scispace - formally typeset

Topic

Online analytical processing

About: Online analytical processing is a(n) research topic. Over the lifetime, 5042 publication(s) have been published within this topic receiving 92175 citation(s). The topic is also known as: OLAP.


Papers
More filters
Journal ArticleDOI
01 Mar 1997
TL;DR: An overview of data warehousing and OLAP technologies, with an emphasis on their new requirements, is provided, based on a tutorial presented at the VLDB Conference, 1996.
Abstract: Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.

2,770 citations

Proceedings Article
10 Sep 2000
TL;DR: This paper presents an end-to-end solution to the problem of selecting materialized views and indexes for SQL databases, and describes results of extensive experimental evaluation that demonstrate the effectiveness of the techniques.
Abstract: Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a non-trivial task. A judicious choice must be cost-driven and influenced by the workload experienced by the system. Although there has been work in materialized view selection in the context of multidimensional (OLAP) databases, no past work has looked at the problem of building an industry-strength tool for automated selection of materialized views and indexes for SQL workloads. In this paper, we present an end-to-end solution to the problem of selecting materialized views and indexes. We describe results of extensive experimental evaluation that demonstrate the effectiveness of our techniques. Our solution is implemented as part of a tuning wizard that ships with Microsoft SQL Server 2000.

670 citations

Book
22 Dec 1999
TL;DR: This one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework helps you understand the principles of data warehousing and data mining systems and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible.
Abstract: From the Publisher: How data mining delivers a powerful competitive advantage! Are you fully harnessing the power of information to support management and marketing decisions? You will,with this one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework. Authors Alex Berson,Stephen Smith,and Kurt Thearling help you understand the principles of data warehousing and data mining systems,and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible. Find out about Online Analytical Processing (OLAP) tools that quickly navigate within your collected data. Explore privacy and legal issues. . . evaluate current data mining application packages. . . and let real-world examples show you how data mining can impact — and improve — all of your key business processes. Start uncovering your best prospects and offering them the products they really want (not what you think they want)! How data mining delivers a powerful competitive advantage! Are you fully harnessing the power of information to support management and marketing decisions? You will,with this one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework. Authors Alex Berson,Stephen Smith,and Kurt Thearling help you understand the principles of data warehousing and data mining systems,and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible. Find out about Online Analytical Processing (OLAP) tools thatquickly navigate within your collected data. Explore privacy and legal issues. . . evaluate current data mining application packages. . . and let real-world examples show you how data mining can impact — and improve — all of your key business processes. Start uncovering your best prospects and offering them the products they really want (not what you think they want)!

623 citations

Proceedings Article
03 Sep 1996
Abstract: At the heart of all OLAP or multidimensional data analysis applications is the ability to simultaneously aggregate across many sets of dimensions. Computing multidimensional aggregates is a performance bottleneck for these applications. This paper presents fast algorithms for computing a collection of group bys. We focus on a special case of the aggregation problem - computation of the CUBE operator. The CUBE operator requires computing group-bys on all possible combinations of a list of attributes, and is equivalent to the union of a number of standard group-by operations. We show how the structure of CUBE computation can be viewed in terms of a hierarchy of group-by operations. Our algorithms extend sort-based and hashbased grouping methods with several .optimizations, like combining common operations across multiple groupbys, caching, and using pre-computed group-by8 for computing other groupbys. Empirical evaluation shows that the resulting algorithms give much better performance compared to straightforward meth

607 citations

Proceedings ArticleDOI
11 Apr 2011
TL;DR: This work presents an efficient hybrid system, called HyPer, that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data.
Abstract: The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load-data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer, that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the ACID properties of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.

597 citations

Network Information
Related Topics (5)
Web service

57.6K papers, 989K citations

82% related
Ontology (information science)

57K papers, 869.1K citations

80% related
Cluster analysis

146.5K papers, 2.9M citations

80% related
Web page

50.3K papers, 975.1K citations

79% related
Server

79.5K papers, 1.4M citations

79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20223
202175
2020144
2019161
2018195
2017203