Online analytical processing

About: Online analytical processing is a research topic. Over the lifetime, 5042 publications have been published within this topic receiving 92175 citations. The topic is also known as: OLAP.

An overview of data warehousing and OLAP technology

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.

Automated Selection of Materialized Views and Indexes in SQL Databases

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.

HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots

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.

Building Data Mining Applications for CRM

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)!

The Current State of Business Intelligence

Abstract: Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.