Introduction - Data Warehouse Systems: Design and Implementation - page 9

snapshot of the business data at a given moment in time. These data are
then used for refreshing the contents of the data warehouse. Historically,
this process has been considered acceptable, since in the early days of data
warehousing it was almost impossible to obtain real-time, continuous feeds
from production systems. Moreover, it was dicult to get consistent, reliable
results from query analysis if warehouse data were constantly changing.
However, nowadays the user requirements have changed: business intelligence
applications constantly need current and up-to-date information. In addition,
while in those early days only selected users accessed the data warehouse, in
today's web-based architectures the number of users has been constantly
growing. Moreover, modern data warehouses need to remain available 24/7,
without a time window when access could be denied. In summary, the need of
near real-time data warehousing is challenging ETL technology. To approach
real time, the time elapsed between a relevant application event and its
consequent action (called the data latency) needs to be minimized. Therefore,
to support real-time business intelligence, real-time data warehouses are
needed. We study these kinds of data warehouses also in Chap. 13 .
The above are not the only challenges for data warehousing and OLAP in
the years to come. There is also a need to keep up with new application
requirements. For example, the web is changing the way in which data
warehouses are being designed, used, and exploited. For some data analysis
tasks (like worldwide price evolution of some product), the data contained
in a conventional data warehouse may not suce. External data sources,
like the web, can provide useful multidimensional information, although
usually too volatile to be permanently stored. The semantic web aims at
representing web content in a machine-processable way. The basic layer of
the data representation for the semantic web recommended by the World
Wide Web Consortium (W3C) is the Resource Description Framework
(RDF), on top of which the Web Ontology Language (OWL) is based.
In a semantic web scenario, domain ontologies (defined in RDF or some
variant of OWL) define a common terminology for the concepts involved in a
particular domain. Semantic annotations are especially useful for describing
unstructured, semistructured, and textual data. Many applications attach
metadata and semantic annotations to the information they produce (e.g.,
in medical applications, medical imaging, and laboratory tests). Thus, large
repositories of semantically annotated data are currently available, opening
new opportunities for enhancing current decision-support systems. The data
warehousing technology must be prepared to handle semantic web data.
In this topic, we study semantic web and unstructured data warehouses in
Chap. 14 .
Finally, there are many interesting topics in the data warehouse domain
that are still under development. Among them, we can mention temporal
data warehouses, 3D/4D spatial data warehouses, text and multimedia data
warehouses, and graph data warehouses. Although all of them are strong
candidates to play a relevant role in the data warehousing field, due to space