Different Kinds of Hierarchies in Multidimensional Models

Data Warehouses ◽

Detailed Data ◽

Analytical Processing ◽

Multidimensional Models ◽

Drill Down

In the database design, the advantages of using conceptual models for representing users’ requirements are well known. Nevertheless, even though data warehouses (DWs) are databases that store historical data for analytical purposes, they are usually represented at the logical level using the star and snowflake schemas. These schemas facilitate delivery of data for online analytical processing (OLAP) systems. In particular, hierarchies are important since traversing them, OLAP tools perform automatic aggregations of data using the roll-up and drill-down operations. The former operation transforms detailed data into aggregated ones (e.g., daily into monthly sales) while the latter does the opposite.

Spatial Data in Multidimensional Conceptual Models

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch007 ◽

2009 ◽

pp. 56-64

Author(s):

Elzbieta Malinowski

Keyword(s):

Spatial Data ◽

Conceptual Models ◽

Historical Data ◽

Data Warehouses ◽

Online Analysis ◽

Aggregated Data ◽

Star Schema ◽

Online Analysis Processing ◽

Numeric Data ◽

Drill Down

Data warehouses (DWs) are used for storing and analyzing high volumes of historical data. The structure of DWs is usually represented as a star schema consisting of fact and dimension tables. A fact table contains numeric data called measures (e.g., quantity). Dimensions are used for exploring measures from different analysis perspectives (e.g., according to products). They usually contain hierarchies required for online analysis processing (OLAP) systems in order to dynamically manipulate DW data. While traversing hierarchy, two operations can be executed: the roll-up operation, which transforms detailed measures into aggregated data (e.g., daily into monthly sales); and the drill-down operation, which does the opposite.

Schema Evolution in Multiversion Data Warehouses

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2021100101 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-28

Author(s):

Waqas Ahmed ◽

Esteban Zimányi ◽

Alejandro A. Vaisman ◽

Robert Wrembel

Keyword(s):

Efficient Solution ◽

Business Processes ◽

User Requirements ◽

Schema Evolution ◽

Data Warehouses ◽

Relational Schema ◽

External Sources ◽

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

ANALISIS DATA KEJADIAN BENCANA ANGIN PUTING BELIUNG DENGAN METODE ONLINE ANALYTICAL PROCESSING (OLAP)

SINTECH (Science and Information Technology) Journal ◽

10.31598/sintechjournal.v2i1.298 ◽

2019 ◽

Vol 2 (1) ◽

pp. 18-23 ◽

Cited By ~ 1

Author(s):

Ridho Darman

Keyword(s):

Decision Making ◽

Data Analysis ◽

Natural Disaster ◽

Historical Data ◽

Study Data ◽

Graphical Form ◽

High Incidence ◽

A whirlwind is a natural disaster with a relatively high incidence. In improving whirlwinddisaster mitigation preparedness, analysis of historical data of events is needed to minimize the possibility of losses. In this study, data analysis was carried out using the Online Analytical Processing (OLAP) method with the Zoho Reports application so that it can be known to the region prone to whirlwind and the time of occurrence to help those who have an importance in decision making. The results of the analysis are in the form of information displayed in graphical form from data on the occurrence of whirlwind in Indonesia in 2011-2014.

Storage Strategies in Data Warehouses

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch198 ◽

2011 ◽

pp. 1054-1058

Author(s):

Xinjian Lu

Keyword(s):

Data Warehouse ◽

Data Aggregation ◽

Historical Data ◽

Response Times ◽

Data Warehouses ◽

Individual Values ◽

Processing Data ◽

On Line ◽

Analytical Processing ◽

Require Data

A data warehouse stores and manages historical data for on-line analytical processing, rather than for on-line transactional processing. Data warehouses with sizes ranging from gigabytes to terabytes are common, and they are much larger than operational databases. Data warehouse users tend to be more interested in identifying business trends rather than individual values. Queries for identifying business trends are called analytical queries. These queries invariably require data aggregation, usually according to many different groupings. Analytical queries are thus much more complex than transactional ones. The complexity of analytical queries combined with the immense size of data can easily result in unacceptably long response times. Effective approaches to improving query performance are crucial to a proper physical design of data warehouses.

