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About 10,000 papers for “large databases”

VERY LARGE DATABASES

n.n. · 2008

A growing number of database applications require online, interactive access to very large volumes of data to perform a varietyofdataanalysistasks. As an example, largetelecommunication and Internet service providers typically collect and store Gigabytes or Terabytes of detailed usage information (Call Detail Records, SNMP/RMON packet flow data, etc.) from the underlying network to satisfy the requirements of various network management tasks, including billing, fraud/anomaly detection, and strategic planning. Such large datasets are typically represented either as massive alphanumeric data tables (in the relational data model) or as massive labeled data graphs (in richer, semistructured data models, such as extensible markup language (XML)). In order to deal with the huge data volumes, high query complexities, and interactive response time requirements characterizing these modern data analysis applications, the idea of effective, easy-to-compute approximations over precomputed, compact data synopses has recently emerged as a viable solution. Due to the exploratory nature of most target applications, there are a number of scenarios in which a (reasonably accurate) fast approximate answer over a smallfootprint summary of the database is actually preferable over an exact answer that takes hours or days to compute. For example, during a drill-down query sequence in ad hoc data mining, initial queries in the sequence frequently have the sole purpose of determining the truly interesting queries and regions of the database. Providing fast approximate answers to these initial queries gives users the ability to focus their explorations quickly and effectively, without consuming inordinate amounts of valuable system resources. The key, of course, behind such approximate techniques for dealing with massive datasets lies in the use of appropriate data reduction techniques for constructing compact data synopses that can accurately approximate the important features of the underlying data distribution. In this article, we provide an overview of date reduction and approximation methods for massive databases and discuss some of the issues that develop from different types of data, large data volumes, and applications-specific requirements.

Cited by 0

Very large databases

Peter J. B. King · ITNOW · 1987

Abstract As general conference chairman of the next VLDB which is to be held in the UK for the first time in September, I was delighted to accept the invitation to write this guest editorial.

Cited by 0

Very Large Databases.

Robert H Dorin, Joanne Z Sattley · Defense Technical Information Center (DTIC) · 1977

This report summarizes work performed under the ARPA Very Large Databases program. Section 1 describes the Datacomputer Technology Transfer project (DTT). This project was aimed at expansion of the Datacomputer user community by increasing the awarness of this unique tool in the government community. The result of this project was a substantial increase in Datacomputer utilization. Section 2 describes the Message Archiving and Retrieval System (MARS) project. The MARS effort was directed toward the design and implementation of a prototype system to provide economical storage and convenient retrieval of Arpanet mail. The system has been fully implemented and is operational at CCA on an experimental basis. This report describes the MARS prototype as well as concepts for future extensions of the system.

Cited by 0

Managing Large Databases

Jon Sewell · 2007

This report proposes a new approach to address the problems that are likely to arise from an increase in the complexity of databases for computer graphics. The first section provides a summary of the problems with existing systems, then the structural requirements for a solution are outlined in Section 2 and Section 3 gives a description of the functionality of the major components. Finally, likely developments in model descriptions are anticipated and their consequences discussed. 1 Introduction 1.1 The Entire Database Traditionally, a computer graphics application that renders images from a three dimensional scene description, considers the entire object database, which is often loaded and compiled from a textual representation at invocation time. As the size of such databases increases with the desire to model more complex scenes, an ever increasing cost is incurred. The I/O bandwidth required places limits on the processing speed and puts unnecessary load on the memory system. Ap...

Cited by 0

Querying Large Databases

Nathan Beneke · Scholarly Horizons University of Minnesota Morris Undergraduate Journal · 2018

This paper investigates two approaches to improving query times on large relational databases. The first technique capitalizes on the knowledge of a database's structures and properties one typically has. This technique can execute some queries exactly in a constant, bounded amount of time. When this technique cannot be used to exactly execute a query we show how it can still be used to drastically lower the run-time on the query while getting a good approximation of the exact result. We also discuss the complexity of deciding whether a query is evaluable in this way, both theoretically and practically. The second approach approximates aggregate queries by incorporating only part of the data, rather than all of the data the query pertains to. We briefly investigate an established method of sampling a random subset of the data, and then a newer method which partially reads every tuple and puts deterministic error bounds on the results.

Cited by 1Open access

Very Large Databases

Minos Garofalakis, Neoklis Polyzotis · Wiley Encyclopedia of Computer Science and Engineering · 2007

Abstract In this article, we provide an overview of date reduction and approximation methods for massive databases and discuss some of the issues that develop from different types of data, large data volumes, and applications‐specific requirements.

