3,564 research outputs found
XML content warehousing: Improving sociological studies of mailing lists and web data
In this paper, we present the guidelines for an XML-based approach for the
sociological study of Web data such as the analysis of mailing lists or
databases available online. The use of an XML warehouse is a flexible solution
for storing and processing this kind of data. We propose an implemented
solution and show possible applications with our case study of profiles of
experts involved in W3C standard-setting activity. We illustrate the
sociological use of semi-structured databases by presenting our XML Schema for
mailing-list warehousing. An XML Schema allows many adjunctions or crossings of
data sources, without modifying existing data sets, while allowing possible
structural evolution. We also show that the existence of hidden data implies
increased complexity for traditional SQL users. XML content warehousing allows
altogether exhaustive warehousing and recursive queries through contents, with
far less dependence on the initial storage. We finally present the possibility
of exporting the data stored in the warehouse to commonly-used advanced
software devoted to sociological analysis
SODA: Generating SQL for Business Users
The purpose of data warehouses is to enable business analysts to make better
decisions. Over the years the technology has matured and data warehouses have
become extremely successful. As a consequence, more and more data has been
added to the data warehouses and their schemas have become increasingly
complex. These systems still work great in order to generate pre-canned
reports. However, with their current complexity, they tend to be a poor match
for non tech-savvy business analysts who need answers to ad-hoc queries that
were not anticipated. This paper describes the design, implementation, and
experience of the SODA system (Search over DAta Warehouse). SODA bridges the
gap between the business needs of analysts and the technical complexity of
current data warehouses. SODA enables a Google-like search experience for data
warehouses by taking keyword queries of business users and automatically
generating executable SQL. The key idea is to use a graph pattern matching
algorithm that uses the metadata model of the data warehouse. Our results with
real data from a global player in the financial services industry show that
SODA produces queries with high precision and recall, and makes it much easier
for business users to interactively explore highly-complex data warehouses.Comment: VLDB201
- âŠ