedy algorithms (Yang et al., 1997; Kalnis et al., 2002). In this article, we focus the attention on state-ofthe- art methods for the view selection problem in Data Warehousing and OLAP, and complete our analytical contribution with a theoretical analysis of these proposals under different selected properties that nicely model spatial and temporal complexity aspects of the investigated problem.

scholarly journals Multidimensional Data Exploration with Glue

2013 ◽  
Author(s):  
Christopher Beaumont ◽  
Thomas Robitaille ◽  
Alyssa Goodman ◽  
Michelle Borkin
Keyword(s):  
Data Exploration ◽  
Multidimensional Data

scholarly journals Evaluating Immersive Approaches to Multidimensional Information Visualization

2019 ◽  
Author(s):  
Jorge A. Wagner Filho ◽  
Carla M. D. S. Freitas ◽  
Luciana Nedel
Keyword(s):  
Information Visualization ◽  
User Study ◽  
Direct Interaction ◽  
Data Exploration ◽  
Multidimensional Data ◽  
Frequent User ◽  
Multidimensional Information ◽  
Immersive Environment ◽  
Time Overhead ◽  
User Comfort

This dissertation investigates the use of Virtual Reality for the exploration of multidimensional data represented as 3D scatterplots. After an initial user study indicated that an immersive environment required less effort to find information and less navigation, but resulted in inefficient times and frequent user discomfort, we proposed and evaluated an alternative data exploration approach based on the use of physical movements, direct interaction with data at arms reach and a virtual reproduction of the analysts work desk. Through a second study, we demonstrate that this setup, named VirtualDesk, presents excellent results regarding user comfort, and performs equally or better in all tasks, while adding minimal or no time overhead and amplifying data exploration.

scholarly journals WormGUIDES: an interactive single cell developmental atlas and tool for collaborative multidimensional data exploration

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Anthony Santella ◽  
Raúl Catena ◽  
Ismar Kovacevic ◽  
Pavak Shah ◽  
Zidong Yu ◽  
...  
Keyword(s):  
Single Cell ◽  
Data Exploration ◽  
Multidimensional Data

Recommendations for deploying multidimensio

2017 ◽  
Vol 11 (1) ◽  
pp. 68-80
Author(s):  
E. E. Akimkina
Keyword(s):  
Comparative Analysis ◽  
Management System ◽  
Analytical Data ◽  
Enterprise Management ◽  
Multidimensional Data ◽  
Dimensional Approach ◽  
Process Data ◽  
Modeling Systems ◽  
Enterprise Management System ◽  
Practical Recommendations

A comparative analysis of different approaches to analytical data and shows that the most ample opportunities has a multi-dimensional approach, implemented with the help of OLAP technology. Presented multidimensional OLAP-cube model with the measurements for the analysis and processing of process data. Practical recommendations for the deployment of a multidimensional data modeling systems with regard to their integration into existing enterprise management system.

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