Cited by 0

Large Distributed Databases group

Walter De Back, Marco Wiering, Edwin De Jong et al. · 2012

The growing interest in the biological roots of cognition leads to the crossfertilization between the fields of autonomous robotics and artificial life. This requires new tools that facilitate research on the interface between embodied cognitive science and theoretical biology. In this thesis, a model is presented that enables the simulation of evolving ecosystems of situated agents. This model enables studies to the interplay between situated interaction, selforganised collective behaviour and evolution by natural selection. The use of complex computer simulations as scientific tools requires a theoretical embedding. This is established by analysing and interpreting the results of ecological simulations (of bitrophic and tritrophic food chains) in term of analytical models of population dynamics. These ordinary differential equation (ODE) models allow us to understand and control the population dynamics that emerge from simulations. Moreover, the evolutionary dynamics observed in eco-evolutionary simulations can be interpreted as changes in the ODE model, which enables us to understand evolvability of certain traits in

Cited by 0

Mining very large databases

Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan · Computer · 1999

Established companies have had decades to accumulate masses of data about their customers, suppliers, products and services, and employees. Data mining, also known as knowledge discovery in databases, gives organizations the tools to sift through these vast data stores to find the trends, patterns, and correlations that can guide strategic decision making. Traditionally, algorithms for data analysis assume that the input data contains relatively few records. Current databases however, are much too large to be held in main memory. To be efficient, the data mining techniques applied to very large databases must be highly scalable. An algorithm is said to be scalable if (given a fixed amount of main memory), its runtime increases linearly with the number of records in the input database. Recent work has focused on scaling data mining algorithms to very large data sets. The authors describe a broad range of algorithms that address three classical data mining problems: market basket analysis, clustering, and classification.

Cited by 155

Tautomerism in large databases

Markus Sitzmann, Wolf‐Dietrich Ihlenfeldt, Marc C. Nicklaus · Journal of Computer-Aided Molecular Design · 2010

We have used the Chemical Structure DataBase (CSDB) of the NCI CADD Group, an aggregated collection of over 150 small-molecule databases totaling 103.5 million structure records, to conduct tautomerism analyses on one of the largest currently existing sets of real (i.e. not computer-generated) compounds. This analysis was carried out using calculable chemical structure identifiers developed by the NCI CADD Group, based on hash codes available in the chemoinformatics toolkit CACTVS and a newly developed scoring scheme to define a canonical tautomer for any encountered structure. CACTVS's tautomerism definition, a set of 21 transform rules expressed in SMIRKS line notation, was used, which takes a comprehensive stance as to the possible types of tautomeric interconversion included. Tautomerism was found to be possible for more than 2/3 of the unique structures in the CSDB. A total of 680 million tautomers were calculated from, and including, the original structure records. Tautomerism overlap within the same individual database (i.e. at least one other entry was present that was really only a different tautomeric representation of the same compound) was found at an average rate of 0.3% of the original structure records, with values as high as nearly 2% for some of the databases in CSDB. Projected onto the set of unique structures (by FICuS identifier), this still occurred in about 1.5% of the cases. Tautomeric overlap across all constituent databases in CSDB was found for nearly 10% of the records in the collection.

Cited by 83Open access

Large databases in anaesthesiology

Richard P. Dutton · Current Opinion in Anaesthesiology · 2015

The Information Age is bringing new capabilities for large database research to the specialty of anaesthesiology, driven by the formation of registries capable of capturing a large fraction of all cases performed.

Cited by 14

Browsing Large Image Databases

Andrew Liebeskind, Christopher North, Shahram Or · 2007

Image browsers are a vital tool for the access and management of digital image databases in that they provide the user with a means to manually traverse and search images in the database. These image browsers must make a tradeoff between the size in which they display each image and the number of images which they display at once. Some present a single large high resolution image and allow the user to page through the database of images one at a time. Others use a technique called thumbnailing in which images are shrunken down to a smaller size, allowing several to be displayed on screen at once. This paper describes an experiment to discover the optimal thumbnailing strategy in terms of image size vs. image density. The results suggested that the ideal balance lies in the direction of presenting many small images at once. However, this strategy can be taken too far, causing performance to drop. Guidelines for image browser design are discussed, as well as the effectiveness of such an ...

Cited by 0