article · 2008

VERY LARGE DATABASES

n.n.

Cited by 0Abstract and details only

Abstract

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.

Cite this paper

APA 7

n.n. (2008). VERY LARGE DATABASES.

MLA 9

n.n. “VERY LARGE DATABASES.” 2008.

Chicago

n.n. 2008. “VERY LARGE DATABASES.”

Harvard

n.n. (2008) ‘VERY LARGE DATABASES’.

BibTeX

@article{nn2008very,
  title = {{VERY LARGE DATABASES}},
  author = {n.n.},
  year = {2008}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.