Online Analytical Processing and Data-Cube Technologies

Handbook of Research on Public Information Technology ◽

10.4018/978-1-59904-857-4.ch057 ◽

2008 ◽

pp. 627-635

Author(s):

Lixin Fu ◽

Wen-Chen Hu

Keyword(s):

Decision Support ◽

Large Data ◽

Data Cube ◽

Data Sets ◽

Data Warehouses ◽

Strategic Decisions ◽

Executive Information Systems ◽

Executive Management ◽

Since the late ’80s and early ’90s, database technologies have evolved to a new level of applications: online analytical processing (OLAP), where executive management can make quick and effective strategic decisions based on knowledge in terms of queries against large amounts of stored data. Some OLAP systems are also regarded as decision support systems (DSSs) or executive information systems (EIS). The traditional, well-established online transactional processing (OLTP) systems such as relational database management systems (RDBMS) mainly deal with mission-critical daily transactions. Typically, there are a large number of short, simple queries such as lookups, insertions, and deletions. The main focus is transaction throughput, consistency, concurrency, and failure recovery issues. OLAP systems, on the other hand, are mainly analytical and informational. OLAP systems are usually closely coupled with data warehouses, which can contain very large data sets that may include historical data as well as data integrated from different departments and geographical locations. So the sizes of data warehouses are usually significantly larger than common OLTP systems. In addition, the workloads of OLAP are quite different from those of traditional transaction systems: The queries are unpredictable and much more complicated. For example, an OLAP query could be, “For each type of car and each manufacturer, list market share change in terms of car sales between the first quarter of 2005 and the first quarter of 2006.” The purpose of these queries is not for the daily operational maintenance of data; instead, it is for deeper knowledge from data used for decision support.

A Temporal Multidimensional Model and OLAP Operators

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2020100107 ◽

2020 ◽

Vol 16 (4) ◽

pp. 112-143

Author(s):

Waqas Ahmed ◽

Esteban Zimányi ◽

Alejandro Ariel Vaisman ◽

Robert Wrembel

Keyword(s):

Decision Making ◽

Time Varying ◽

Data Warehouses ◽

Multidimensional Model ◽

Relational Schema ◽

Temporal Semantics ◽

History Of ◽

Temporal Model ◽

Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical online analytical processing (OLAP) operations with temporal semantics, which is formalized for the four classic operations, namely roll-up, dice, project, and drill-across. Finally, the mapping from the generalized temporal model into a relational schema is presented together with an implementation of the temporal OLAP operations in standard SQL.

Data Warehouses, Online Analytical Processing, and Metadata

Business Intelligence ◽

10.1016/b978-155860916-7/50007-5 ◽

2003 ◽

pp. 75-90

Author(s):

David Loshin

Keyword(s):

Data Warehouses ◽

Study and Performance Analysis of Different Techniques for Computing Data Cubes

Global Journal of Computer Science and Technology ◽

10.34257/gjcstcvol19is3pg33 ◽

2019 ◽

pp. 33-42

Author(s):

Aiasha Siddika

Keyword(s):

Decision Making ◽

Data Warehouse ◽

Historical Data ◽

Large Data ◽

Huge Amount ◽

Data Volume ◽

Analytical Processing ◽

And Performance ◽

Problem Data

Data is an integrated form of observable and recordable facts in operational or transactional systems in the data warehouse. Usually, data warehouse stores aggregated and historical data in multi-dimensional schemas. Data only have value to end-users when it is formulated and represented as information. And Information is a composed collection of facts for decision making. Cube computation is the most efficient way for answering this decision making queries and retrieve information from data. Online Analytical Process (OLAP) used in this purpose of the cube computation. There are two types of OLAP: Relational Online Analytical Processing (ROLAP) and Multidimensional Online Analytical Processing (MOLAP).This research worked on ROLAP and MOLAP and then compare both methods to find out the computation times by the data volume. Generally, a large data warehouse produces an extensive output, and it takes a larger space with a huge amount of empty data cells. To solve this problem, data compression is inevitable. Therefore, Compressed Row Storage (CRS) is applied to reduce empty cell overhead.