Databases Reference
In-Depth Information
Contextual Dimensions.
Contextual dimensions are those that highly depend on the
context of the task at hand as well as on the subjective preferences of the data consumer.
There are three dimensions
completeness
,
amount-of-data
and
relevancy
that are part
of this group.
Definition 8 (Completeness).
Completeness refers to the degree to which all required
information is present in a particular dataset. In general, completeness is the extent to
which data is of su
cient depth, breadth and scope for the task at hand. In terms of
Linked Data, we classify completeness as follows:
-
Schema completeness, the degree to which the classes and properties of an ontology
are represented, thus can be called "ontology completeness",
-
Property completeness, measure of the missing values for a specific property,
-
Population completeness, the percentage of all real-world objects of a particular
type that are represented in the datasets and
-
Interlinking completeness, which has to be considered especially in Linked Data
and refers to the degree to which instances in the dataset are interlinked.
Metrics.
Completeness can be measured by detecting the number of classes, properties,
values and interlinks that are present in the dataset by comparing it to the original dataset
(or gold standard dataset). It should be noted that in this case, users should assume a
closed-world-assumption where a gold standard dataset is available and can be used to
compare against.
Example.
In the use case, the flight search engine should contain complete information
so that it includes all o
ers for flights (population completeness). For example, a user
residing in Germany wants to visit her friend in America. Since the user is a student, low
price is of high importance. But, looking for flights individually on the airlines websites
shows her flights with very expensive fares. However, using our flight search engine she
finds all o
ff
ers, even the less expensive ones and is also able to compare fares from dif-
ferent airlines and choose the most a
ff
ordable one. The more complete the information
for flights is, including cheap flights, the more visitors the site attracts. Moreover, su
ff
-
cient interlinks between the datasets will allow her to query the integrated dataset so as
to find an optimal route from the start to the end destination (interlinking completeness)
in cases when there is no direct flight.
Definition 9 (Amount-of-data).
Amount-of-data refers to the quantity and volume of
data that is appropriate for a particular task.
Metrics.
The amount-of-data can be measured in terms of bytes (most coarse-grained),
triples, instances, and
or links present in the dataset. This amount should represent an
appropriate volume of data for a particular task, that is, appropriate scope and level of
detail.
Example.
In the use case, the flight search engine acquires enough amount of data so
as to cover all, even small, airports. In addition, the data also covers alternative means
of transportation. This helps to provide the user with better travel plans, which includes
smaller cities (airports). For example, when a user wants to travel from Connecticut
to Santa Barbara, she might not find direct or indirect flights by searching individual
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