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Business Activity Monitoring (BAM) with Complex Business Event Processing (BEP/CEP)Business Activity Monitoring (BAM) has been for a while. However, it has been limited to real-time dashboard applications. Basically, it displays visual real-time graphs and charts based on KPIs or Key Performance Indicators. It lacks Complex Event Processing (CEP) capabilities that businesses require. Dashboards are just a tiny segment of BAM. Current BAM cannot meet the business requirements. For example, dashboards show only snapshots of information. It does not indicate what transitions have happened. In addition, managers do not keep watching them all the time. Vital events can be missed. Obviously, better framework is BAM with CEP. Complex Business Activity Monitoring with Event Processing (CAMEP) combines the two as a single frame work. Rosella Analytic Platform is an integrated end-to-end developer environment which provides all the needed ingredients for CAMEP. It incorporates the followings;
BAM feeders collect information from operational databases, data warehouses, data marts, and from other sources, and feed it into RME-EP expert systems. RME-EP engine evaluates rules and invokes event handlers to generate business events. Rules are developed by business users and analysts. RME-EP – Expert Systems EngineThe core of CAMEP is the RME-EP engine. Rete engine is a de facto industry standard technique for rule-based expert systems. It is also known as expert system shells. The problem with conventional implementations of Rete engines is that they are based on pattern-matching algorithms which are not only un-intuitive but also difficult to learn, especially, by non-IT users such as business analysts, medical doctors, and domain experts. As a consequence, rules have to be extracted from domain experts and then translated by IT knowledge engineers. This process not only takes time to develop but also makes it difficult to extract domain knowledge. The worst part is that detection of errors can be extremely difficult since domain experts cannot verify what the written rules exactly mean. To alleviate this problem, RME-EP (Rule-based Model Evaluation - Rete) has been developed to use SQL-like language which can be learnt by business users and domain experts very easily and quickly. As a matter of facts, users already familiar with SQL can learn it within an hour! Non-IT professional users can write RME-EP expert system rules by themselves without IT professional help. RME-EP opens a new era for business users and domain experts. SQL-like language for Business Activity MonitoringThe popularity of SQL as the preferred database query language has been largely due to the natural intuitiveness of the language as well as the richness of logical expressions. SQL is very easy to learn. Expressions written in SQL is very easy to understand and hence has been widely used by many non-IT professional such as business analysts and users, as well as by IT professionals. RME-EP is based on SQL-99 syntax supporting most logical and arithmetic language features of the language. The following shows examples of RME-EP rules. These rules show how newly developing sales/market trends can be detected automatically and generate notification messages;
// SALES TREND MONITORING;
RULE 1: // if sales dropped over 5% with correlation coefficient 0.3 and over;
IF TIMESERIES(RR, LINEAR, 1, Month1, Month2, Month3, Month4)) >= 0.3
AND TIMESERIES(GROWTH_RATE, LINEAR, 1, Month1, Month2, Month3, Month4) < -0.05
THEN SET sales.trend AS 'declining' END;
RULE 2: // if sales increased over 15% with correlation coefficient 0.3 and over;
IF TIMESERIES(RR, LINEAR, 1, Month1, Month2, Month3, Month4)) >= 0.3
AND TIMESERIES(GROWTH_RATE, LINEAR, 1, Month1, Month2, Month3, Month4) > 0.15
THEN SET sales.trend AS 'increasing' END;
RULE 3: // alert if something detected;
IF sales.trend IS NOT NULL
THEN THROWEVENT('alert', Region, Channels, sales.trend) END;
The following rule shows how RME-EP rules can be used in medical diagnosis;
RULE 4: // GENETIC DIAGNOSIS;
IF patient.gene.k12h2 PLIKE '*-C-*(2,4)-C-*(3)-[LIVMFVWC]-*(8)-H-*(3,5)-H-*'
AND PREDICT("model1") USING(patient.age, patient.gender, ...) > 0.34
THEN SET patient.diagnosis AS 'has cancer gene' END;
For more, please read RME-EP Rule Specification Language. Event HandlingRME-EP engine evaluates rules and responds by generating events to event handlers. For example, when there is a violation of regulations, users may write rules that inform to relevant business managers. Event handlers are normally user provided functions. They may include;
These functions can be easily implemented using extensible APIs. Predictive ModelingThe core concept of RME-EP is Rule-based Model Evaluation (RME) of predictive models. RME-EP incorporates predictive models with many logical and mathematical expressions of SQL-99. By incorporating advanced predictive models into Rete engine, RME-EP provides a superb platform for advanced rule-based expert systems. RME-EP supports the following predictive models;
For more, please read Predictive Modeling Software. High Performance!RME-EP uses compile-time analysis to build efficient run-time rule activation and evaluation environment. Rules are activated very efficiently. Facts are also managed and fetched using fast binary search techniques. Note that with binary search techniques, fetching a fact out of 1 million facts takes 20 only comparative steps at the worst case. High performance is the theme of RME-EP! Applications of BEP BAMBEP BAM or CAMEP powered by RME-EP is ideally suited for many mission-critical real-time tasks. The robustness of the rule specification language combined with advanced predictive modeling provides a superb platform for RTE applications. Real-time applications need high processing ratios. Embedded deployment of applications will provide the needed speed, if SOAP/SOA based deployment cannot meet! Applications of RME-EP may include;
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