SEKE 2012 Proceedings - Knowledge Systems Institute

Match
production rules
Enterprise System
Knowledge
Base
Conector
Adapter
Engine
Data
Events
Facts
Persists the
accounting
Database
Id Rule Count Measure Timestamp
1 Cardiology 10 2012-06-31
cost age > 50
2 Cardiology 20 2012-07-01
cost age > 50
4 Other Rule 71 2012-07-02
5 Other Rule 12 2012-07-02
Table 2. Table that illustrates how our system
integrates all matched occurrences of the
LRS of a rule over time, using day as the
smaller unit of time. All events that match
a given rule are summed into the count measure.
Figure 5. Run-time Engine
by the production rules are stored in a relational database
through auxiliary functions provided by a simple API.
Dynamically, all these five components work together
as a consumer of data streams from an enterprise system.
When a new event arrives in the stream, it is splited in facts
to be confronted with the production rules from the knowledge
base, producing measures that will compose the OLAP
cube. This process is shown in Figure 6.
Event Stream
Production Rules
Yes
No
Discretized Time
Measures
Figure 6. Stream to business measures
In the next section we describe how this system was integrated
into a large governmental health care management
system.
5. Case Study
The proposed architecture is part of a real claim processing
system of a government health insurance service in
Brazil. This system is currently being used to smartly monitor
health service spending for some medical specialities,
for instance cardiological procedures on people older than
50 years old. With this kind of data, it is possible to efficiently
manage financial resources and create prevention
campaigns.
This goal is implemented by creating rules that filter only
the specific medical events, monitoring, accounting for and
storing the associated appointments. For instance, a possible
query to be executed in this scenario could be: ”How
much did cost the cardiology exams made in july for persons
above 50 years old?”. In our system, the whole conversion
into a cube, and the query over a traditional OLAP system
can be replaced by the simpler production rule shown
in Figure 7.
production rule "CARDIOLOGY exams' costs for USER with age > 50"
when
User(age > 50)
Speciality(name = "cardiology", isExam = true)
Claim($cost = totalCost, $date = date)
do
update_cube($cost, $date, $this)
Figure 7. Production rule that filters only the
events of interest and integrates them over
time.
As this rule is applied to an event stream, the business
measures are persisted into a database as shown in Table 2,
characterizing a cube with the well-defined time dimension
in column ”timestamp” and aditional dimensions (User and
Speciality) implicitly defined trough the rule’s LHS. With
this data, the query from example can be executed by filtering
only the desired date interval.
6. Conclusions
OLAP applications allow the execution of complex
queries over summarized data, also know as measures,
which aids decision-making for managers. Normally, the
creation and maintenance of a data warehouse separated
from normal system database is required, where the data
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