The Development of Ordered SQL Packages to Support Data Warehousing

2011 ◽

pp. 72-103

Author(s):

Wilfred Ng ◽

Mark Levene

Keyword(s):

Data Warehousing ◽

Effective Means ◽

Dynamic Environment ◽

Peer To Peer ◽

Relational Model ◽

Wide Range ◽

Peer Network ◽

Partial Orderings ◽

Data Content ◽

Peer To Peer Network

This chapter discusses how the capabilities of database languages are enhanced to manipulate user-defined data orderings within the framework of the Ordered Relational Model (the ORM), which incorporates partial orderings into data domains. The motivation for applying the ORM in data warehousing environment is that business queries in an enterprise usually involve order. We have already defined and implemented Ordered SQL (OSQL), which allows users to capture the underlying semantics of the ordering of the data for a given application. Herein we demonstrate that OSQL aided with a package discipline can be an effective means to manage the inter-related operations and the underlying data domains of a wide range of advanced applications that are vital in data warehousing, such as temporal, incomplete, and fuzzy information. We also discuss the employment of OSQL system with three packages of OSQL_TIME, OSQL_INCOMP, and OSQL_FUZZY over a Peer-to-Peer network. Using our suggested framework, the data content of a data warehouse can be better adapted in a dynamic environment.

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Humanitites Data Warehousing

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch141 ◽

2008 ◽

pp. 2364-2370

Author(s):

Janet Delve

Keyword(s):

Data Warehouse ◽

Relational Databases ◽

Data Warehousing ◽

Numerical Data ◽

Complex Nature ◽

Data Warehouses ◽

Textual Data ◽

Numeric Data ◽

First Time ◽

And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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Humanities Data Warehousing

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch153 ◽

2011 ◽

pp. 987-992

Author(s):

Janet Delve

Keyword(s):

Data Warehouse ◽

Relational Databases ◽

Data Warehousing ◽

Numerical Data ◽

Complex Nature ◽

Data Warehouses ◽

Textual Data ◽

Numeric Data ◽

First Time ◽

And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (in say Wal-Mart’s data warehouse (Westerman, 2000)) and astronomical data (for example SKICAT) in scientific research, with textual data providing a descriptive rather than a central analytic role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for ‘non-numeric’ data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model, and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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Web Technologies and Data Warehousing Synergies

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch545 ◽

2005 ◽

pp. 3065-3067

Author(s):

John M. Artz

Keyword(s):

Data Warehouse ◽

Web Site ◽

Relational Databases ◽

Data Warehousing ◽

Emerging Technology ◽

Web Technologies ◽

The Past ◽

Large Sets ◽

The Web

Data warehousing is an emerging technology that greatly extends the capabilities of relational databases specifically in the analysis of very large sets of time-oriented data. The emergence of data warehousing has been somewhat eclipsed over the past decade by the simultaneous emergence of Web technologies. However, Web technologies and data warehousing have some natural synergies that are not immediately obvious. First, Web technologies make data warehouse data more easily available to a much wider variety of users. Second, data warehouse technologies can be used to analyze traffic to a Web site in order to gain a much better understanding of the visitors to the Web site. It is this second synergy that is the focus of this article.

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Preservation of Data Warehouses

Innovations in XML Applications and Metadata Management ◽

10.4018/978-1-4666-2669-0.ch008 ◽

2013 ◽

pp. 136-159

Author(s):

Carlos Aldeias ◽

Gabriel David ◽

Cristina Ribeiro

Keyword(s):

Data Warehouse ◽

Relational Databases ◽

Relational Model ◽

Markup Language ◽

Data Warehouses ◽

Dimensional Model ◽

Extensible Markup ◽

Definition Of ◽

Long Term Preservation

Data warehouses are used in many application domains, and there is no established method for their preservation. A data warehouse can be implemented in multidimensional structures or in relational databases that represent the dimensional model concepts in the relational model. The focus of this work is on describing the dimensional model of a data warehouse and migrating it to an XML model, in order to achieve a long-term preservation format. This chapter presents the definition of the XML structure that extends the SIARD format used for the description and archive of relational databases, enriching it with a layer of metadata for the data warehouse components. Data Warehouse Extensible Markup Language (DWXML) is the XML language proposed to describe the data warehouse. An application that combines the SIARD format and the DWXML metadata layer supports the XML language and helps to acquire the relevant metadata for the warehouse and to build the archival format.

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Web Technologies and Data Warehousing Synergies

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch214 ◽

2008 ◽

pp. 3411-3415

Author(s):

John M. Artz

Keyword(s):

Data Warehouse ◽

Web Site ◽

Relational Databases ◽

Data Warehousing ◽

Emerging Technology ◽

Web Technologies ◽

The Past ◽

Large Sets ◽

The Web

Data warehousing is an emerging technology that greatly extends the capabilities of relational databases specifically in the analysis of very large sets of time-oriented data. The emergence of data warehousing has been somewhat eclipsed over the past decade by the simultaneous emergence of Web technologies. However, Web technologies and data warehousing have some natural synergies that are not immediately obvious. First, Web technologies make data warehouse data more easily available to a much wider variety of users. Second, data warehouse technologies can be used to analyze traffic to a Web site in order to gain a much better understanding of the visitors to the Web site. It is this second synergy that is the focus of this article.

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Humanitites Data Warehousing

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch108 ◽

2011 ◽

pp. 570-574

Author(s):

Janet Delve

Keyword(s):

Data Warehouse ◽

Relational Databases ◽

Data Warehousing ◽

Numerical Data ◽

Complex Nature ◽

Data Warehouses ◽

Textual Data ◽

Numeric Data ◽

First Time ◽

And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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From Web Log to Data Warehouse

Managing Internet and Intranet Technologies in Organizations ◽

10.4018/978-1-878289-95-7.ch012 ◽

2001 ◽

pp. 203-216

Author(s):

John M. Artz

Keyword(s):

Data Warehouse ◽

Web Site ◽

Relational Databases ◽

Data Warehousing ◽

Research Data ◽

Data Set ◽

Web Technologies ◽

Web Log ◽

Large Sets ◽

The Web

Data warehousing is an emerging technology that greatly extends the capabilities of relational databases specifically in the analysis of very large sets of time-oriented data. The emergence of data warehousing has been somewhat eclipsed by the simultaneous emergence of Web technologies. However, Web technologies and data warehousing have some natural synergies that are just now being recognized. First, Web technologies make data warehouse data more easily available to a much wider variety of users both internally and externally. Since the value of data is directly related to its availability for exploitation, Internets and intranets help increase the value of the data in the warehouse. Second, data warehouse technologies can be used to analyze traffic to a Web site in a wide variety of ways in order to make the Web site more effective. This chapter will focus on the latter of these synergies and show, through an evolving example, how a simple data set from the Web log can be enhanced, in a step-wise fashion, into a full-fledged market research data warehouse.

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The State versus the People

10.1093/oso/9780198840428.001.0001 ◽

2020 ◽

Author(s):

Matthew Rendle

Keyword(s):

French Revolution ◽

Effective Means ◽

State Control ◽

Scarce Resources ◽

Soviet State ◽

The People ◽

Wide Range ◽

Complex Picture ◽

The Military

This book provides the first detailed account of the role of revolutionary justice in the early Soviet state. Law has often been dismissed by historians as either unimportant after the October Revolution amid the violence and chaos of civil war or even, in the absence of written codes and independent judges, little more than another means of violence. This is particularly true of the most revolutionary aspect of the new justice system, revolutionary tribunals—courts inspired by the French Revolution and established to target counter-revolutionary enemies. This book paints a more complex picture. The Bolsheviks invested a great deal of effort and scarce resources into building an extensive system of tribunals that spread across the country, including into the military and the transport network. At their peak, hundreds of tribunals heard hundreds of thousands of cases every year. Not all ended in harsh sentences: some were dismissed through lack of evidence; others given a wide range of sentences; others still suspended sentences; and instances of early release and amnesty were common. This book, therefore, argues that law played a distinct and multifaceted role for the Bolsheviks. Tribunals stood at the intersection between law and violence, offering various advantages to the Bolsheviks, not least strengthening state control, providing a more effective means of educating the population on counter-revolution, and enabling a more flexible approach to the state’s enemies. All of this adds to our understanding of the early Soviet state and, ultimately, of how the Bolsheviks held on to power.

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